Frontiers of Statistics and Forecasting in Celebration of the 80Th Birthday of George C

Frontiers of Statistics and Forecasting in Celebration of the 80th Birthday of George C. Tiao

Humanities and Social Sciences Building Academia Sinica, Taipei, Taiwan

December 17~18, 2013

The Chinese Institute of Probability and Statistics Institute of Economics, Academia Sinica Institute of Statistical Science, Academia Sinica DGBAS, Executive Yuan, R.O.C.

Welcome

Dear Friends:

On behalf of the organizing committee, it is my great privilege and honor to welcome you to the conference “Frontiers of Statistics and Forecasting” to celebrate the 80th birthday of Professor George C. Tiao. A leading figure in statistics, George has had a great impact on many econometricians and statisticians around the world, especially in the Chinese community. It is particularly fitting to have the celebration hosted by the Academia Sinica and sponsored jointed by The Chinese Institute of Probability and Statistics, Institute of Economics, Institute of Statistical Science, and the Directorate General of Budget, Accounting and Statistics.

Many of you took a long journey, including from Sao Paulo, London, Madrid, Illinois, New Jersey, Washington DC, Beijing, Hong Kong, Shanghai, and Tokyo, to join the celebration. To you, we say “thank you”. In the next two days, you will hear many of George’s innovative contributions in statistics and economics. However, the conference cannot cover all of George’s contributions. He is instrumental in the developments of statistical education and research in Taiwan, Beijing, and Hong Kong, and in the establishment of the International Chinese Statistical Association. He is also the founding chair-editor of Statistica Sinica. He has co-established both the annual NBER/NSF Time Series Conference and the Conference for Making Statistics More Effective in Schools of Business. We have benefited greatly from George. Please join me in expressing our sincere thanks to George. Happy Birthday George!

Sincerely, Ruey S. Tsay, Chair of the Organizing Committee

Organizing Committee

Ruey S. Tsay Chairman, The University of Chicago Booth School of Business Chun-houh Chen Institute of Statistical Science, Academia Sinica, Taiwan Yi-Ting Chen Institute of Economics, Academia Sinica, Taiwan Ching-Shui Cheng Institute of Statistical Science, Academia Sinica, Taiwan Ray Yeutien Chou Institute of Economics, Academia Sinica, Taiwan Jing-Shiang Hwang -Institute of Statistical Science, Academia Sinica, Taiwan Kamhon Kan Institute of Economics, Academia Sinica, Taiwan Shin-Kun Peng Institute of Economics, Academia Sinica, Taiwan

Local Committee

Tzu-Pu Chang Chairman, Institute of Economics, Academia Sinica, Taiwan Ci-Ren Jiang Institute of Statistical Science, Academia Sinica, Taiwan Jen-Che Liao Institute of Economics, Academia Sinica, Taiwan Chang-Ching Lin Institute of Economics, Academia Sinica, Taiwan Yin-Jing Tien Institute of Statistical Science, Academia Sinica, Taiwan Tso-Jung Yen Institute of Statistical Science, Academia Sinica, Taiwan

How people look at George C. Tiao?

We thank The Institute of Mathematical Statistics for granting permission to post this article on our conference website and this program book.

Organizing Committee, Frontiers of Statistics and Forecasting About George

GEORGE C. TIAO

EDUCATION: 1955 B.A. in Economics, National Taiwan University 1958 M.B.A. in Banking and Finance, New York University 1962 Ph.D. in Economics, University of Wisconsin (Title of thesis: Bayesian Analysis of Statistical Assumptions) HONORS: Awarded the Ford Foundation Dissertation Fellowship (1961-62); Elected Fellow of the American Statistical Association (1973); Elected Fellow of the Institute of Mathematical Statistics (1974); Elected Member of Academia Sinica (1976); Elected Member of International Statistical Institute (1980); Honorary Professor, Fudan University, Shanghai (1989); Honorary Professor, Yunnan University, Kunming (1992); Distinguished Service Medal, by the Directorate-General of Budget, Accounting and Statistics, Taipei (1993); Julius Shiskin Award, by the Washington Statistical Society, the National Association for Business Economics, and the American Statistical Association (2001); Samuel S. Wilks Memorial Medal Award, by the American Statistical Association (2001); Honorary Professor, Peking University, Beijing (2002); Doctor Honoris Causa, Universidad Carlos III De Madrid (2003); Honorary Doctorate, National Tsing Hua University (2003); Statistician of the Year Award, by the Chicago Chapter of the American Statistical Association (2005); 2005 Stratospheric Ozone Protection Award, member of a research “Tiger” Team given the award by the EPA (2005).

PROFESSIONAL EXPERIENCE: 1962-65 Assistant Professor of Statistics and Commerce, University of Wisconsin, Madison 1965-66 Visiting Associate Professor, Harvard Business School 1966-68 Associate Professor of Statistics and Business, University of Wisconsin, Madison 1968-81 Professor of Statistics and Business, University of Wisconsin, Madison 1970-71 Visiting Professor, University of Essex, Colchester, England 1973-75 Chairman, Department of Statistics, University of Wisconsin, Madison 1975-76 Visiting Chair Professor, National Taiwan University, Taipei 1980-81 Visiting Ford Foundation Professor of Statistics, University of Chicago 1981-82 Bascom Professor of Statistics and Business, University of Wisconsin, Madison 1982-97 W. Allen Wallis Professor of Statistics, Graduate School of Business, University of Chicago 1987-94 Research Fellow, Institute of Statistical Science, Academia Sinica, Taipei 1996 Visiting Chair Professor, National Tsing Hua University, Hsinchu 1997-03 W. Allen Wallis Professor of Econometrics and Statistics, Graduate School of Business, University of Chicago 2001 Distinguished Visiting Professor, Guanghua School of Management, Peking University, Beijing 2002- Distinguished Research Fellow, Xian Statistical Research Institute, Xian 2003- W. Allen Wallis Professor of Econometrics and Statistics (emeritus), Graduate School of Business, University of Chicago

PROFESSIONAL SERVICE: Associate Editor, Journal of the American Statistical Association (1976-81); Member, Review Board, American Statistical Association/Bureau of the Census Research Project (1978-87); Chairman, Business and Economics Section, American Statistical Association (1980-81); Member, Ad Hoc Emergency Committee, Institute of Mathematical Statistics (1979-81); Senior Advisor, Bureau of Statistics, Taipei (1976-); Member, Technical Council, Air Pollution Control Association (1976-81); Member of Council, Institute of Mathematical Statistics (1979-83); Member, U.S. Census Advisory Committee (1979-85); Associate Editor, Journal of Time Series Analysis (1980-88); Associate Editor, Journal of Business&Economic Statistics (1981-88); Member, NSF Panel for the Mathematical Sciences Postdoctoral Research Fellowships (1983-90; Chair, 1986 and 1990); Member, Board of Academic Advisors, Institute of Statistical Science, Academia Sinica, Taipei (1982-; Chair, 1994-96); Member, Finance Committee, Institute of Mathematical Statistics (1984-87); Member, Committee on Applied and Theoretical Statistics, National Research Council (1985-88); Nominating Committee, American Statistical Association (1988-89); Member, International Committee, American Statistical Association (1990-93); Founding President, International Chinese Statistical Association (1987-88); Founding Chair Editor, Statistica Sinica (1988-93); Member, Review Board, Chiang Ching-Kuo Foundation for International Exchange (1989-91; 1993-98; 2000-2002); Member, Board of Academic Advisors, Institute of Economics, Academia Sinica, Taipei (1991-); Advisor, Chung Hua Institution of Economic Research, Taipei (1985-); Advisor, National Statistical Bureau, Beijing (1992-); Member of Council, Academia Sinica , Taipei (1993-); Chair, Search Committee for Editor of Journal of Business and Economic Statistics, American Statistical Association (1994); Advisor, National Tsing-Hua University, Hsinchu (1994-97); Member, Advisory Committee, Division of Biostatistics and Bioinformatics, National Health Research Institute, Taipei (1996-; Chair 1996-2001); Member, Wilks Memorial Medal Committee, American Statistical Association (1997-99); Principal Advisor, Quantitative Finance Program, National Tsing Hua University, Hsinchu (1997-); President, Chinese Economic Association in North America (1999); Member, Advisory Board, School of Technology Management, National Tsing Hua University, Hsinchu (1999-); Member, Advisory Board, Econometric Theory (1999-); Member, Advisory Board, University Centers for Excellence Program, Ministry of Education, Taipei (1999-); Chair, Advisory Committee, Department of Business Statistics and Econometrics, Guanghua School of Management, Peking University, Beijing (2001-); Chair, International Relations Committee, American Statistical Association (2003-).

MEMBERSHIPS: Royal Statistical Society; Institute of Mathematical Statistics; American Statistical Association; Chinese Statistical Association, Taipei; International Statistical Institute; International Chinese Statistical Association. Books

A Course in Time Series Box on Quality and Discovery, Bayesian Inference in Statistical

Analysis with Design, Control, and Analysis

Robustness

Directions in Time Series Bayesian Inference The Collected Works of G.E.P. Box, Vol. I and II Schedule

Dec. 16 (Mon.) Dec. 17 (Tue.) Dec. 18 (Wed.) 8:00 Registration

8:50 Opening Remarks

9:20 Plenary 1 Plenary 3

10:10 Plenary 2 Plenary 4

11:00 Photo Session / Break Break 11:20 Session 1 Session 4

12:30 Lunch Lunch and Poster Session 13:30 14:00 Session 2

Session 5 15:15 Break 15:30 15:45 Break 16:00 Session 3 Session 6

17:00 Registration 17:10 (Activity Center) Closing Remarks 17:15 17:25

18:00

19:00 Reception Banquet Dinner-(Invited Speakers) 20:30 ( Café Academia)

Program

December 17 (Tuesday), 2013 Time Humanities and Social Sciences Building International Conference Hall

08:00~08:50 Registration

Opening Remarks 08:50~09:20 Chair: Ching-Shui Cheng, Kamhon Kan, and Ruey S. Tsay Speaker: Vice President Fan-Sen Wang, Academia Sinica Plenary Session 1 Chair: Wen-Jang Huang, National University of Kaohsiung 09:20~10:10 Title: An Appreciation of Professor George C. Tiao from Across the Ocean Speaker: Howell Tong, London School of Economics Plenary Session 2 Chair: Chung-Shu Wu, Academia Sinica 10:10~11:00 Title: ARIMA Model Based Seasonal Adjustment at Work: Automatic Large-scale Performance Speaker: Agustin Maravall, Bank of Spain 11:00~11:20 Photo Session / Break HSS HSS Time 1st Conference Room 2nd Conference Room Session 1A Session 1B Seasonal Adjustment and Functional Credit Risk Time Series Chair: Shih-Ti Yu, Chair: Jin-Lung Henry Lin, National Tsing-Hua University National Dong Hua University Measuring Credit Risk of Individual Comparing ARIMA Model-Based and Corporate Bonds and Deriving Term Census X-11 Seasonal Adjustment 11:20~12:30 Structures of Default Probabilities William Bell, U.S. Census Bureau Takeaki Kariya, Kyoto University The Forecast of the Systemic Credit Risk of Taiwan’s Banking System and Convolution Autoregressive Models for Its Use in Implementing Basel III Functional Time Series Capital Requirement Rong Chen, Rutgers University Ching-Fan Chung, National Tsing Hua University

