Selected Readings

SELECTED READINGS

Focus on: Arnold Zellner

October 2012

1 Selected Readings –October 2012 INDEX

INTRODUCTION...... 8

1. WORKING PAPERS AND ARTICLES ...... 10

1.1. A. Zellner, “Models, Prior Information, and Bayesian Analysis,” Journal of Econometrics, 75, 1, 1996, 51-68...... 10

1.2. A. Zellner, “Past, Present and Future of Econometrics,” Journal of Statistical Planning and Inference, 49, 1996, 3-8, reprinted with commentary in Medium Econometrische Toepassingen (MET), 14, 2, 2006, 2-9...... 10

1.3. A. Zellner, “The Bayesian Method of Moments (BMOM): Theory and Applications,” Advances in Econometrics, 12, 1997, 85-105...... 11

1.4. A. Zellner, J. Tobias and H. Ryu, “Bayesian Method of Moments (BMOM) Analysis of Parametric and Semiparametric Regression Models”...... 11

1.5. A. Zellner and J. Tobias, “Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model,” April 1997. Paper presented at the Econometric Society Meeting, Caltech, June 1997 and published in the International Economic Review, 42, No. 1, February, 2001, 121-140...... 11

1.6. A. Zellner, “Remarks on a ‘Critique’ of the Bayesian Method of Moments (BMOM),” June 1997, published in Journal of Applied Statistics, 28, No. 6, 2001, 775-778...... 12

1.7. A. Zellner, J. Tobias and H. Ryu, “Bayesian Method of Moments Analysis of Time Series Models with an Application to Forecasting Turning Points in Output Growth,” October 1998...... 12

1.8. A. Zellner, “The Finite Sample Properties of Simultaneous Equations’ Estimates and Estimators: Bayesian and Non-Bayesian Approaches,” invited paper presented at conference honouring Carl F. Christ and published in L.R. Klein (ed.), Annals Issue, Journal of Econometrics, 83, 1998, 185-212...... 13

1.9. A. Zellner, “Past and Recent Results on Maximal Data Information Priors,” Journal of Statistical Research, 32, No.1, 1998, 1-22...... 13

1.10. A. Zellner and H. Ryu, “Alternative Functional Forms for Production, Cost and Returns to Scale Functions," Journal of Applied Econometrics, 13, 1998, 101-127...... 14

1.11. A. Zellner, “Keep It Sophisticatedly Simple,” 1998, Invited paper presented to the Tilburg Conference on Simplicity and published in A. Zellner, H. Kuezenkamp and M. McAleer (eds.), Simplicity, Inference and Econometric Modeling, Cambridge University Press, 2001, 242-262...... 15

2 Selected Readings –October 2012 1.12. A. Zellner, “New Information-Based Econometric Methods in Agricultural Economics: Discussion,” American Journal of Agricultural Economics, 81, 1999, 742-746...... 15

1.13. A. Zellner, “Bayesian Analysis of Golf,” May 1999, presented at Research Conference honouring George J. Judge, U. of Illinois, Champaign- Urbana...... 16

1.14. A. Zellner and C. Min, “Forecasting Turning Points in Countries’ Output Growth Rates: A Response to Milton Friedman,” Journal of Econometrics, 88, 1999, 203-206...... 16

1.15. A. Zellner and F.C. Palm, “Correction to Cointegration and Dynamic Simultaneous Equations Model by Cheng Hsiao.” 1999, and Econometrica, 68, Sept., 2000, 1293...... 16

1.16. A. Zellner and J. Tobias, “A Note on Aggregation, Disaggregation and Forecasting Performance,” June 1999, published in the Journal of Forecasting, 19 (2000), 457-469...... 16

1.17. A. Zellner, “Bayesian and Non-Bayesian Approaches to Scientific Modeling and Inference in Economics and Econometrics,” invited keynote address, presented at Research Conference in Honour of Professor Tong Hun Lee, Korea, August 1999 and published in Special Issue of the Korean Journal of Money and Finance, 2000, 11-56...... 17

1.18. A. Zellner and B. Chen, "Bayesian Modeling of Economies and Data Requirements," May 2000, paper presented as an invited keynote address at the June 2000 meeting of the International Institute of Forecasters and the International Journal of Forecasting, Lisbon, Portugal and as the Third Soumitra Kumar Chakravarti Lecture, Calcutta, India, December 2000 and published in Macroeconomic Dynamics, 5, 2001, 673-700. [See "A Report on Third Soumitra Kumar Chakravarti Memorial Lecture," with discussion by K. Das in Calcutta Statistical Association Bulletin, 51, 2001, 1-10.] ...... 18

1.19. A. Zellner, "Information Processing and Bayesian Analysis," August 2000, presented to the ASA August 2001 meeting and published in Annals Issue of the Journal of Econometrics, edited by A. Golan, 107 (2002), 41-50...... 19

1.20. A. Zellner, "The Marshallian Macroeconomic Model," September 2000, published in T. Nagishi, R.V. Ramachandran and K. Mino (eds.), Economic Theory, Dynamics and Markets: Essays in Honour of Ryuzo Sato, Kluwer Academic Publishers, 19-29...... 19

1.21. A. Zellner, "Comments on 'The State of Macroeconomic Forecasting' by Robert Fildes and H.O. Stekler "November 2000 and published in Journal of Macroeconomics 24, 4 (December 2002), 499-502...... 20

1.22. A. Zellner, "ISBA History and Meetings," November 2000, invited contribution for the International Society for Bayesian Analysis (ISBA) Bulletin, and included on ISBA website...... 20

3 Selected Readings –October 2012 1.23. A. Zellner, “Some Recent Developments in Econometric Inference,” November 20011, invited paper for volume honouring Robert L. Basmann, published in Econometric Reviews, 22 (2003), 203-215...... 20

1.24. A. Zellner, "Comments on Papers by Engle, Geweke and Granger," Journal of Econometrics, 100 1 (2001), 93-94...... 21