12:30~13:30 Lunch Break (Level 4, HSS) Session 2A Session 2B Factor Models Biostatistics and Inference Chair: Rouh-Jane Chou, Chair: Chen-Hsin Chen, National Tsing Hua University Academia Sinica Characterization of hESC Dynamic Principal Components in Transcriptome by Hybrid Sequencing Time Domain Wing Hung Wong, Daniel Peña, Carlo de lll, Spain Stanford University 13:30~15:15 Monitoring Structural Stability of Revitalizing Clinical Trial Methodology Dynamic Factor Models and Translational-Statistics Wen-Jen Tsay, Academia Sinica Lee-Jen Wei, Harvard University

Coverage-based Rarefaction and Recent Developments in Extrapolation: High-Dimensional Time Series Standardizing Samples by Analysis Completeness Rather Than Size Ruey S. Tsay, University of Chicago Anne Chao, National Tsing-Hua University 15:15~15:30 Break Session 3A Session 3B China Study Time Series and Dependent Data Chair: Ray-Yeutien Chou, Chair: Mong-Na Lo Huang, Academia Sinica National Sun Yat-sen University Multivariate Stochastic Regression: Prospect of China’s Economic Growth Sparsity, Singular Value Decomposition and Reform and Time Series Applications Weiying Zhang, Peiking University Tze Leung Lai, Stanford University 15:30~17:15 Preliminary Analysis on Mainland Estimating Social Inter-correlation with China’s Inflation Data Samples Network Data Songxi Chen, Peiking University and Hansheng Wang, Peiking University Iowa State University Analysis of Chinese Inter-industry On Recent Developments of Nonstationary and Long-Memory Time Spillover Effect of R&D Series Pinfang Zhu, Ngai-Hang Chan, Shanghai Academy of Social Sciences Chinese University of Hong Kong 17:30 Shuttle buses leave the Humanities and Social Sciences Building Banquet Statistica Sinica Dinner Speech: 18:00~20:30 Chair: Ruey S. Tsay Speakers: Kung-Yee Liang, National Yang-Ming University Jeff Wu, Georgia Institute of Technology December 18 (Wednesday), 2013 TimeH Humanities and Social Sciences Building International Conference Hall Plenary Session 3 Chair: Jeff Wu, Georgia Institute of Technology

09:20~10:10 Title: Being an Informed Bayesian: Assessing Prior Informativeness and Prior-Likelihood Conflict Speaker: Xiao-Li Meng, Harvard University Plenary Session 4 Chair: Norden E. Huang, National Central University 10:10~11:00 Title: Beyond Statistics: The Legacy of George Tiao on Environmental Science Speaker: Donald J. Wuebbles, University of lllinois, Urbana-Champaign 11:00~11:20 Break HSS HSS Time 1st Conference Room 2nd Conference Room Session 4A Session 4B Environment Finance Chair: Jane-Ling Wang, Chair: Sheng-Cheng Hu, University of California, Davis Academia Sinica Local-Momentum Autoregression for George Tiao and the Issue of Ozone Modeling Interest Rate and Term Change 11:20~12:30 Structure Alvin (Jim) Miller, Climate Prediction Jin-Chuan Duan, National University Center/NCEP/NWS/NOAA of Singapore Association of Cardiovascular On Buffered GARCH Processes Responses with Source-Apportioned Wai Keung Li, Fine Particle Air Pollutions in Beijing Hong Kong University Jing-Shiang Hwang, Academia Sinica Lunch Break / 12:30~14:00 Poster Session (Level 4, HSS) Chair: Ci-Ren Jiang, Academia Sinica Session 5A Session 5B Bayesian Inference Parsimony and Model Selection Chair: Dennis K.J. Lin, Chair: Hung Chen, The Pennsylvania State University National Taiwan University

Aspects of Dimension Reduction and

Doubly Constrained Factor Models with Variable Selection from Forward, Applications Backward, and Parsimonious

Henghsiu Tsai, Academia Sinica Modeling Perspectives

Ker-Chau Li, Academia Sinica

14:00~15:45 Bayesian Inference on Smoothed Lexis Parsimony Inducing Priors for Large Diagrams with Applications to Lung and Scale State-Space Models Breast Cancer Trends Hedibert Freitas Lopes, Chao Agnes Hsiung, George Washington University National Health Research Institutes Combination of Forecasts and Bayesian Prediction Bounds for Operating Room Durations, Even for Procedures with A Misspecification-Resistant Few or No Historical Data Information Criterion Johannes Ledolter, University of Iowa Ching-Kang Ing, Academia Sinica and Vienna University of Economics and Business 15:45~16:00 Break Session 6A Session 6B Dependent Data Finance Chair: Yi-Ching Yao, Academia Sinica Chair: Shin-Kun Peng, Academia Sinica Risk Measures Based on First Four Estimation of Extreme Quantiles for Moments and Resulting Trading 16:00~17:10 Functions of Dependent Random Strategies Variables Chung-Ming Kuan, Qiwei Yao, London School of Economics National Taiwan University Forecast of Portfolio Returns and Time Evolution of Income Distribution Trading Strategies Yi-Ting Chen, Academia Sinica Hwai-Chung Ho, Academia Sinica Closing Remark 17:10~17:25 Chair: Ching-Shui Cheng, Kamhon Kan, and Ruey S. Tsay Speaker: George C. Tiao

17:40 Shuttle buses leave the Humanities and Social Sciences Building

18:00~20:30 Dinner (Invited Speakers)

Access

From Taiwan Taoyuuan International Airport to Academia Sinica 1. By Taxi(~NT$1500) 2. By Bus (~NT$150) + → Take bus to Taipei Station. Taxi (~NT$350) #1: → Take taxi to Academia Sinica. 3. By Bus (~NT$115) + → Take bus (Kuo-Kuang Motor Transportation: Route 1843) Taxi (~NT$100) #2: to Taipei World Trade Center Nangang Exhibition Hall. → Take taxi to Academia Sinica. 4. By Bus (~NT$150)+ → Take bus to Taipei Station. MRT (~NT$25) + → Take MRT to Nangang Station, No.1 exit. Bus (~NT$15) #1: → Take bus 212-straight/270/270-shuttle/BL25 to Academia Sinica. 5. By Bus (~NT$150) + → Take bus to Taipei Station. MRT (~NT$25) + → Take MRT to Taipei Nangang Exhibition Center Station, No.2 exit. Bus (~NT$15) #2: → Take bus 212/205/645(vice-line)/645/620/306 to Academia Sinica. 6. By Bus (~NT$150) + → Take bus to Taipei Station Bus (~NT$30) → Take bus 212-straight/270/205 to Academia Sinica.

From Taipei Songshan Airport to Academia Sinica 1. By Taxi (~NT$ 350) 2. By MRT (~NT$ 35) + → Take MRT to Taipei Nangang Exhibition Center Station, No.2 exit. Bus (~NT$ 15) → Take bus 212/205/645(vice-line)/645/620/306 to Academia Sinica. 3. By Bus (~NT$ 30) → Take bus 205/306 to Academia Sinica Stop. Metro Network

Map of Academia Sinica

15.Institute of Statistical Science 20.Activity Center (Western restaurants) 24.Humanities and Social Sciences Building(HSSB) 39.Institute of Economics Map of Academia Sinica

Map of Activity Center

Map of Humanities and Social Sciences Building

Reception and Banquet ■Reception - Café Academia (http://sinica.howard-hotels.com/RT_Sinica2.php) Time: 12/16/2013 18:00~20:30 Location: Academia Sinica

■Banquet - The Butterfly Dining (http://www.butterflydining.com.tw/bin/home.php) Time: 12/17/2013 18:00~20:30 Buses leave Humanity and Social Sciences Center at 17:30 Location: No 297-1 Zhongxiao East Road, Section 5, Taipei, Taiwan

■Dinner (Invited Speakers) - Shin Yeh: Shin Kong Mitsukoshi Xinyi Branch (Website: http://www.shinyeh.com.tw/English_web/store.php#Branch_5) Time: 12/18/2013 18:00~20:30 Buses leave Humanity and Social Sciences Building at 17:40 Location: 8F, No. 9, Songshou Rd., Xinyi Dist., Taipei, Taiwan (A9 of Shin Kong Mitsukoshi Xinyi Place Branch, 8F

Schedule and Abstract

December 17, 2013 Daily Schedule

December 17 (Tuesday):

Opening Remarks [08:50 – 09:20]: Location: HSS International Conference Hall Speaker: Vice President Fan-Sen Wang, Academia Sinica Chair: Ching-Shui Cheng, Kamhomn Kan, and Ruey S. Tsay

Plenary Session 1 [09:20 – 10:10]: Location: HSS International Conference Hall Title: An appreciation of Professor George C. Tiao from Across the Ocean Speaker: Howell Tong, London School of Economics Chair: Wen-Jang Huang, National University of Kaohsiung

Plenary Session 2 [10:10 – 11:00]: Location: HSS International Conference Hall Title: ARIMA Model Based Seasonal Adjustment at Work: Automatic Large-scale Performance Speaker: Agustin Maravall, Bank of Spain Chair: Chung-Shu Wu, Academia Sinica

Photo Session / Break [11:00 – 11:20]:

Parallel Sessions [11:20 – 12:30]: Session 1A (Seasonal adjustment and Functional time series) Location: HSS 1st Conference Room Chair: Jin-Lung Henry Lin, National Dong Hwa University 11:20 a.m. Comparing ARIMA Model-Based and Census X-11 Seasonal Adjustment William Bell, U.S. Census Bureau 11:55 a.m. Convolution Autoregressive Models for Functional Time Series Rong Chen, Rutgers University

Session 1B (Credit risk) Location: HSS 2nd Conference Room Chair: Shih-Ti Yu, National Tsing-Hua University 11:20 a.m. Measuring Credit Risk of Individual Corporate Bonds and Deriving Term Structures of Default Probabilities Takeaki Kariya, Kyoto University 11:55 a.m. The Forecast of the Systemic Credit Risk of Taiwan’s Banking System and Its Use in Implementing Basel III Capital Requirement Ching-Fan Chung, National Tsing Hua University