1.25. A. Zellner, "Foreword for Frontier Session, 'Markov Chain Monte Carlo Methods: A User's Guide for Agricultural Economics,’ “Canadian Journal of Agricultural Economics, 49 (2001), 1-2...... 21

1.26. A.J. van der Merwe, A.L. Pretorius, J. Hugo and A. Zellner, "Traditional Bayes and the Bayesian Method of Moment Analysis for the Mixed Linear Model with an Application to Animal Breeding," South African Statistical Journal, (2001), 35, 19-68...... 21

1.27. A. Zellner, “My Experiences with Nonlinear Dynamic Models in Economics,” invited keynote address to the Society for Nonlinear Dynamics in Economics meeting, Atlanta, Georgia, March 2001, published in Studies in Nonlinear Dynamics and Econometrics, vol. 6, No. 2 (2002), 1-16...... 22

1.28. A. Zellner, “Econometric and Statistical Data Mining, Prediction and Policy-Making,” invited paper presented at University of Tennessee, C. Warren Neel Conference on Statistical Data Mining and Knowledge Discovery, June 2002, and published in H. Bozdogan (ed.), Statistical Data Mining and Knowledge Discovery, New York: CRC Press, 2004, 57-78...... 22

1.29. A. Zellner, “Bayesian Shrinkage Estimates and Forecasts of Individual and Total or Aggregate Outcomes,” paper presented at American Statistical Association Meeting, New York, August 2002...... 23

1.30. A. Zellner, “Welcoming Message to the JIRSS,” Journal of the Iranian Statistical Society, Vol. 1, No. 1, 2002, 1-5...... 23

1.31. A. Zellner and G. Israilevich, “The Marshallian Macroeconomic Model: A Progress Report,” May 2003, invited paper presented at the Conference in Honour of Victor Zarnowitz, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Essen, Germany, June 27-28, 2003, published in Macroeconomic Dynamics, Vol. 9, 2005, 220-243 and reprinted in International Journal of Forecasting, 21 2005, 627-645, with discussion by A. Espasa...... 24

1.32. A. Zellner, “Some Aspects of the History of Bayesian Information Processing,” July 2003, presented at the American Statistical Association’s meeting, San Francisco, August 2003, to appear in Annals Issue of Journal of Econometrics, “Information and Entropy Econometrics – A Volume in Honour of Arnold Zellner.” ...... 24

1.33. R.A.L. Carter and A. Zellner, “The ARAR Error Model for Univariate Time Series and Distributed Lag Models,” December 2003, published in Studies in Nonlinear Dynamics and Econometrics, Vol. 8, Issue 1, 2004, 1-42. ... 25

4 Selected Readings –October 2012 1.34. R. Carter and A. Zellner, “AR versus MA Disturbance Terms,” Economics Bulletin, Vol. 3, No. 21, 2004, 1-3...... 25

1.35. A. Zellner, “Comments on Size Matters: The Standard Error of Regressions in The American Economic Review,” January 2004, presented at the American Economic Association meeting, San Diego, CA and published in The Journal of Socio-Economics, 33 (2004), 581-586, under the title “To test or not to test and if so, how? Comments on “size matters.” ...... 25

1.36. L. Marsh and A. Zellner, “Bayesian Solutions to Graduate Admissions and Related Selection Problems,” April 2004, published in Journal of Econometrics Annals Issue, 121 (2004), 405-426, “The Econometrics of Higher Education.” ...... 26

1.37. A. Zellner, “Generalizing the Standard Product Rule of Probability Theory,” July, 2004, revised December, 2005, published in Journal of Econometrics Annals Issue, 138, 1 (2007), 14-23...... 26

1.38. A. Zellner, “Honorary Lecture on S. James Press and Bayesian Analysis.” Invited keynote address presented to the Retirement Conference for Professor S. James Press, University of California at Riverside, May 2005. Published in Macroeconomic Dynamics, 10 (2006), 667-684 and reprinted, with permission, as “Some Thoughts about S. James Press and Bayesian Analysis” in the Journal of Quantitative Economics, New Series, Vol. 5, No. 2, July 2007, 1-18...... 27

1.39. A. Zellner, “Bayesian Analysis and Information Theory,” Summary of invited paper presented at the 2nd Conference on Information and Entropy Econometrics (IEE), September 23-25, 2005 to be published in American Statistical Association’s Business and Economics Statistics Section’s Proceedings Publication...... 27

1.40. A. Zellner, “Philosophy and Objectives of Econometrics.” Reprinted from Macroeconomic Analysis: Essays in Macroeconomics and Econometrics, D. Currie, R. Nobay, and D. Peels, eds. (London: Croom Helm, 1981), pp. 24- 34 with the kind permission of Croom Helm, Ltd. An Addendum [2005] has been added to the original paper reprinted in the Journal of Econometrics, 136 (2007), 331-339...... 28

1.41. A. Zellner, “Bayesian Econometrics: Past, Present and Future.” Invited keynote address presented at the Bank of Sweden’s Research Conference on Bayesian Econometric Methodology, Stockholm, September 8- 9, 2006...... 28

1.42. A. Zellner, “In Memory of Milton Friedman, A Great Economic Scientist and Person,” January 2007. published in Medium Econometrische Toepassingen (MET), vol. 15, issue 1, 2007, 2-5 and Indian Journal of Quantitative Economics, June, 2007...... 29

5 Selected Readings –October 2012 1.43. A. Zellner and T. Ando, “A Direct Monte Carlo Approach for Bayesian Analysis of the Seemingly Unrelated Regression Model,” March 2008. 29

1.44. A. Zellner, “Comments on ‘Mixtures of g-priors for Bayesian Variable Selection,’ by F. Liang, R. Paulo, G. Molina, M.A. Clyde and J.O. Berger,” July 2008...... 29

1.45. A. Zellner and J. Kibambe Ngoie, “The Effects of Freedom Reforms on the Growth Rate of the South African Economy,” 64 pp...... 30

1.46. A. Zellner and T. Ando, “Bayesian and Non-Bayesian Analysis of the Seemingly Unrelated Regression Model with Student-t Errors, 38 pp...... 30

1.47. A. Zellner. “Comments on ‘The Limits of Statistical Modeling’ by David Freedman,” April 2009, invited contribution to be published in the Eurasian Econometric Review...... 31

1.48. A. Zellner. “Comments on ‘Harold Jeffreys’ Theory of Probability Revisited,’ co-authored by C.P. Robert, N. Chopin and J. Rousseau.” Invited contribution presented at the O’Bayes Conference, Wharton School, University of Pennsylvania, June 2009...... 31

2. BOOKS ...... 32

2.1. D.A. Berry, K. Chaloner and J.F. Geweke (eds.), Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner, Wiley Series in Probability and Statistics, Wiley, 1996...... 32

2.2. A. Zellner, Bayesian Analysis in Econometrics and Statistics: The Zellner View and Papers, invited contribution to Economists of the Twentieth Century Series, M. Perlman and M. Blaugh, eds., Edward Elgar Publ. Co., UK and US, 1997...... 32

2.3. A. Zellner, H. Kuezenkamp and M.McAleer (eds.), Simplicity, Inference and Modeling (Keeping it Sophisticatedly Simple), Cambridge University Press, 2001...... 32

2.4. J. Crutchfield and A. Zellner, Economics of Marine Resources and Conservation Policy, reprint of the study of the International Pacific Halibut Conservation Program, Economic Aspects of the Pacific Halibut Industry, by J. Crutchfield and A. Zellner (with current commentary by D. Zilberman, A. Scott, J.E. Wilen, F.R. Homans and D. MacCaughran), University of Chicago Press, 2003...... 32

2.5. A. Zellner and F.C. Palm (eds.), The Structural Econometric Modeling, Time Series Analysis (SEMTSA) Approach, Cambridge University Press, 2004. 32

2.6. A. Zellner, Statistics, Econometrics and Forecasting, invited lectures in honor of Sir Richard Stone presented at Bank of England and National

6 Selected Readings –October 2012 Institute for Economic and Social Research, London, May 2001, and published by Cambridge University Press, 2004...... 32

2.7. A. Zellner, An Introduction to Bayesian Inference in Econometrics. Authorized translation from the English language edition published by John Wiley & Sons...... 32

2.8. A. Zellner, “Some Recent Developments in Bayesian Statistics and Econometrics,” September 1998. Summary of presentation to Maxent 1998 Meeting, Max Planck Institute for Plasma Physics, Garching b.Munich, Germany, July 27-31, 1998, and published in the Conference volume honoring Edwin T. Jaynes, W. van der Linden, V. Dose, R. Fischer and R. Preuss (eds.), Maximum Entropy and Bayesian Methods, Kluwer Academic Publishers, 1999, 207-216...... 32

3. INTERVIEWS...... 33

3.1. McClure, Michael, Turkington, Darrell and Weber, Ernst Juerg, "A Conversation with Arnold Zellner", 13pp, interview during visit to the University of West Australia, September 2009...... 33

3.2. Morrissey, Kathy (2006), "Interview with Arnold Zellner, November 2004," Amstat News, September, pp. 12-16...... 33

3.3. Garcia-Ferrer, Antonio (1998), "Professor Zellner: An Interview for the International Journal of Forecasting," International Journal of Forecasting, 14, pp. 303-312...... 33

3.4. Rossi, Peter (1989), "The ET Interview: Professor Arnold Zellner," in Econometric Theory, 5 (2), pp. 287-317...... 33

7 Selected Readings –October 2012 INTRODUCTION

Arnold Zellner, a leading economist at the University Of Chicago Booth School Of Business who pioneered the field of Bayesian econometrics, died on 11th August 2010 at his home in Chicago. He was 83. Zellner was known for the breadth of his contributions to many different areas of econometrics. His pioneering work in systems of equations, Bayesian statistics and econometrics, or time series analysis would each have earned him worldwide recognition. In addition to his theoretical work, Arnold Zellner fostered applications in fisheries conservation, production theory, forecasting, and many other fields. In both his theoretical and applied research, he believed that complicated problems can be solved by the application of a few powerful, simplifying concepts, what he called “sophisticated simplicity.” Zellner’s achievements include founding two major journals, organizing two National Bureau of Economic Research/National Science Foundation seminar series, and supervising more than 30 doctoral (Ph.D) dissertations in economics, finance, econometrics and statistics. He retired from teaching in 1996 after 30 years on the Chicago Booth faculty, but he remained active at the school until a few months before his death, doing research, publishing papers in academic journals and advising students on how to achieve their career goals. Zellner published more than 200 scholarly articles and 22 books and monographs, including An Introduction to Bayesian Inference in Econometrics, J. Wiley and Sons, Inc., 1971 and Basic Issues in Econometrics, University of Chicago Press, 1984. He also founded the International Society of Bayesian Analysis. In 1962, he published what became one of the most cited articles in econometrics, “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias,” in the Journal of the American Statistical Association. In addition to teaching M.B.A. courses in econometrics and Bayesian inference in econometrics, Arnold Zellner taught several Ph.D. courses. He was the H.G. B. Alexander Distinguished Service Professor of Economics and Statistics and director of Booth’s H.G. B. Alexander Research Foundation.

8 Selected Readings –October 2012 Beyond his strong commitment to teaching and research, Zellner was also known for his work to solve social and economic problems such as famine, unemployment, and economic stagnation. He received the prestigious McKinsey Award for Excellence in Teaching at Booth in 1984 and he established the B. Peter Pashigian Lecture Fund and Lecture Series in 2001. Arnold Zellner was a fellow of the Econometric Society, and the American Academy of Arts and Sciences, and a distinguished fellow of the American Economic Association. He was also president and a fellow of the American Statistical Association, and was a fellow of the International Institute of Forecasters and the American Association for the Advancement of Science.