Lunch break [12:30 – 13:30]:

Parallel Sessions [13:30 – 15:15]: Session 2A (Factor models) Location: HSS 1st Conference Room Chair: Rouh-Jane Chou, National Tsing Hua University 1:30 p.m. Dynamic Principal Components in Time Domain Daniel Peña, Carlo de III, Spain 2:05 p.m. Monitoring Structural Stability of Dynamic Factor Models Wen-Jen Tsay, Academia Sinica 2:40 p.m. Recent Developments in High-Dimensional Time Series Ruey S. Tsay, University of Chicago

Session 2B (Biostatistics and inference) Location: HSS 2nd Conference Room Chair: Chen-Hsin Chen, Academia Sinica 1:30 p.m. Characterization of hESC Transcriptome by Hybrid Sequencing Wing Hung Wong, Stanford University 2:05 p.m. Revitalizing Clinical Trial Methodology and Translational Statistics Lee-Jen Wei, Harvard University 2:40 p.m. Coverage-based Rarefaction and Extrapolation: Standardizing Samples by Completeness Rather Than Size Anne Chao, National Tsing-Hua University

Break [15:15 – 15:30]:

Parallel Sessions [15:30 – 17:15]: Session 3A (China study) Location: HSS 1st Conference Room Chair: Ray Yeutien Chou, Academia Sinica 3:30 p.m. Prospect of China’s Economic Growth and Reform Weiying Zhang, Peiking University 4:05 p.m. Preliminary Analysis on Mainland China’s Inflation Data Songxi Chen, Peiking University and Iowa State University 4:40 p.m. Analysis of Chinese Inter-industry Spillover Effect of R&D Pinfang Zhu, Shanghai Academy of Social Sciences

Session 3B (Time series and dependent data) Location: HSS 2nd Conference Room Chair: Mong-Na Lo Huang, National Sun Yat-sen University 3:30 p.m. Multivariate Stochastic Regression: Sparsity, Singular Value Decomposition and Time Series Applications Tze Leung Lai, Stanford University 4:05 p.m. Estimating Social Inter-correlation with Samples Network Data Hansheng Wang, Peiking University 4:40 p.m. On Recent Developments of Nonstationary and Long-Memory Time Series Ngai-Hang Chan, Chinese University of Hong Kong

Banquet [18:00 – 20:30 ]- The Butterfly Dining: Location: No 297-1 Zhongxiao East Road, Section 5, Taipei, Taiwan Buses leave Humanity and Social Sciences Center at 17:30 Statistica Sinica Dinner Speech: Chair: Ruey S. Tsay Speakers: Kung-Yee Liang, National Yang-Ming University Jeff Wu, Georgia Institute of Technology

Plenary Session 1

Chair: Wen-Jang Huang National University of Kaohsiung

09:20~10:10 December 17, 2013 An Appreciation of Professor George C. Tiao from Across the Ocean

Howell Tong London School of Economics and Political Science

Abstract

Professor George Tiao is an eminent statistician, who has spent almost all his professional career in the USA and has lasting connections with universities in Taiwan, Hong Kong and Beijing. As a statistician from the other side of the Atlantic Ocean, I shall highlight some of his major achievements from the perspective of a nonlinear time series analyst, emphasizing our intellectual interaction.

Plenary Session 2

Chair: Chung-Shu Wu Academia Sinica

10:10~11:00 December 17, 2013 Arima Model Based Seasonal Adjustment at Work: Automatic Large-scale Performance

Agustin Maravall Bank of Spain

Abstract

Seasonality, i.e., the seasonal component of a time series, is never directly observed, nor does it have a generally accepted and precise definition. These limitations obscure proper treatment and analysis. Because, as Hawking and Mlodinow state ”there can be no model-independent test of reality,” following the work of George Tiao and his students at the University of Wisconsin, in the early 80’s, a seasonal adjustment based on minimum mean squared error estimation of unobserved components in linear stochastic time series models (ARIMA) was proposed (Hillmer and Tiao, 1982). This ARIMA-model- based (AMB) approach to seasonal adjustment seemed interesting because it would provide the analyst with a precise definition of seasonality by means of a model consistent with the model identified for the observed series. The approach would further permit model-derived diagnostics and parametric inference. Application of the approach in production (i.e., when many series are to be treated) was discarded because it seemed to imply heavy computational and time series analyst resources. Besides, many series need some preadjustment before they can be assumed the output of a linear stochastic process: for example, calendar effects may need removal, and the series may be con- taminated by outliers. Because not all users need to be time series modeling experts, and because even if they are- the number of series that need treatment may be too big, an automatic model identification (AMI) procedure is needed. The procedure should address both preadjustment of the series and identification of the ARIMA model.

In the 90’s, Gómez and Maravall presented a first version of two linked programs that enforced the AMB approach and contained an AMI option. The first program, TRAMO (”Time series Regression with ARIMA Noise, Miss- ing Observations and Outliers”) performed preadjustment mostly by means of regression- and ARIMA model identification. The complete model is referred to as the ”regression (reg)-ARIMA” model. The second program, SEATS (”Signal Extraction in ARIMA Time Series”) decomposed the series into unobserved components and, in particular, performed seasonal adjustment.

The two programs proved eﬃcient and reliable, and TRAMO and SEATS have been recommended often, together with X12-ARIMA, the U.S. Bureau of the Census program by many task forces (recent examples are European Statistical System, 2009, and United Nations, 2011). They are part of the new USBC X13-ARIMA-SEATS program, and of the Eurostat-National Bank of Belgium program DEMETRA+.

The presentation centers on the empirical performance of TRAMO-SEATS in automatic large-scale applications. Failure of the AMI procedure can be due to two reasons: First, for a series that follows a reg-ARIMA model, the procedure may fail to identify the proper one. Second, the procedure may also fail because the reg-ARIMA model is inadequate for modeling the series. Two large-scale applications are considered: one looks at the results for a set of 50000 simulated monthly series, generated with 50 diﬀerent ARIMA models. The other looks at the results on a set of 16000 real monthly series, so that the departure from pure reg-ARIMA models implied by reality can in turn be assessed.

The simulation exercise shows an excellent performance of the default automatic procedure. As for the real series exercise, for monthly series with lengths not exceeding 30 years the procedure is found remarkably reliable. (When a careful manual identiﬁcation is intended, it can certainly provide good bench- mark or starting point.)

Session 1A

Seasonal Adjustment and Functional Time Series

Chair: Jin-Lung Henry Lin National Dong Hwa University

11:20~12:30 December 17, 2013 Comparing ARIMA Model-Based and Census X-11 Seasonal Adjustment

William R. Bell

U.S. Census Bureau

Abstract

Methods of seasonal adjustment based on moving-average filters were developed starting in the 1920s, and more or less culminated in the Census X-11 method in 1965. In 1976, Cleveland and Tiao showed that seasonal adjustment filters obtained from time series component models could approximate those of X-11. This soon led to development of the canonical ARIMA model- based approach to seasonal adjustment (Hillmer and Tiao 1982), and later to additional research comparing X-11 and model-based seasonal adjustment filters. We will review these developments, including some recent work that provides further insights into differences between ARIMA model-based and X-11 seasonal adjustment. Convolutional Autoregressive Models for Functional Time Series

Rong Chen Rutgers University

Abstract

Due to the advances of technology and data collection capability, there are more and more applications involving functional data observed over time. We develop a convolutional AR model to model the dynamics of functional time series. Estimation methods and their asymptotic properties are studied. Simulated and real data examples are presented.

Session 1B

Credit Risk

Chair: Shih-Ti Yu National Tsing-Hua University

11:20~12:30 December 17, 2013 Measuring Credit Risk of Individual Corporate Bonds and Deriving Term Structures of Default Probabilities

Takeaki Kariya Yoshiro Yamamura Koji Inui Meiji University Meiji University Meiji University

Zhu Wang ZW System

Abstract

No doubt, the importance of empirical credit risk analysis has ever been increasing not only in financial industries but also in business and even in government under increasing world-wide uncertainties. In this paper, basing our arguments on the model of pricing government bonds (GBs) in Kariya et,al (2012) and using corporate bond (CB) prices as our data source on credit, we first propose a measure of credit risk price spread (CRPS) for each CB relative to a GB-equivalent CB price. To choose an empirically effective CRPS measure, we test a hypothesis of no attribute preference with respect to investors behaviors forming prices in the market of GBs. The results strongly reject the hypothesis against maturity preference as well as coupon preference. Sec- ondly using the CRPS measure, a specific agency credit rating is shown to be ineffective for making credit-homogeneous groups of corporate bonds, where industry category is also used. To get our credit-homogeneous grouping, the CRPS measure is standardized by adjusting the differences of maturities and a three-stage cluster analysis is applied to the observed standardized CRPSs for Japanese CBs to get 14 groups, where 1545 CB prices as of 2010.8 are included. Since the grouping by the cluster analysis is a posterior grouping which is based on stochastically realized CBs and GBs, we propose Fixed In- terval Rating (FIR) Method based on the standardized CRPS, and form 10 credit homogeneous groups. Thirdly, we derive the term structures of default probabilities (TSDPs) for some cluster groups and FIR groups and some individual firms via Kariya (2012) model, where industry factor is also considered. Naturally the TSDPs reflect the investors future perspective on defaults of individual firms or groups. The Forecast of the Systemic Credit Risk of Taiwan’s Banking System and Its Use in Implementing Basel III Capital Requirements

Ching-Fan Chung National Tsing Hua University

Abstract

In this paper we develop an empirical model for a banking system that con- sists of regression equations for industries’ and consumers’ loan default rate as well as a vector autoregressive model for macroeconomic variables that are shown to exert signiﬁcant inﬂuences over loan defaults. Based on the estimated results of such a system, we are able to simulate the credit loss distribution for the entire banking system from individual loans and calculate the Value at Risk (VaR) as a measure of the systemic credit risk. This framework is then applied to Taiwanese banking credit data that include over a million of individual credit exposure, credit rating, as well as loss given default samples. Both estimation and simulation require a huge amount of computation time. The result from our study can be used as an empirical foundation for implementing Basel III capital requirements for systemic credit risks.