The following list is a non-exhaustive, subjective selection of Arnold Zellner’s publications.

More information can be found at:

• The address of Arnold Zellner’s homepage at: http://faculty.chicagobooth.edu/arnold.zellner/more/

Contact point: GianLuigi Mazzi, "Responsible for Euro-indicators and statistical methodology", Estat – C4 "Key Indicators for European Policies" [email protected].

9 Selected Readings –October 2012 1. WORKING PAPERS AND ARTICLES

1.1. A. Zellner, “Models, Prior Information, and Bayesian Analysis,” Journal of Econometrics, 75, 1, 1996, 51-68.

Formulation of models for observations and prior densities for their parameters is an important activity in many sciences. In the present paper, after a discussion of this area of activity, entropy-based methods are employed to derive many central econometric and statistical models and noninformative and informative prior densities for their parameters in an explicit, reproducible manner. Examples are provided to illustrate the general procedures. In particular, maxent is employed to produce linear and nonlinear regression and autoregression models, hierarchical models, time- varying parameter models, etc. Then maximal data information prior (MDIP) densities for hyperparameters, common parameters in different likelihood functions, multinomial parameters, etc., are derived. Also the MDIP approach is utilized to produce prior odds for alternative hypotheses or models.

Full text available at: http://www.sciencedirect.com/science/article/pii/0304407695017682

In this article salient aspects of the past, present and future of econometrics are considered. These include a resumé of past key developments in econometric modelling, inference and uses of econometrics. Further, some comments are made relating to various statistical inference procedures, techniques of model formulation and the relations of theory and application. It is concluded that a stronger interaction between theory and application will do much to promote further progress in econometrics in the future.

Full text available at: http://www.sciencedirect.com/science/article/pii/0378375895000275

10 Selected Readings –October 2012 1.3. A. Zellner, “The Bayesian Method of Moments (BMOM): Theory and Applications,” Advances in Econometrics, 12, 1997, 85-105.

No abstract is available.

Full text available at: http://www.emeraldinsight.com/books.htm?chapterid=1840016

1.4. A. Zellner, J. Tobias and H. Ryu, “Bayesian Method of Moments (BMOM) Analysis of Parametric and Semiparametric Regression Models”.

The Bayesian Method of Moments is applied to semiparametric regression models using alternative series expansions of an unknown regression function. We describe estimation loss functions, predictive loss functions and posterior odds as techniques to determine how many terms in a particular expansion to keep and how to choose among different types of expansions. The developed theory is then applied in a Monte-Carlo experiment to data generated from a CES production function.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/current-papers/paper2.pdf

In this article we extend previous BMOM results by showing how information about a variance parameter and its relation to regression coefficients produces a rich class of post-data densities for regression parameters. Prediction and model selection techniques are also described. We also discuss the well-documented link between cross-entropy and the average log odds and then use this criterion in an experiment to compare results obtained from BMOM and Bayes approaches using data generated from known models.

Full text available at: http://onlinelibrary.wiley.com/doi/10.1111/1468-2354.00103/full

11 Selected Readings –October 2012 1.6. A. Zellner, “Remarks on a ‘Critique’ of the Bayesian Method of Moments (BMOM),” June 1997, published in Journal of Applied Statistics, 28, No. 6, 2001, 775-778.

No abstract is available.

Full text available at: http://www.tandfonline.com/doi/citedby/10.1080/02664760120059291#tabModule

1.7. A. Zellner, J. Tobias and H. Ryu, “Bayesian Method of Moments Analysis of Time Series Models with an Application to Forecasting Turning Points in Output Growth,” October 1998.

Bayesian method of moments (BMOM) analyses of central time series models are presented. These include derivations of post data densities for parameters, predictive densities for future observations and relative expected losses associated with alternative model specifications, e.g. a unit root versus a non-unit root AR(1) process or an AR(1) versus higher order AR processes. BMOM results are compared with those provided by traditional Bayesian and non-Bayesian approaches. An application to forecasting turning points in 18 countries’ annual output growth rates, 1980-1995 is provided using several variants of an autoregressive leading indicator model. Optimal forests include not only forecasts of dichotomous outcomes, e.g. downturn or no downturn, as in previous work, but also trichotomous outcomes, e.g., minor downturn, major downturn or no downturn or minor upturn, major upturn or no upturn. Empirical results indicate that about 70 percent of dichotomous outcomes are forecasted correctly, in line with previous results obtained using earlier data for the period 1974-1986 for the same 18 countries. A summary of results and some comments on future research are provided.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT-PAPERS/paper3.pdf

12 Selected Readings –October 2012 1.8. A. Zellner, “The Finite Sample Properties of Simultaneous Equations’ Estimates and Estimators: Bayesian and Non-Bayesian Approaches,” invited paper presented at conference honouring Carl F. Christ and published in L.R. Klein (ed.), Annals Issue, Journal of Econometrics, 83, 1998, 185-212.

After discussing the need for good finite sample estimation procedures for simultaneous equations models and showing the inadequacies of asymptotically justified estimators, it is shown how the Bayesian method of moments (BMOM) provides an exact, finite sample analysis of unrestricted reduced form systems. Then optimal, finite sample estimates of structural coefficients are derived using three standard loss functions and they are compared to traditional Bayesian optimal estimates. Monte Carlo experimental evidence from four studies on the relative performance of Bayesian and non-Bayesian estimators is reviewed with the finding that the performance of Bayesian estimators is better.

Full text available at: http://www.sciencedirect.com/science/article/pii/S0304407697000699

1.9. A. Zellner, “Past and Recent Results on Maximal Data Information Priors,” Journal of Statistical Research, 32, No.1, 1998, 1-22.

In this paper the origins of maximal data information priors (MDIPs), their various interpretations and justifications and relation to some other procedures for producing prior densities are reviewed. MDIPs for parameters of many statistical models are presented including MDIP odds for alternative hypotheses or models, MDIPs for the same parameter(s) in different likelihood functions and MDIPs for hyperparameters of hierarchical models. Last, it will be shown how the MDIP approach can produce not only "noninformative" priors but also "informative" priors.

Full text available at: http://www.isrt.ac.bd/sites/default/files/jsrissues/v32n1/jellner.pdf

13 Selected Readings –October 2012 1.10. A. Zellner and H. Ryu, “Alternative Functional Forms for Production, Cost and Returns to Scale Functions," Journal of Applied Econometrics, 13, 1998, 101-127.

We consider generalized production functions, introduced in Zellner and Revankar (1969), for output y=g(f) where g is a monotonic function and f is a homogeneous production function. For various choices of the scale elasticity or returns to scale as a function of output, differential equations are solved to determine the associated forms of the monotonic transformation, g(f). Then by choice of the form of f, the elasticity of substitution, constant or variable, is determined. In this way, we have produced and generalized a number of homothetic production functions, some already in the literature. Also, we have derived and studied their associated cost functions to determine how their shapes are affected by various choices of the scale elasticity and substitution elasticity functions. In general, we require that the returns to scale function be a monotonically decreasing function of output and that associated average cost functions be U- or L-shaped with a unique minimum. We also represent production functions in polar coordinates and show how this representation simplifies study of production functions' properties. Using data for the US transportation equipment industry, maximum likelihood and Bayesian methods are employed to estimate many different generalized production functions and their associated average cost functions. In accord with results in the literature, it is found that the scale elasticities decline with output and that average cost curves are U- or L-shaped with unique minima.

Data available at: http://qed.econ.queensu.ca/jae/1998-v13.2/zellner-ryu/

14 Selected Readings –October 2012 1.11. A. Zellner, “Keep It Sophisticatedly Simple,” 1998, Invited paper presented to the Tilburg Conference on Simplicity and published in A. Zellner, H. Kuezenkamp and M. McAleer (eds.), Simplicity, Inference and Econometric Modeling, Cambridge University Press, 2001, 242-262.

Some years ago, I came upon the phrase used in industry, ‘Keep it simple stupid’, that is, KISS, and thought about it in relation to scientific model-building. Since some simple models are stupid, I decided to reinterpret KISS to mean ‘Keep it sophisticatedly simple.’ In any event, KISS is very popular in many scientific and non-scientific areas. For example, the slogan of the Honda Motor Company is, ‘We make it simple.’ The Dutch Schipol airport in its advertising claims that, ‘It excels because it is simple and convenient.’ And it is well known that Einstein advised in connection with theorizing in the natural sciences, ‘Make it as simple as possible but no simpler.’ Also, the famous physicist Jaynes (1985, p. 344) wrote, ‘We keep our model as simple as possible so as not to obscure the point to be made and also to heed Arnold Zellner's wise advice about “sophisticatedly simple” models.’

Many, including myself, have for long advocated that workers in econometrics and statistics follow the advice of natural scientists and others to keep analyses and models sophisticatedly simple. In addition, I have pointed out that there are many important, sophisticatedly simple models and methods that work well in practice, that is in explanation and prediction, namely s = ½ gt2, E = mc2, PV = RT, maxent, etc.

Full text available at: http://ebooks.cambridge.org/chapter.jsf?bid=CBO9780511493164&cid=CBO978051 1493164A025

1.12. A. Zellner, “New Information-Based Econometric Methods in Agricultural Economics: Discussion,” American Journal of Agricultural Economics, 81, 1999, 742-746.

No abstract is available.

Full text available at: http://www.jstor.org/discover/10.2307/1244044?uid=3738488&uid=2129&uid=2&ui d=70&uid=4&sid=21101320035151

15 Selected Readings –October 2012 1.13. A. Zellner, “Bayesian Analysis of Golf,” May 1999, presented at Research Conference honouring George J. Judge, U. of Illinois, Champaign-Urbana.

In this paper Bayesian analysis is used to analyse some problems that arise in playing golf. Some issues that are examined include: whether a scientific analysis of golf is possible; concepts of probability, models and inference procedures that are most useful in fulfilling these aims; and whether Bayesian decision theoretic methods can be used to help improve George Judge's and other golfers' scores. Several canonical golf problems are formulated and analysed using Bayesian methods. Finally, frameworks for analysing a consumer demand for golfing services and products and professional golfers' income optimization problems are provided. In the concluding section, implications for the future will be considered.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/AZgolf1.pdf

1.14. A. Zellner and C. Min, “Forecasting Turning Points in Countries’ Output Growth Rates: A Response to Milton Friedman,” Journal of Econometrics, 88, 1999, 203-206.

No abstract is available.

Full text available at: http://www.sciencedirect.com/science/article/pii/S0304407698000177

1.15. A. Zellner and F.C. Palm, “Correction to Cointegration and Dynamic Simultaneous Equations Model by Cheng Hsiao.” 1999, and Econometrica, 68, Sept., 2000, 1293.

No abstract is available.

Full text available at: http://arno.unimaas.nl/show.cgi?fid=3291

1.16. A. Zellner and J. Tobias, “A Note on Aggregation, Disaggregation and Forecasting Performance,” June 1999, published in the Journal of Forecasting, 19 (2000), 457-469.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT-PAPERS/note.pdf

16 Selected Readings –October 2012 1.17. A. Zellner, “Bayesian and Non-Bayesian Approaches to Scientific Modeling and Inference in Economics and Econometrics,” invited keynote address, presented at Research Conference in Honour of Professor Tong Hun Lee, Korea, August 1999 and published in Special Issue of the Korean Journal of Money and Finance, 2000, 11- 56.