Session 2A

Factor Models

Chair: Rouh-Jane Chou National Tsing Hua University

13:30~15:15 December 17, 2013 Dynamic Principal Components in the Time Domain

Daniel Pe˜na Victor Yohai

Universidad Carlos III de Madrid Universidad de Buenos Aires

Abstract

We propose a time domain approach to deﬁne dynamic principal components (DPC) using a reconstruction of the original series criterion. This approach to deﬁne DPC was introduced by Brillinger, who gave a very ele- gant theoretical solution in the stationary case using the cross spectrum. Our procedure can be applied under more general conditionss including the case of non stationary series and relatively short series. We also present a robust version of our procedure that allows to estimate the DPC when the series have outlier contamination. Our non robust and robust procedures are illustrated with real datasets. Monitoring Structural Stability of Dynamic Factor Models

Yu-Chin Cheny Wen-Jen Tsay Academia Sinica Academia Sinica

Abstract

This paper aims to test the out-of-sample structural changes of the factor augmented regression (FAR) model as compared to the in-sample structural stability tests of factor model considered in Breitung and Eickmeier (2011), and Chen, Dolado, and Gonzalo (2011). We extends the ﬂuctuation monitoring test of Chu, Stinchcombe, and White (1996) by proposing a rescaled monitoring test to deal with the scenario where there is a large number of cross-section series, N, each with T observations, and each series has some predictive ability for the variable of interest as in the study of Bai and Ng (2006). We recognize the diculties of monitoring the predictive ability of the estimated factors by pointing out the randomness nature of estimated factor from principle component analysis (PCA) when new observation arrives. Nevertheless, under the null hypothesis of no structural change, the rescaled monitoring test is proved to asymptotically behaves as a Brownian bridge for the FAR model. This implies the approach suggested in Chu et al. (1996) still can be used for the new test. The Mont Carlo experiment reveals the promising performance of the proposed test. Recent Developments in High-Dimensional Time Series Analysis

Ruey S. Tsay

The University of Chicago

Abstract

We discuss some recent developments in analysis of high-dimensional time series. The methods considered include (a) scalar component models, (b) various factor models, (c) model-based clustering, (d) diﬀusion index (principal components), and (e) partial least squares. Real examples are used in demon- stration. For scalar component models, a simpliﬁed method is proposed to search for scalar components

Session 2B

Biostatistics and Inference

Chair: Chen-Hsin Chen Academia Sinica

13:30~15:15 December 17, 2013 Characterization of hESC Transcriptome by Hybrid Sequencing

Wing Hung Wong

Stanford University

Abstract

Although transcriptional and posttranscriptional events are detected in RNAseq data from second generation sequencing (SGS), fulllength mRNA isoforms are not captured. On the other hand, third generation sequencing (TGS), which yields much longer reads, has current limitations of lower raw accuracy and throughput. Here, we combine SGS and TGS with a customde- signed statistical method for isoform identification and quantification to gener- ate a high confidence isoform data set for human embryonic stem cells (hESC). Revitalizing Clinical Trial Methodology and Translational Statistics

L. J. Wei

Harvard University

Abstract

Over the years, the process of designing, monitoring, and analyzing clinical studies for evaluating new treatments has gradually fallen into a fixed pattern. Generally clinical trialists do not utilize ”better” or newer methodologiesper- haps to avoid potential delays in the review process for drug approval. Scien- tific investigation is an evolving process. What we have learned from previous studies about methodological shortcomings should help us better plan and analyze future trials. Unfortunately, use of inefficient or inappropriate procedures persists even when better alternatives are available. In this lecture, we will explore various methodological issues and potential solutions to them. More importantly, we will discuss how to improve the current practice for speeding drug development by fostering reliable, clinically meaningful assessments on new drugs or devices with respect to the risk-cost-benefit perspectives. Coverage-based Rarefaction and Extrapolation: Standardizing Samples by Completeness Rather Than Size

Anne Chao Lou Jost

National Tsing Hua University EcoMinga Foundation

Abstract

We propose an integrated sampling, rarefaction, and extrapolation methodology to compare species richness of a set of communities based on samples of equal completeness (as measured by sample coverage) instead of equal size. Traditional rarefaction or extrapolation to equal-sized samples can misrepre- sent the relationships between the richnesses of the communities being compared, because a sample of a given size may be sufficient to fully characterize the lower-diversity community but insufficient to characterize the richer community. Thus the traditional method systematically biases the degree of differences between community richnesses. We derive a new analytic method for seamless coverage-based rarefaction and extrapolation. We show that this method yields less-biased comparisons of richness between communities, and manages this with less total sampling effort. When this approach is integrated with an adaptive coverage-based stopping rule during sampling, samples may be compared directly without rarefaction, so no extra data is taken and none is thrown away. Even if this stopping rule is not used during data collection, coverage-based rarefaction throws away less data than traditional size-based rarefaction, and more efficiently finds the correct ranking of communities according to their true richnesses. Several hypothetical and real examples demonstrate these advantages.

Session 3A

China Study

Chair: Ray-Yeutien Chou Academia Sinica

15:30~17:15 December 17, 2013 Prospect of China’s Economic Growth and Reform

Zhang Weiying Peking University

Abstract

China is in transition in both growth model and institutions. I shall analyze why the economic growth is necessarily downward-ajusting and how sustain- ability of even a lower-growth such as 7% depends upon some fundamental institutional changes, both economic and political. I shall also discuss major bariiers to these changes. I shall argue that the key to a successful transition is ideas and leadership. Put all together, future of China is very uncertain and unpredictable. Preliminary Analyses on Mainland China’s Inﬂation Data

Song Xi Chen Yudong Tu Peking University Peking University

Abstract

We analyze on the inflation (CPI) data from 2001-2012 and the associate macro-economic variable of the same period. Empirical results on predicting the CPI and fitting the data with models will be presented. Analysis of Chinese Interindustry Spillover Effect of R&D Capital Both the Horizontal and Vertical

Pingfang Zhu The Center for Econometric Study

Gede Xiang The Center for Econometric Study

Qing Han The Center for Econometric Study

LongTu Ke Shanghai ZhonShan Senior High School

Abstract

Making use of their characteristics, the paper analyzes the spillover effects of R&D capital in the Chinese industries. Both the horizontal and vertical spillovers are investigated. The vertical spillover effect is further divided into forward Spillover and backward Spillover. Employing spatial econometric analysis, we consider a panel data of 33 industries over 12 years in the study. Our empirical investigation shows that the forward spillover effect is not significant, but the backward and horizontal effects are statistically significant. The 33 industries are divided into three categories, namely high-, intermediate, and low-tech, based on the level of technology used. There is a substitution effect between the R&D of the high-tech industrial firms and that of the industry, shown by the horizontal spillover effect. On the other hand, there is a comple- ment effect between the R&D of the mid or low-tech industrial firms and that of the industry, reflected in the backward and horizontal spillover effects. This implies that suppliers of the upstream raw materials or intermediate goods dont contribute significantly to the motivation of the R&D within the industry. Thus, the R&D of the Chinese manufacturing industry doesnt completely support the virtuous cycle that the breakthrough in the fundamental study and innovation motivates application study, leading further to the development for R&D of higher-class products and the crucial role of upstream departments in the process. Consequently, our study shows that Chinese manufacturing industry depends more on external demand, confirming that the R&D of Chi- nese manufacturing industry is more likely to be affected by the spillover effect of its downstream industries.

Keywords: Chinese inter-industry; Spillover eﬀect of R&D; Spatial Econo- metrics; horizontal and vertical.

Session 3B

Time Series and Dependent Data

Chair : Mong-Na Lo Huang National Sun Yat -sen University

15:30~17:15 December 17, 2013 Multivariate Stochastic Regression: Sparsity, Singular Value Decomposition, and Time Series Applications

Tze Leung Lai

Stanford University

Abstract

We ﬁrst give a review of multivariate time series analysis, beginning with (a) canonical correlation analysis and scalar components of vector ARIMA models pioneered by Tiao and his collaborators and (b) multivariate ARMAX models in linear systems theory. The review also describes recent developments in multivariate forecasts in econometric time series, which often involve a large number of predictor variables in a moving window of historical data to cope with possible parameter changes. We then introduce a new uniﬁed approach involving singular value decompositions, sparsity, and high-dimensional variable selection followed by rank selection in stochastic regression models. Applications to macroeconomic time series data are also discussed. Estimating Social Intercorrelation with Sampled Network Data

Hansheng Wang Peking Unievrsity

Abstract

We consider here a social network from which one observes not only network structure (i.e., nodes and edges) but also a set of labels (or tags, keywords) for each node (or user). These labels are self-created and closely related to the user’s career status, life style, personal interests, and many others. Thus, they are of great interest for online marketing. To model their joint behavior with network structure, a statistical model is developed. The model is based on

the classical p1 model but allows the reciprocation parameter to be label dependent. For both dense and sparse networks, we obtain maximum likelihood estimators, which are statistically eﬃcient but computationally expensive. To alleviate the computational cost, a novel conditional maximum likelihood estimator is proposed for large scaled sparse network. The asymptotic properties of these estimators are investigated. Simulation studies are conducted and a real Sina Weibo dataset is analyzed. On Recent Developments of Nonstationary and Long-Memory Time Series

Ngai Hang Chan Chinese University of Hong Kong

Abstract

This talk aims at surveying some of the recent developments of nonstationary and long-memory time series. The underlying theme of recent endeav- our arises from the consideration of the order of magnitude of the observed Fisher’s information number. By means of a simple AR(1) model, the so called ”SNoTE”, it is shown how this number aﬀects the nonstationary behaviour in a subtle and important way. The talk concludes with the discussion of some of the recent results involving negative moment bounds of the observed Fisher’s information number and their applications to multi-step ahead predictions.