After brief remarks on the history of modelling and inference techniques in economics and econometrics, attention is focused on the emergence of economic science in the 20th century. First, the broad objectives of science and the Pearson- Jeffreys’ “unity of science” principle will be reviewed. Second, key Bayesian and non-Bayesian practical scientific inference and decision methods will be compared using applied examples from economics, econometrics and business. Third, issues and controversies on how to model the behaviour of economic units and systems will be reviewed and the structural econometric modelling, time series analysis (SEMTSA) approach will be described and illustrated using a macro-economic modelling and forecasting problem involving analyses of data for 18 industrialized countries over the years since the 1950s. Point and turning point forecasting results and their implications for macroeconomic modelling of economies will be summarized. Last, a few remarks will be made about the future of scientific inference and modelling techniques in economics and econometrics.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/bayesian.pdf

17 Selected Readings –October 2012

In previous work, we have used Bayesian methods in the analysis of various models to explain past variation and forecast future values of the rates of growth of real GDP for industrialized countries. Using these models, point and turning point forecasts were calculated and found to be reasonably accurate compared to those of benchmark and other models' forecasts. In this paper, Marshallian demand, supply and entry models are employed for major sectors of an economy that can be combined with factor market models for money, labor, capital and bonds to provide a Marshallian macroeconomic model (MMM). Herein, the sectoral models are used to produce sectoral output forecasts which are summed to provide forecasts of annual growth rates of U.S. real gross domestic product (GDP). These disaggregative forecasts are compared to forecasts derived from models implemented with aggregate data. The empirical evidence indicates that it pays to disaggregate, particularly when employing Bayesian shrinkage forecasting procedures. Further, some considerations bearing on alternative model-building strategies will be presented using the MMM as an example and describing its general properties. Last, data requirements for implementing MMMs are discussed.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/Modeling.pdf

18 Selected Readings –October 2012

Science involves learning from data. Here in this process of learning or information processing is considered within the context of optimal information processing, as in Zellner (1988, 1991, 1997). Information criterion functionals are formulated and optimized to provide optimal information processing rules, one of which is Bayes’ theorem. By varying the inputs and using alternative side conditions, various optimal information processing rules are derived and evaluated. Generally output information = input information for these rules and thus they are 100% efficient learning rules. When different weights or costs are associated with alternative inputs, “anchoring” like effects, much emphasized in the psychological literature are the results of optimal information processing procedures. Further, dynamic information processing results are reviewed and extensions noted. Last, some implications of the information processing approach for learning from data will be discussed.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/infoprob.pdf

In this progress report, we first indicate the origins and early development of the Marshallian Macroeconomic Model and briefly review some of our past empirical forecasting experiments with the model. Then we present recently developed one- sector, two-sector and n-sector models of an economy that can be employed to explain past experience predict future outcomes and analyze policy problems. The results of simulation experiments with various versions of the model are provided to illustrate some of its dynamic properties that include "chaotic" features. Last, we present comments on planned future work with the model.

Full text available at: http://economics.ucr.edu/seminars/winter06/econometrics/ArnoldZellner3-10-06.pdf

19 Selected Readings –October 2012 1.21. A. Zellner, "Comments on 'The State of Macroeconomic Forecasting' by Robert Fildes and H.O. Stekler "November 2000 and published in Journal of Macroeconomics 24, 4 (December 2002), 499-502.

No abstract is available.

Full text available at: http://www.sciencedirect.com/science/article/pii/S0164070402000605

1.22. A. Zellner, "ISBA History and Meetings," November 2000, invited contribution for the International Society for Bayesian Analysis (ISBA) Bulletin, and included on ISBA website.

No abstract is available.

Full text available at: http://www.stat.duke.edu/~brown/ISBA/archives/history.html

1.23. A. Zellner, “Some Recent Developments in Econometric Inference,” November 20011, invited paper for volume honouring Robert L. Basmann, published in Econometric Reviews, 22 (2003), 203-215.

Recent results in information theory, see Soofi (1996; 2001) for a review, include derivations of optimal information processing rules, including Bayes' theorem, for learning from data based on minimizing a criterion functional, namely output information minus input information as shown in Zellner (1988; 1991; 1997; 2002). Herein, solution post data densities for parameters are obtained and studied for cases in which the input information is that in (1) a likelihood function and a prior density; (2) only a likelihood function; and (3) neither a prior nor a likelihood function but only input information in the form of post data moments of parameters, as in the Bayesian method of moments approach. Then it is shown how optimal output densities can be employed to obtain predictive densities and optimal, finite sample structural coefficient estimates using three alternative loss functions. Such optimal estimates are compared with usual estimates, e.g., maximum likelihood, two-stage least squares, ordinary least squares, etc. Some Monte Carlo experimental results in the literature are discussed and implications for the future are provided.

Full text available at: http://www.tandfonline.com/doi/abs/10.1081/ETC-120020463

20 Selected Readings –October 2012

1.24. A. Zellner, "Comments on Papers by Engle, Geweke and Granger," Journal of Econometrics, 100 1 (2001), 93-94.

No abstract is available.

Full text available at: http://www.sciencedirect.com/science/article/pii/S0304407600000671

1.25. A. Zellner, "Foreword for Frontier Session, 'Markov Chain Monte Carlo Methods: A User's Guide for Agricultural Economics,’ “Canadian Journal of Agricultural Economics, 49 (2001), 1-2.

No abstract is available.

Full text available at: http://onlinelibrary.wiley.com/doi/10.1111/j.1744-7976.2001.tb00304.x/abstract

The Bayesian Method of Moments (BMOM) was introduced by Arnold Zellner in 1994. Given the data it enables researchers to make inverse probability statements about unknown parameters if the form of the likelihood function is unknown. In this paper the theory and results derived by Zellner (l972a) are extended to the mixed linear model. To illustrate the application of the method, data from the Elsenbuig Dormer sheep stud were analysed. A total of 879 weaning weight records, from the progeny of 17 sires were used. The estimates obtained were compared with the traditional Bayes and REML estimates.