Schedule and Abstract

December 18, 2013 Daily Schedule

December 18 (Wednesday):

Plenary Session 3 [09:20 – 10:10]: Location: HSS International Conference Hall Title: Being an Informed Bayesian: Assessing Prior Informativeness and Prior-Likelihood Conflict Speaker: Xiao-Li Meng, Harvard University Chair: Jeff Wu, Georgia Institute of Technology

Plenary Session 4 [10:10 – 11:00]: Location: HSS International Conference Hall Title: Beyond Statistics: The Legacy of George Tiao on Environmental Science Speaker: Donald J. Wuebbles, University of Illinois, Urbana-Champaign Chair: Norden E. Huang, National Central University

Break [11:00 – 11:20]:

Parallel Sessions [11:20 – 12:30]: Session 4A (Environment) Location: HSS 1st Conference Room Chair: Jane-Ling Wang, University of California, Davis 11:20 a.m. George Tiao and the Issue of Ozone Change Alvin (Jim) Miller, Climate Prediction Center/NCEP/NWS/NOAA 11:55 a.m. Association of Cardiovascular Responses with Source-Apportioned Fine Particle Air Pollutions in Beijing Jing-Shiang Hwang, Academia Sinica

Session 4B (Finance) Location: HSS 2nd Conference Room Chair: Sheng-Cheng Hu, Academia Sinica 11:20 a.m. Local-Momentum Autoregression for Modeling Interest Rate and Term Structure Jin-Chuan Duan, National University of Singapore 11:55 a.m. On Buffered GARCH Processes Wai Keung Li, Hong Kong University

Lunch break and poster session [12:30 – 14:00]:

Parallel Sessions [14:00 – 15:45]: Session 5A (Bayesian inference) Location: HSS 1st Conference Room Chair: Dennis K.J. Lin, The Pennsylvania State University 2:00 p.m. Doubly Constrained Factor Models with Applications Henghsiu Tsai, Academia Sinica 2:35 p.m. Bayesian Inference on Smoothed Lexis Diagrams with Applications to Lung and Breast Cancer Trends Chao Agnes Hsiung, National Health Research Institutes 3:10 p.m. Combination of Forecasts and Bayesian Prediction Bounds for Operating Room Durations, Even for Procedures with Few or No Historical Data Johannes Ledolter, University of Iowa and Vienna University of Economics and Business

Session 5B (Parsimony and model selection) Location: HSS 2nd Conference Room Chair: Hung Chen, National Taiwan University 2:00 p.m. Aspects of Dimension Reduction and Variable Selection from Forward, Backward, and Parsimonious Modeling Perspectives Ker-Chau Li, Academia Sinica 2:35 p.m. Parsimony Inducing Priors for Large Scale State-Space Models Hedibert Freitas Lopes, George Washington University 3:10 p.m. A Misspecification-Resistant Information Criterion Ching-Kang Ing, Academia Sinica

Break [15:45 – 16:00]:

Parallel Sessions [16:00 – 17:10]: Session 6A (Dependent data) Location: HSS 1st Conference Room Chair: Yi-Ching Yao, Academia Sinica 4:00 p.m. Estimation of Extreme Quantiles for Functions of Dependent Random Variables Qiwei Yao, London School of Economics 4:35 p.m. Time Evolution of Income Distribution Yi-Ting Chen, Academia Sinica Session 6B (Finance) Location: HSS 2nd Conference Room Chair: Shin-Kun Peng, Academia Sinica 4:00 p.m. Risk Measures Based on First Four Moments and Resulting Trading Strategies Chung-Ming Kuan, National Taiwan University 4:35 p.m. Forecast of Portfolio Returns and Trading Strategies Hwai-Chung Ho, Academia Sinica

Closing Remark [17:10 – 17:25]: Location: HSS International Conference Hall Speaker: George C. Tiao Chair: Ching-Shui Cheng , Kamhon Kan, and Ruey S. Tsay

Dinner (Invited Speakers) [18:00 – 20:30] - Shin Yeh: Shin Kong Mitsukoshi Xinyi Branch Location: 8F, No. 9, Songshou Rd., Xinyi Dist., Taipei, Taiwan (A9 of Shin Kong Mitsukoshi Xinyi Place Branch, 8F) Buses leave Humanity and Social Sciences Building at 17:40

Plenary Session 3

Chair: Jeff Wu Georgia Institute of Technology

09:20~10:10 December 18, 2013 Being an Informed Bayesian: Assessing Prior Informativeness and Prior–Likelihood Conﬂict

Xiao-Li Meng

Harvard University

Abstract

Dramatically expanded routine adoption of the Bayesian approach has sub- stantially increased the need to assess both the confirmatory and contradictory information in our prior distribution, in reference to the information provided by our likelihood. Our diagnostic approach starts with the familiar posterior matching method; for a given likelihood model, we identify the difference in the sample sizes needed to form two likelihood functions that, when combined respectively with a given prior and a baseline prior, will lead to the same posterior summaries as chosen. This difference can be viewed as a ”prior data size” M(k), relative to the likelihood based on k independent, identically dis- tributed observations. The confirmatory information is captured by the M(k) function, which is roughly constant over k when no serious prior-likelihood conflict arises. The contradictory information is detectable in its derivative or finite difference as M(k) tends to decrease with k when contradictory prior specification detracts information from the likelihood. Intriguing findings include a universal low bound, -1, on the derivative of M(k) that represents the most extreme prior-likelihood conflict, and a super-informative phenomenon where the prior effectively gains an extra 50% prior data size relative to the baseline when the prior mean coincides with the truth. We demonstrate our method via several examples, including an application exploring the effects of immunoglobulin levels on lupus nephritis. We also establish theoretical results showing why the derivative of M(k) is a useful indicator for prior-likelihood conflict. (This is joint work with Matthew Reimherr and Dan Nicolae of The University of Chicago.)

Plenary Session 4

Chair: Norden E. Huang National Central University

10:10~11:00 December 18, 2013 Beyond Statistics: The Legacy of George Tiao on Science

Donald J. Wuebbles

University of Illinois

Abstract

George Tiao has had an amazing career. Everyone at this meeting is well aware of the major role that George has played in the development of statistics, including the development of time series analysis, Bayesian statistics, and the area of environmental statistics. Other presentations will focus on the eﬀects his work has had on the discipline of statistics and on the world of business. But the rest of the story is the inﬂuence he has had on three major issues con- fronting the atmospheric sciences and the study of the environment. Georges research has been instrumental in the development of the science to understand the role of human activities in local /regional air pollution, in the decline in the amount of stratospheric ozone, and in the increase in surface temperature and related changes occurring to the Earths climate. These are some of most important issues facing humanity over our lifetimes. The presentation focuses on those issues, our current understanding of the science, and the legacy left by George Tiao.

Session 4A

Environment

Chair: Jane-Ling Wang University of California, Davis

11:20~12:30 December 18, 2013 George Tiao and the Issue of Ozone Change

Alvin (Jim) Miller NOAA, Climate Prediction Center

Shi-Keng Yang Donald Wuebbles Wyle, Climate Prediction Center University of Illinois

Craig Long Climate Prediction Center

Abstract

We will begin with a very brief history of the issue of global ozone change. Its’ importance and the complications of the data that made it, initially, a com- plex statistical issue. We then discuss how the statistical discussions evolved into the development of the ozone ”Tiger Team”; a group comprised of ex- pert statisticians, data climatologists and numerical modelers. This ”Tiger Team” was the ﬁrst to develop methods that clearly indicated that the global ozone was changing not only in ways predicted by theory, but also in ways that were not, thus leading to an extended theory of chemical ozone depletion. Together, these results were an important element leading up to the adoption of the Montreal Protocols (and its successors) which were meant to alleviate the ozone issue. As an original member of this ”Tiger Team”, George Tiao was instrumental in the development of the statistical concepts employed and also demonstrated that the methods of statistical evaluation in common use by the meteorological community grossly overestimated the statistical signiﬁ- cance of the computed trends. This has led to a re-evaluation of the statistical standards used by the meteorological community. Finally, we will present the results of the most recent calculations that examine whether or not the ozone is, in fact, in recovery and what we believe are the next steps necessary. Association of Cardiovascular Responses with Fine Particle Air Pollutions in Beijing

Jing-Shiang Hwang Academia Sinica

Abstract

For concerns about the health of athletes and international visitors to 2008 Olympic Games in Beijing, the government mitigated the ambient air pollution by relocating, limiting or temporarily closing highly polluting, energy-intensive facilities in and around the city, and reducing vehicle usage by elaborate traf- fic regulations. These air quality interventions, albeit temporary, encouraged numerous investigations on air pollution and its biological effects before, during and after the Games, and provided us a unique opportunity to assess the effect of reduction in fine particles on cardiovascular responses. In this study, Bayesian approaches were used to identify fine particulate matter (PM) sources and estimate their contributions to the ambient air pollution in Beijing. The estimated contributions were brought into mixed-effects models as exposures for examining the association of cardiovascular responses of the exposed mice in a sub-chronic experiment. We will show how the alterations in cardiac parameters were closely related to changes in Beijing ambient PM concentration and various pollution source concentrations.

Session 4B

Finance

Chair: Sheng-Cheng Hu Academia Sinica

11:20~12:30 December 18, 2013 Local-Momentum Autoregression for Modeling Interest Rate and Term Structure

Jin-Chuan Duan

National University of Singapore

Abstract

The local-momentum autoregression (LM-AR) model is motivated by ob- serving the US interest rate movement over many decades. We note that interest rate over a long time span seems to mean revert albeit very slowly, and over a period of several months or years, interest rate can actually exhibit a momentum-like behavior, continuing an upward or downward trend for a fairly long time. We devise a parsimonious autogressive model that is globally mean-reverting but locally driven by momentum. The LM-AR model carries one extra parameter as compared to the standard mean-reverting model. We use the LM-AR model as the dominant factor for the interest rate dynamic, which reﬂects the global variation component of interest rates. In addition, we add a local perturbation factor that is of the standard mean-reverting type in order to capture more transitory aspect of the interest rate dynamic. We then derive the term structure model corresponding to this two-factor interest rate dynamic. Interestingly, this term structure, albeit being based on a global mean-reverting interest rate dynamic, can actually be hump-shaped. The interest rate and term structure models are empirically implemented on the US interest rates of several maturities on a weekly frequency over the period from 1954 to 2010. On Buﬀered GARCH Processes

Pak Hang Lo Guodong Li University of Hong Kong University of Hong Kong

Bo Guan Philip L. H. Yu University of Hong Kong University of Hong Kong

Wai Keung Li University of Hong Kong

Abstract

The traditional threshold GARCH model is extended to a new type of model through introducing a new regime switching mechanism. The new model, buffered GARCH model, has a ”buffer” zone in between two thresh- olds such that regime switching will not occur if the threshold variable is inside the buffer zone. We concentrate on the self-exciting buffered GARCH model. Maximum likelihood estimation is considered for the buffered-GARCH model. Asymptotic properties such as strong consistency and asymptotic normality are derived. Simulation results give support for the asymptotic properties. Two real data sets are fitted with conditional mean models and those residu- als series obtained are fitted with the buffered GARCH model to demonstrate the usefulness of the buffered GARCH model.

Session 5A

Bayesian Inference

Chair: Dennis K.J. Lin The Pennsylvania State University

14:00~15:45 December 18, 2013 Doubly Constrained Factor Models with Applications

Henghsiu Tsai Ruey S. Tsay Edward M. H. Lin Academia Sinica University of Chicago Academia Sinica

Ching-Wei Cheng Purdue University

Abstract

This paper focuses on factor analysis of high-dimensional data. We propose statistical methods that enable an analyst to make use of prior knowledge or substantive information to sharpen the estimation of common factors. Specif- ically, we consider a doubly constrained factor model that enables analysts to specify both row and column constraints of the data matrix to improve the estimation of common factors. The row constraints may represent classiﬁcations of individual subjects whereas the column constraints may show the categories of variables. We derive both the maximum likelihood and least squares estimates of the proposed doubly constrained factor model and use simulation to study the performance of the analysis in ﬁnite samples. Akaike information criterion is used for model selection. Monthly U.S. housing starts of nine ge- ographical divisions are used to demonstrate the application of the proposed model. Bayesian Inference on Smoothed Lexis Diagrams with Applications to Lung and Breast Cancer Trends

Chao A. Hsiung National Health Research Institutes

Li-Chu Chien Yuh-Jenn Wu National Health Research Institutes Christian University

I-Shou Chang National Health Research Institutes

Abstract

Cancer surveillance research often begins with a rate matrix, also called a Lexis diagram, of cancer incidence derived from cancer registry and census data. This paper constructs a smoothed Lexis diagram using Bayesian approach based on Bernstein polynormials and indicates its use in cancer surveillance research. This approach is direct and intuitive and avoids the non-identifiability issues frequently discussed in age-period-cohort models. We illustrate our approach by studying the trends in lung and breast cancer incidence in Taiwan. We find that for nearly every age group, the incidence rates for lung adenocarcinoma and female invasive breast cancer increased rapidly in past two decades and those for male lung squamous cell carcinoma started to decrease, which is consistent with the decline in male smoking rate started in 1985. The analyses indicate strong age, period and cohort effects. Combination of Forecasts and Bayesian Prediction Bounds for Operating Room Durations, Even for Procedures with Few or No Historical Data

Johannes Ledolter Franklin Dexter

University of Iowa University of Iowa

Abstract

Operational decisions on the day before and on the day of surgery rely on the forecasts of OR times, and the uncertainty in those forecasts aﬀects patient and surgeon waiting times. With ample historic data available for the surgeon and the procedure scheduled, standard statistical methods based on the lognor- mal distribution with parameters estimated by their sample statistics are quite accurate. However, standard methods work poorly for surgeon/procedure groups that contain little prior data, and they cannot be applied for procedures that the surgeon has not scheduled before. This limitation is commonplace. More than 75% of procedures are performed just once or twice annually, and more than half of cases are of a procedure scheduled by the surgeon less than three times per year.

OR durations (expressed in minutes) for three large academic hospitals are stratiﬁed according to procedure/surgeon groups. For each group, we have available historic (actual) OR durations as well as the scheduled OR durations that are determined by the surgeon and/or hospital administrator prior to the start of each surgery. The ﬁrst part of this talk assesses the performance of three types of forecasts: (1) Forecasts that make use of the average of prior OR times in that group; (2) forecasts that rely on just the scheduled OR time; and (3) forecasts that combine the scheduled OR time with the average of historic observations. We investigate how best to combine the information and we discuss how the combination weights change with the group size.

Point forecasts are of little use if they are not accompanied by prediction intervals. However, the calculation of a prediction interval is diﬃcult when there are only few (or in the worst case, no) observations as then one cannot estimate the group variance that is needed for its construction. We develop a Bayesian approach that uses a normal prior for the group mean and an inverse Gamma prior for the group variance. The scheduled duration is used as the mean, and a fraction of the group variance is taken as the variance of the prior distribution of the group mean. The parameters of the prior inverse Gamma distribution for the group variance are estimated from groups with ample data. The mean of the resulting posterior prediction interval is a weighted average of the sample mean of the available data and the scheduled duration; the weight depends on the number of observations and the worth of the scheduled duration as prior estimate of the mean. The sample variance of the available data adjusts the prior estimate of the variance, and the width of the posterior prediction interval balances the prior information and the variability in the sample data. The Bayesian approach can be used for groups with no prior data; it uses the scheduled duration and the prior information on the variance. For groups with a moderate amount of data, the prediction interval combines the prior information (the scheduled duration and the prior variance) with the available data.

Session 5B

Parsimony and Model Selection

Chair: Hung Chen National Taiwan University

14:00~15:45 December 18, 2013 Aspects of Dimension Reduction and Variable Selection from Forward, Backward and Parsimonious Modeling Perspectives

Ker-Chau Li

Academia Sinica

Abstract

There is a rich body of parameter-regularization methods aiming at find- ing important variables in multiple linear regression of very high dimension, including LASSO. More recently, the inverse regression perspective is also ex- ploited successfully for new methods of variable selection, as demonstrated in a recent paper by Professor Jun Liu and his collaborators (see also the references therein). Conceptually, one major advantage of the inverse regression formula- tion is the ability to bypass the nonlinearity issue faced in forward regression. This talk is motivated by an attempt to bring the notion of liquid association (LA) back to the well-developed area of regression. LA was introduced to study dynamic patterns of co-regulation between genes (Li 2002, PNAS). LA depicts the change in the covariation of the expression of two genes X, Y as some (but unknown to biologists ) cellular conditions vary. Imposing the additional assumption that the key cellular condition may also affect the expression of a third (but also unknown to biologists) gene Z, a simple statistical measure can be derived and used to search for leading candidates of Z, thereby shedding light on the molecular mechanism of gene regulation. How the LA methodology may be used for regression variable selection will be discussed, along with other issues such as how to comprise model deficiency for improving prediction accuracy. Parsimony Inducing Priors for Large Scale State-Space Models

H. F. Lopes R. E. McCulloch The University of Chicago The University of Chicago

R. S. Tsay The University of Chicago

Abstract

State-space models are commonly used in the engineering, economic, and statistical literatures. They are exible and encompass many well-known statistical models, including random coeffcient autoregressive models and dynamic factor models. Bayesian analysis of state-space models has attracted much interest in recent years. However, for large scale models, prior specifiation becomes a challenging issue in Bayesian inference. In this paper, we propose a exible prior for state-space models. The proposed prior is a mixture of four commonly entertained models, yet achieves parsimony in high-dimensional systems. Here ”parsimony” is represented by the idea that in a large system, some states may not be time-varying. Simulation and simple examples are used throughout to demonstrate the performance of the proposed prior. As an application, we consider the time-varying conditional covariance matrices of daily log returns of 94 components of the S&P 100 index, leading to a state- space model with 94 × 95/2 = 4, 465 time-varying states. Our model for this large system enables us to use parallel computing. A Misspecification-Resistant Information Criterion

Ching-Kang Ing Academia Sinica

Abstract

Model selection problems are usually classified into two categories according to whether the data generating process (DGP) is included among the family of candidate models. The first category assumes that the DGP belongs to the candidate family, and the objective of model selection is to identify this DGP with high probability. The second category assumes that the DGP is not one of the candidate models. In this case, choosing a model having good prediction capability becomes a major concern. However, most existing model selection criteria can only perform well in at most one category, and hence when the underlying category is unknown, the determination of selection criteria itself becomes a key point of contention. In this talk, we propose a misspecification- resistant information criterion (MRIC) to rectify this dilemma. We also illustrate the performance of MRIC from both theoretical and practical points of view. (This is joint work with Drs. H.-L. Hsu. and Chiao-Yi Yang )

Session 6A

Dependent Data

Chair: Yi-Ching Yao Academia Sinica

16:00~17:10 December 18, 2013 Estimation of Extreme Quantiles for Functions of Dependent Random Variables

Qiwei Yao

London School of Economics

Abstract

We propose a new method for estimating the extreme quantiles for a function of several dependent random variables. In contract to the conventional approach based on extreme value theory, we do not impose the condition that the tail of the underlying distribution admits an approximate parametric form, and, furthermore, our estimation makes use of the full observed data. The proposed method is semiparametric as no parametric forms are assumed on all the marginal distributions. But we select appropriate bivariate copulas to model the joint dependence structure by taking the advantage of the recent development in constructing large dimensional vine copulas. Consequently a sample quantile resulted from a large bootstrap sample drawn from the ﬁtted joint distribution is taken as the estimates for the extreme quantile. This estimator is proved to be consistent as long as the quantile to be estimated is not too extreme. The realiable and robust performance of the proposed method is further illustrated by simulation. Time Evolution of Income Distributions

Yi-Ting Chen Ruey S. Tsay Academia Sinica The University of Chicago

Abstract

In this paper, we propose a simple method to explore whether and how the income distribution (ID) evolution of a population could be explained by a set of time-series factors. This method compares the estimated ID sequence with a hypothetical ID sequence which is generated by the time-series factors and the subgroup-share sequences when the population is composed of different subgroups. It is applicable to exploring various aspects of the ID evolution, such as the growth (inequality) trend measured by the mean (Gini coeffcient) sequence and to decomposing the changes of the ID, mean, and Gini coeffcient over times for explanations. In the empirical part, we apply this method to assessing whether and how Taiwan’s family ID (FID) evolution in 1981-2010 could be explained by the family structure change. JEL

Session 6B

Finance

Chair: Shin-Kun Peng Academia Sinica

16:00~17:10 December 18, 2013 Risk Measures Based on First Four Moments and Resulting Trading Strategies

O-Chia Chuang Chung-Ming Kuan National Taiwan University National Taiwan University

Abstract

In this paper we propose a method to calculate the risk measures proposed by Aumann and Serrano (2008) and Huang, Tzeng, and Wang (2012), where the former is related to stochastic dominance, and the latter hinges on central dominance. This method enables us to utilize the information about mean, variance, skewness, and kurtosis of a distribution. We demonstrate that the risk measure of Huang et al. (2012) provides suﬀcient information for the investment decision of all constant absolute risk averse investors in the traditional portfolio selection model. A trading strategy is then constructed with respect to this measure. Our empirical results show that this trading strategy outperforms any buy-and-hold trading strategies during sample period from January 2001 to October 2009. Forecast of Portfolio Returns and Trading Strategies

Hwai-Chung Ho Academia Sinica

Abstract

Momentum strategies, also known as relative strength strategies of buy- ing winner stocks and selling loser ones, are prevalent among traders for its anomaly confirmed by many empirical studies. The momentum effect has attracted a great deal of attention as it poses a fundamental challenge to one of the main tenets of financial theory, the efficient market hypothesis. To further investigate the relation between the effect and market inefficiency, we propose an information diffusion model for asset pricing in which a price risk index is introduced to quantify the downside risk. We use the index to modify the traditional momentum strategy to test whether abnormal returns can be obtained. The empirical results demonstrate that the modified strategy not only achieves significant improvement on the overall performance, but also substan- tially reduce the drastic losses suffered from the 2008 global recession. We also establish the connection between the price risk index and the cross-sectional return differences based on the well-known three factors, the market beta, the firm size and the book-to-market ratio.