Full text available at: http://reference.sabinet.co.za/document/EJC99042

21 Selected Readings –October 2012 1.27. A. Zellner, “My Experiences with Nonlinear Dynamic Models in Economics,” invited keynote address to the Society for Nonlinear Dynamics in Economics meeting, Atlanta, Georgia, March 2001, published in Studies in Nonlinear Dynamics and Econometrics, vol. 6, No. 2 (2002), 1-16.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/nonlinea.pdf

How to formulate models that work well in explanation, prediction and policy-making is a central problem in all fields of science. In this presentation, I shall explain the strategy, our Structural Econometric Modeling, Times Series Analysis (SEMTSA) approach that my colleagues and I have employed in our efforts to produce a macroeconomic model that works well in point and turning point forecasting, explanation and policy-making. Data relating to 18 industrialized countries over the years, taken from the IMF-IFS data base have been employed in estimation and forecasting tests of our models using fixed and time varying parameter models, Bayesian posterior odds, model combining or averaging, shrinkage, and Bayesian method of moments procedures. Building on this past work, in recent research economic theory and data for 11 sectors of the U.S. economy have been employed to produce models for each sector. The use of sector data and models to forecast individual sectors’ output growth rates and from them growth rates of total U.S. output will be compared to use of aggregate data and models to forecast growth rates of total U.S. output. As will be seen, IT PAYS TO DISAGGREGATE in this instance. Last, a description of some steps underway to improve and complete our Marshallian Macroeconomic Model of an economy will be described.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/ecostatd.pdf

22 Selected Readings –October 2012 1.29. A. Zellner, “Bayesian Shrinkage Estimates and Forecasts of Individual and Total or Aggregate Outcomes,” paper presented at American Statistical Association Meeting, New York, August 2002.

Bayesian shrinkage à la Stein and others can improve estimation of individual parameters and forecasts of individual future outcomes. In this paper the issue of the impact of shrinkage on the estimation of sums or totals of individual parameters and of individual outcomes is analyzed. Quadratic and “balanced” loss functions will be employed. The latter are linear combination of “goodness of fit” and “precision of estimation” loss functions. Several examples will be analyzed in detail to illustrate general principles.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/bayshrin.pdf

1.30. A. Zellner, “Welcoming Message to the JIRSS,” Journal of the Iranian Statistical Society, Vol. 1, No. 1, 2002, 1-5.

No abstract is available.

Full text available at: http://www.jirss.irstat.ir/browse.php?a_id=1&slc_lang=en&sid=1&ftxt=1

23 Selected Readings –October 2012 1.31. A. Zellner and G. Israilevich, “The Marshallian Macroeconomic Model: A Progress Report,” May 2003, invited paper presented at the Conference in Honour of Victor Zarnowitz, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Essen, Germany, June 27-28, 2003, published in Macroeconomic Dynamics, Vol. 9, 2005, 220-243 and reprinted in International Journal of Forecasting, 21 2005, 627-645, with discussion by A. Espasa.

In this progress report, we first indicate the origins and early development of the Marshallian Macroeconomic Model (MMM) and briefly review some of our past empirical forecasting experiments with the model. Then we present recently developed one sector, two sector and n sector models of an economy that can be employed to explain past experience, predict future outcomes and analyze policy problems. The results of simulation experiments with various versions of the model are provided to illustrate some of its dynamic properties that include “chaotic” features. Last, we present comments on planned future work with the model.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/marshmac.pdf

For many years, traditional Bayesian (TB) and information theoretic (IT) procedures for learning from data were viewed as distinctly different approaches. Derivations of the TB and IT learning models are reviewed and compared. Then the 1988 synthesis of the TB and IT learning models and generalizations of them are described along with descriptions of selected applications. Included are learning procedures that do not require use of likelihood functions and/or priors. Works by leading Bayesians and information theorists are cited and related to TB/IT issues.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/histybip.pdf

24 Selected Readings –October 2012 1.33. R.A.L. Carter and A. Zellner, “The ARAR Error Model for Univariate Time Series and Distributed Lag Models,” December 2003, published in Studies in Nonlinear Dynamics and Econometrics, Vol. 8, Issue 1, 2004, 1-42.

We show that the use of prior information derived from former empirical findings and/or subject matter theory regarding the lag structure of the observable variables together with an AR process for the error terms can produce univariate and single equation models that are intuitively appealing, simple to implement and work well in practice.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/ararerrm.pdf

1.34. R. Carter and A. Zellner, “AR versus MA Disturbance Terms,” Economics Bulletin, Vol. 3, No. 21, 2004, 1-3.

We show how several models with moving average errors can be easily rewritten as models with autoregressive errors, thereby simplifying inference.

Full text available at: http://www.accessecon.com/pubs/EB/2003/Volume3/EB-03C20006A.pdf

1.35. A. Zellner, “Comments on Size Matters: The Standard Error of Regressions in The American Economic Review,” January 2004, presented at the American Economic Association meeting, San Diego, CA and published in The Journal of Socio-Economics, 33 (2004), 581- 586, under the title “To test or not to test and if so, how? Comments on “size matters.”

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/comsize.pdf

25 Selected Readings –October 2012 1.36. L. Marsh and A. Zellner, “Bayesian Solutions to Graduate Admissions and Related Selection Problems,” April 2004, published in Journal of Econometrics Annals Issue, 121 (2004), 405-426, “The Econometrics of Higher Education.”

A graduate program has to decide how many offers of admission to make to achieve a targeted number of acceptances for its entering class given uncertainty about how many will accept such offers. We first structure and solve selection problems under the assumption that probabilities associated with uncertain outcomes have assumed values using symmetric and asymmetric loss functions and show sensitivity of solutions to errors in these assumed values. Then we show how these problems can be solved when these probabilities are heterogeneous and have to be estimated from sample data.