Poster Session

Chair: Ci-Ren Jiang Academia Sinica

12:30~14:00 December 18, 2013 Poster List 1. Title: Analysing the Asymmetric Effects of Positive and Negative Information on the Behaviour of Users of a Taiwanese Online Bulletin Board Presenter: Shu-Li Cheng, Academia Sinica

2. Title: A Bayesian Approach to Genome-Wide Genetic Association Studies (GWAS) with Survival Time as Outcome Presenter: Li-Hsin Chien, National Tsing-Hua University

3. Title: Estimation of Inverse Autocovariance Matrices for Long Memory Processes Presenter: Hai-Tang Chiou, National Sun Yat-sen University

4. Title: Dependence Modeling of Spatio-temporal Weather Extreme Events Presenter: Whitney Huang, Purdue University

5. Title: A Study of TAIEX Futures by Using EEMD-based Neural Network Learning Paradigms Presenter: Yu-Cyun Lai, National Chengchi University

6. Title: A Newly Hybrid TAR & MTAR Approach for Measuring the Asymmetric and Nonlinear Relationships Presenter: Shu-Hui Lee, National Taipei University

7. Title: Doubly Constrained Factor Models with Applications Presenter: Edward M. H. Lin, Academia Sinica

8. Title: Applying Hierarchical Bayesian Analysis to Prospect-Valence Learning Model for Iowa Gambling Task of Heroin Users Presenter: Jung-Tzu Liu, National Health Research Institutes

9. Title: High Frequency Cycle Analysis of Financial Data through Hilbert Huang Transform: IEEMD Approach Presenter: Erdost Torun, Academia Sinica

10. Title: SURE Estimates for Two Heteroscedastic Hierarchical Regression Models Presenter: Justin J. Yang, Harvard University

11. Title: Estimating Approximate Factor Models via Shrinkage Methods Presenter: Yu-Min Yen, Academia Sinica

12. Title: Estimation of Stochastic Frontier Model with Unobserved Common Shocks via the EM Algorithm Presenter: Shou-Yung Yin, Academia Sinica Analysing the Asymmetric Eﬀects of Positive and Negative Information on the Behaviour of Users of a Taiwanese Online Bulletin Board

Shu-li Cheng* Frederick Kin Hing Phoa Academia Sinica Academia Sinica

Jing-Shiang Hwang Wei-chung Liu Yun-jin Huang Academia Sinica Academia Sinica Academia Sinica

Abstract

Fitting statistical models to data collected from an online bulletin board, we investigate the effects of global and local social influence on users; behaviour towards posted messages. Although in an anonymous situation where social influence is assumed to be at minimum, our results show that there is a tendency for on-line users to adapt both positive and negative information locally and conform to the present trend when expressing opinions. More- over, the results suggest asymmetric effects between the influence of approval and disapproval comments on following users. The impact of local disapproval density is stronger than that of approval density on the relative log odds of expressing disapproval comments versus expressing approval comments subse- quently. However, the findings suggest no evidence of global social influence. To better understand and verify the impact of social influence, a further analysis is carried out to examine such relationships in a short time duration when users have not much time to observe others’ responses. The results suggest that the asymmetric effects between approval and disapproval comments on opinion expression are still significant. A Bayesian Approach to Genome-Wide Genetic Association Studies (GWAS) with Survival Time as Outcome

Li-Hsin Chien* National Tsing-Hua University

I-Shou Chang National Health Research Institutes

Chao A. Hsiung National Health Research Institutes

Abstract

Genome-wide association study using survival time as phenotype deserves attention. Important examples include time to progression or recurrence free survival of a cancer patient underwent a speciﬁc treatment and onset time of certain disease or biological event. Because the popular single SNP analysis approach with Bonferroni correction is too conservative and causes the miss- ing heritability issue, Bayesian approach with prior distribution motivated by heritability and introduced by proportion of variance explained (PVE) was proposed by Guan and Stephens [Ann. Appl. Stat. 5(3) (2011):17801815] as a variable selection problem in a multivariate linear regression model. We ex- tend this approach to survival outcome by introducing a prior also motivated by the classical concept of heritability. A carefully designed MCMC algorithm is used to sample the posterior distribution. Simulation studies indicate that this method outperforms the single SNP analysis method. Illustrations by a real dataset will be included. Estimation of Inverse Autocovariance Matrices for Long Memory Processes

Ching-Kang Ing Hai-Tang Chiou* Academia Sinica National Sun Yat-sen University

Meihui Guo National Sun Yat-sen University

Abstract

This work aims at estimating inverse autocovariance matrices of long memory processes. A modiﬁed Cholesky decomposition is used in conjunction with an increasing order autoregressive model to achieve this goal. The spectral norm consistency of the proposed estimate is established. We then apply this result to linear regression models with long-memory time series errors. In particular, we show that when the objective is to consistently estimate the inverse autocovariance matrix of the error process, the same approach still works well if the estimated (by least squares) errors are used in place of the unobserv- able ones. Finally, a simulation study is performed to illustrate our theoretical ﬁndings. Dependence Modeling of Spatio-temporal Weather Extreme Events

Whitney Huang* Hao Zhang Purdue University Purdue University

Abstract

There are two main objectives of spatial extreme modeling. The ﬁrst one is to model the marginal behavior of extremes where to calculate return levels is of the main concern. The second one, the modeling of spatio-temporal extreme dependence, which is more challenge. The most widely used approach, max- stable processes, forms one useful characterization of extreme dependence of spatial processes. However, the lack of space-time modeling and the restricted tail dependence structure may lead to overestimation of the level of dependence in the extremes. In this talk, we analyze the extreme events in terms of temperature and precipitation based on observations at 750 weather stations across China to study the regional pattern of extreme events. Some thoughts beyond max-stability are discussed. A Study of TAIEX Futures by Using EEMD-based Neural Network Learning Paradigms

YS Chen SH Huang National Chengchi University National Chengchi University

YC Lai* YH Shiau National Chengchi University National Chengchi University

Abstract

It is known that financial markets change uncertainly. In particular, stock price volatility is the most important issue for investors. In order to meet markets’ demand and to judge the rule of thumb, researchers trying to build theoretical models can efficiently predict the variations of financial markets. In the study, we used three models including ARMA model and two types of EEMD-ANN (EEMD: ensemble empirical mode decomposition; ANN: artifi- cial neural network) composite models to forecast the future price of TAIEX. Through EEMD, the price fluctuation of TAIEX can be decomposed into several IMFs (IMF: intrinsic mode function) with different economical meanings. In addition, two trading strategies were tested after the future price had been forecasted. Our results showed that one of EEMD-ANN models can forecast better outcomes than those of the traditional ARMA model. Owing to that, the increases of trading performance would be expected via the selected EEMD-ANN model. A Newly Hybrid TAR & MTAR Approach for Measuring the Asymmetric and Nonlinear Relationships

Yeong-Jia Goo Shu-Hui Lee* National Taipei University National Taipei University

Abstract

This study investigates the newly Hybrid Random Threshold Autoregres- sive model relationship between index option moneyness and stock index prices in Taiwan. Investigating the stock index price return of the time series is es- sential to improve the performance of options moneyness. Doubly Constrained Factor Models with Applications

Henghsiu Tsai Ruey S. Tsay Edward M. H. Lin* Academia Sinica University of Chicago Academia Sinica

Ching-Wei Cheng Purdue University

Abstract

This paper focuses on factor analysis of high-dimensional data. We propose statistical methods that enable an analyst to make use of prior knowledge or substantive information to sharpen the estimation of common factors. Specif- ically, we consider a doubly constrained factor model that enables analysts to specify both row and column constraints of the data matrix to improve the estimation of common factors. The row constraints may represent classiﬁcations of individual subjects whereas the column constraints may show the categories of variables. We derive both the maximum likelihood and least squares estimates of the proposed doubly constrained factor model and use simulation to study the performance of the analysis in ﬁnite samples. Akaike information criterion is used for model selection. Monthly U.S. housing starts of nine ge- ographical divisions are used to demonstrate the application of the proposed model. Applying Hierarchical Bayesian Analysis to Prospect-Valence Learning Model for Iowa Gambling Task of Heroin Users

Jung T. Liu* National Health Research Institutes

Yu C. Cheng National Health Research Institutes

Sheng C. Wang National Health Research Institutes

Hsiao H. Tsou National Health Research Institutes

Abstract

Impairment of decision-making is characteristic of substance abusers. They tended to choose short-term gains but neglect long-term loss. Whether treatment will modify the risky decision-making process largely remains unclear. In this study, we assessed performance of decision-making using Iowa Gam- bling Task (IGT), and we aimed to explore the treatment and time eﬀect on the decision-making process among heroin-users. By applying the prospect- valence learning (PVL) model of the IGT in patients, 3 latent components in decision-making, i.e., individuals loss aversion (less risky tendency), learning ability, and choice consistency in trials were derived from hierarchical Bayesian estimation procedure. The result shows that the heroin users may have lower learning ability in the most recent event. In addition, we found that the healthy controls choices are more deterministic, resulting in maximal choices from the deck with the highest expectancy. These results have useful implications for the prevention and intervention of drug use. High Frequency Cycle Analysis of Financial Data through Hilbert Huang Transform: IEEMD Approach

Ray Y. Chou Erdost Torun* Academia Sinica Academia Sinica

Norden E. Huang National Central University

Abstract

This study examines the high frequency cycles of daily S&P 500 index, Euro/U.S. dollar rate, USA 30-year government bond yield, west Texas intermediate oil price, and gold bullion price-New York for the period from January 1997 to April 2013 through Hilbert Huang Transformation with mod- ifications (HHT). Cyclic components of financial data are decomposed through Integrated-EEMD (IEEMD) approach, which is modified EEMD to take into account of widely seen financial data features. Comparison of IEEMD and EEMD shows that IEEMD decomposes data into more feasible cyclic components for financial data analysis. Hence, HHT detect instantaneous frequency - energy patterns, which denote velocity and strength of cyclic dynamics in financial data. The results indicate that major global and local economic, financial and political events have effects on energy characteristics of financial system. These results reveal that the HHT methods are useful in analyzing the financial series. SURE Estimates for Two Heteroscedastic Hierarchical Regression Models

Justin J. Yang* Samuel S. Kou Harvard University Harvard University

Abstract

Hierarchical models provide an eﬀective way for combining information from similar resources and achieving partial pooling of inferences. In the con- text of linear egression, one can further incorporate the covariate information in either the data sampling distribution or the prior distribution of mean parameters, which naturally leads to two hierarchical regression models. In this paper, we focus on the issue of shrinkage estimation for these two hierarchical regression models in the heteroscedastic case and propose a class of shrinkage estimators based on Steins unbiased estimate of risk (SURE). We study asymptotic properties of these shrinkage estimators as the number of means to be estimated grows to inﬁnity and then establish their asymptotic risk op- timalities in terms of the mean square loss. This paper is a natural extension of Xie, Kou, and Brown (2012). Estimating Approximate Factor Models via Shrinkage Methods

Ray Yeutien Chou Tso-Jung Yen Yu-Min Yen* Academia Sinica Academia Sinica Academia Sinica

Abstract

Approximate factor models and their extensions are widely used in forecasting and economic analysis due to their ability to extracting useful information from a large number of relevant variables. In these models, candidate pre- dictors are subject to some common components. In this paper, we consider eﬃcient estimation of an approximate factor model in which the candidate pre- dictors are additionally subject to idiosyncratic large uncommon components such as jumps or outliers. By assuming that occurrences of the uncommon components are rare, we propose an estimation procedure to simultaneously disentangle the common and uncommon components. We formulate the estimation problem as a penalized least squares problem in which a penalty function is imposed on the uncommon components. To obtain the estimate for the model, we ﬁrst divide the estimation problem into two sub-problems: an eigenvalue problem and a one dimensional shrinkage estimation problem. We then propose an algorithm to solve the two sub-problems iteratively. Simula- tion studies show the proposed method can deliver performances comparable to the traditional PCA method when predicting important macroeconomic variables. Estimation of Stochastic Frontier Model with Unobserved Common Shocks via the EM Algorithm

Shou-Yung Yin Academia Sinica

Abstract

We consider a linear model with time-invariant fixed effects to represent heterogeneity and the cross-sectional dependence by introducing common correlated effects, and the time-variant technical inefficiency and idiosyncratic errors jointly characterized by a multivariate skew normal distribution. To consistently estimate the slope coefficients and variances in the above model, we propose a transformation to eliminate fixed effects and common correlated effects. Based on the transformed likelihood function, we then introduce an EM Algorithm to robustly estimate these parameters. Our Monte Carlo simulation shows that the proposed method is quite accurate in the presence of common correlated effects, while conventional models that do not take these effects into account can result in severely biased parameter estimates. Participant List

Tomohiro Ando, Keio University

William R. Bell, U.S. Census Bureau

Feng-Shun Chai, Academia Sinica

Ngai-Hang Chan, Chinese University of Hong Kong

Ching-Cheng Chang, Academia Sinica

Ming-Chung Chang, National Tsing-Hua University

Teresa Chang, Directorate-General of Budget, Accounting and Statistics, Executive Yuan

Tzu-Pu Chang, Academia Sinica

Yuan-chin Ivan Chang, Academia Sinica

Yu-Wei Chang, National Tsing-Hua University

Anne Chao, National Tsing Hua University

Chern-Ching Chao, Academia Sinica

Min-Te Chao, Academia Sinica

Been-Lon Chen, Academia Sinica

Cathy WS Chen, Feng Chia University

Chang-Shang Chen, Judicial Yuan

Chen-Hsin Chen, Academia Sinica

Chia-Cheng Chen, Academia Sinica

Chun-Houh Chen, Academia Sinica

Chung Chen, Syracuse University

Hsuan-Yu Chen, Academia Sinica

Hung Chen, National Taiwan University

Hungyin Chen, Academia Sinica

Jau-er Chen, National Taiwan University

Rong Chen, Rutgers University

Song Xi Chen, Guanghua School of Management, Peking Unievrsity

Yi-Hau Chen, Academia Sinica

Yi-Ting Chen, Academia Sinica

Chi-Lun Cheng, Academia Sinica

Ching-Shui Cheng, Academia Sinica

Ching-Wei Cheng, Academia Sinica

Philip E. Cheng, Academia Sinica

Shu-li Cheng, Academia Sinica

Chien-ming Chi, National Taiwan University

Miao-Chen Chiang, Academia Sinica

Li-Hsin Chien, National Tsing Hua University

Shih-Cheng Chien, Academia Sinica

Hai-Tang Chiou, National Sun Yat-sen University

Jeng-Min Chiou, Academia Sinica

Ray Yeutien Chou, Academia Sinica

Rouh-Jane Chou, National Tsing Hua University

Yuan-Shih Chow, Academia Sinica

Shih-Kai Chu, Academia Sinica

Tsai Li Chun, Directorate-General of Budget, Accounting and Statistics, Executive Yuan

Ching-Fan Chung, National Tsing Hua University

Ashish Das, Indian Institute of Technology Bombay

Philip Dawid, Cambridge University

Jin-Chuan Duan, National University of Singapore

Cheng-Der Fuh, National Central University

Xiaoyan Gong, Guanghua School of Management, Peking University

Meihui Guo, National Sun Yat-sen University

Qing Han, Shanghai Academy of Social sciences

Chin-Sheun Ho, Academia Sinica

Hwai-Chung Ho, Academia Sinica

HsiangEn Hou, Academia Sinica

Hsihchia Hsieh, Providence University

Chao Agnes Hsiung, National Health Research Institutes

Hsiang-Ling Hsu, Academia Sinica

Man-Jen Hsu, Academia Sinica

Nan-Jung Hsu, National Tsing-Hua University

Sheng-Cheng Hu, Academia Sinica

Tsuey-Hwa Hu, Academia Sinica

Hsin-Cheng Huang, Academia Sinica

Fali Huang, Singapore Management University

James Huang, Academia Sinica

Mong-Na Lo Huang, National Sun Yat-sen University

Norden E. Huang, National Central University

Shih-Feng Huang, National University of Kaohsiung

Shuai-Wu Huang, National Chengchi University

Su-Yun Huang, Academia Sinica

Wen-Jang Huang, National University of Kaohsiung

Whitney Huang, Purdue University

Yung-Hsiang Huang, National Center for High-Performance Computing

Jing-Shiang Hwang, Academia Sinica

Tea-Yuan Hwang, National Tsing Hua University

Ching-Kang Ing, Academia Sinica

Ci-Ren Jiang, Academia Sinica

Hanjoon Michael Jung, Academia Sinica

Kamhon Kan, Academia Sinica

Chiun-How Kao, Academia Sinica

Takeaki Kariya, Meiji University

Samuel Kou, Harvard University

Chung-Ming Kuan, Council for Economic Planning and Development, Executive Yuan

Chii-shyang Kuo, Academia Sinica

Mei-Yu Lai, Academia Sinica

Shu-Mei Lai, Academia Sinica

Tze Leung Lai, Stanford University

Yu-Cyun Lai, National Chengchi University

Johannes Ledolter, The University of Iowa

Mei-Ling Ting Lee, University of Maryland

Shen-Ming Lee, Feng Chia University

Shu-Hui Lee, National Taipei University

Bowen Li, National Chino Tung University

Change-Yi Li, Academia Sinica

Chenxu Li, Guanghua School of Management, Peking University

Ker-Chau Li, Academia Sinica

Lung-An Li, Academia Sinica

Pai-Ling Li, Tamkang University

Wai-Keung Li, The University of Hong Kong

Zheng-Rong Li, Academia Sinica

Jen-Che Liao, Academia Sinica

Kung-Yee Liang, National Yang-Ming University

Chang-Ching Lin, Academia Sinica

Chia-Yu Lin, Academia Sinica

Dennis K.J. Lin, The Pennsylvania State University

Ding-Shun Lin, National Chengchi University

Edward M.H. Lin, Academia Sinica

Jin-Lung Henry Lin, National Dong Hwa University

Michelle Liou, Academia Sinica

Jun Liu, Harvard University

Jung-Tzu Liu, National Tsing Hua University

Hedibert Lopes, The George Washington University

Heng-Hui Lue, Tunghai University

Ping Ma, University of Georgia

Yanyuan Ma, Texas A&M University

Chao-Cheng Mai, Tamkang University

Agustin Maravall, Bank of Spain

Xiao-Li Meng, Harvard University

Alvin (Jim) Miller, National Oceanic and Atmospheric Administration

Daniel Peña, Universidad Carlos III de Madrid

Chien-Yu Peng, Academia Sinica

Shie-Ming Peng, Academia Sinica

Shin-Kun Peng, Academia Sinica

Ben-Chang Shia, Fu Jen Catholic University

You-Shin Shiau, National Chengchi University

Shwu-Rong Grace Shieh, Academia Sinica

Ada Shih, Academia Sinica

Jia-Hong Sie, National Yang-Ming University

Chor-yiu Sin, National Tsing Hua University

Mike So, The Hong Kong University

Jie-Yu Sung, Academia Sinica

George C. Tiao, The University of Chicago

Yin Jing Tien, Academia Sinica

Naitee Ting, Boehringer-Ingelheim Pharmaceutical Inc.

Howell Tong, London School of Economics and Political Science

Erdost Torun, Academia Sinica

Buu Chau Truong, Feng Chia University

Arthur Chih-Hsin Tsai, Academia Sinica

Henghsiu Tsai, Academia Sinica

Ming-Tien Tsai, Academia Sinica

Tsung-His Tsai, Academia Sinica

Ruey S. Tsay, The University of Chicago

Wen-Jen Tsay, Academia Sinica

Yuh-Chyuan Tsay, Academia Sinica

I-Ping Tu, Academia Sinica

Yundong Tu, Guanghua School of Management, Peking University

Henry Y. Wan, Cornell University

Chih-Chi Wang, Academia Sinica

Fan-Sen Wang, Academia Sinica

Hansheng Wang, Guanghua School of Management, Peking Unievrsity

Jane-Ling Wang, University of California, Davis

Naisyin Wang, University of Michigan

Ping Wang, Washington University

Wen-Ting Wang, Academia Sinica

Yilun Wang, Directorate-General of Budget, Accounting and Statistics, Executive Yuan

Lee-Jen Wei, Harvard University

Wing Hung Wong, Stanford University

Cheng-Wen Wu, National Yang-Ming University

Chung-Shu Wu, Academia Sinica

Jeff Wu, Georgia Institute of Technology

Tiee-Jian Wu, National Cheng Kung University

Wei-Ying Wu, National Dong Hwa University

Don Wuebbles, University of Illinois at Urbana-Champaign

Yoshiro Yamamura, Meiji University

Hsin-Chou Yang, Academia Sinica

Ju-Chen Yang, Harvard University

Sky Shi-Keng Yang, National Oceanic and Atmospheric Administration

Qiwei Yao, London School of Economics

Yi-Ching Yao, Academia Sinica

Chen-Hsiang Yeang, Academia Sinica

Tso-Jung Yen, Academia Sinica

Yu-Min Yen, Academia Sinica

Shou-Yung Yin, Academia Sinica

Jihai Yu, Guanghua School of Management, Peking University

Shih-Ti Yu, National Tsing-Hua University

Shu Hui Yu, National University of Kaohsiung

Shin-Sheng Yuan, Academia Sinica

Junni Zhang, Guanghua School of Management, Peking University

Weiying Zhang, Guanghua School of Management, Peking Unievrsity

Wenxuan Zhong, University of Georgia

Bo Zhou, University of Southern California

Pinfang Zhu, Shanghai Academy of Social Sciences