Full text available at: http://www.sciencedirect.com/science/article/pii/S0304407603002550

1.37. A. Zellner, “Generalizing the Standard Product Rule of Probability Theory,” July, 2004, revised December, 2005, published in Journal of Econometrics Annals Issue, 138, 1 (2007), 14-23.

In this paper the usual product rule of probability theory is generalized by relaxing the assumption that elements of sets are equally likely to be drawn. The need for such a generalization has been noted by Jeffreys (1998, pp. 24-25), among others, in his work on an axiom system for scientific learning from data utilizing Bayes’s Theorem. It is shown that by allowing probabilities of elements to be drawn to be different, generalized forms of the product rule and Bayes’s Theorem are obtained that reduce to the usual product rule and Bayes’s Theorem under certain assumptions that may be satisfactory in many cases encountered in practice in which the principle of insufficient reason is inadequate. Also, in comparing alternative hypotheses, allowing the prior odds to be random rather than fixed provides a useful generalization of the standard posterior odds.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/stdprodu.pdf

26 Selected Readings –October 2012 1.38. A. Zellner, “Honorary Lecture on S. James Press and Bayesian Analysis.” Invited keynote address presented to the Retirement Conference for Professor S. James Press, University of California at Riverside, May 2005. Published in Macroeconomic Dynamics, 10 (2006), 667-684 and reprinted, with permission, as “Some Thoughts about S. James Press and Bayesian Analysis” in the Journal of Quantitative Economics, New Series, Vol. 5, No. 2, July 2007, 1-18.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/pressjam.pdf

Bayesian analysts use a formal model, Bayes’ theorem to learn from their data in contrast to non-Bayesians who usually learn informally. In addition to proofs of Bayes’ theorem in the literature, herein it is shown how to derive Bayes’ theorem, the Bayesian learning model as a solution to an information theoretic optimization problem and that the solution is 100% efficient. Since this direct link between Bayesian analysis and information theory was established in Zellner (1988), recent work has shown how this optimization approach can be employed to produce a range of optimal learning models, all of them efficient, that have been employed to solve a wide range of “nonstandard” problems, e.g., those in which likelihood functions and/or prior densities are unavailable and thus the traditional, Bayesian learning model cannot be employed. These models can be compared to other available models by use of posterior odds. By having a set of optimal learning models “on the shelf” to solve a broader range of inverse inference problems, Bayesian analysis will be even more effective than it is today.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/bayainfo.pdf

27 Selected Readings –October 2012 1.40. A. Zellner, “Philosophy and Objectives of Econometrics.” Reprinted from Macroeconomic Analysis: Essays in Macroeconomics and Econometrics, D. Currie, R. Nobay, and D. Peels, eds. (London: Croom Helm, 1981), pp. 24-34 with the kind permission of Croom Helm, Ltd. An Addendum [2005] has been added to the original paper reprinted in the Journal of Econometrics, 136 (2007), 331-339.

In this paper, the philosophy and objectives of econometrics are discussed. The roles of induction, deduction and reduction in economic research are explained. Further, the roles of sophisticated simplicity and predictive performance in model building are described. Many examples, drawn from the work of leading scientists, are provided to illustrate general points. In addition, the work that has been done leading to the formulation of a disaggregate Marshallian Macroeconomic Model is briefly described. It is concluded that greater emphasis in teaching to explain the roles of deduction, induction and reduction in economic research would be very beneficial in terms of producing more valuable and useful research results. Also, development and use of many more sophisticatedly simple models and further use of Bayesian inference and decision techniques will do much to promote more rapid progress in economic science.

Full text available at: http://www.sciencedirect.com/science/article/pii/S0304407605002174

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan’s (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Last, some thoughts are presented that relate to the future of Bayesian Econometrics.

Full text available at: http://econ.ucsb.edu/~toseland/Zellner.pdf

28 Selected Readings –October 2012 1.42. A. Zellner, “In Memory of Milton Friedman, A Great Economic Scientist and Person,” January 2007. published in Medium Econometrische Toepassingen (MET), vol. 15, issue 1, 2007, 2-5 and Indian Journal of Quantitative Economics, June, 2007.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/current-papers/friedman.pdf

1.43. A. Zellner and T. Ando, “A Direct Monte Carlo Approach for Bayesian Analysis of the Seemingly Unrelated Regression Model,” March 2008.

Computationally efficient methods for Bayesian analysis of seemingly unrelated regression (SUR) models are described and applied that involve the use of a direct Monte Carlo (DMC) approach to calculate Bayesian estimation and prediction results using diffuse or informative priors. This DMC approach is employed to compute Bayesian marginal posterior densities, moments, intervals and other quantities, using data simulated from known models and also using data from an empirical example involving firms’ sales. The results obtained by the DMC approach are compared to those yielded by the use of a Markov Chain Monte Carlo (MCMC) approach. It is concluded from these comparisons that the DMC approach is worthwhile and applicable to many SUR and other problems.

Full text available at: http://www.sciencedirect.com/science/article/pii/S0304407610001119

1.44. A. Zellner, “Comments on ‘Mixtures of g-priors for Bayesian Variable Selection,’ by F. Liang, R. Paulo, G. Molina, M.A. Clyde and J.O. Berger,” July 2008.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/commentsonmixturesofgpriors-.pdf

29 Selected Readings –October 2012 1.45. A. Zellner and J. Kibambe Ngoie, “The Effects of Freedom Reforms on the Growth Rate of the South African Economy,” 64 pp.

In this paper, an evaluation of the effects of several policy reforms on the South African economy's growth rate are evaluated using an estimated disaggregated Marshallian Macroeconometric Model (MMM-DA). The results indicate that institution of these policy reforms would result in a real GDP growth rate of 8.5% with a standard error of 1.3 percentage points. To check on the predictive performance of our MMM-DA, results of forecasting experiments are presented that show it forecasts reasonably well and better than a benchmark autoregressive, leading indicator model. The "freedom reforms" considered include (1) freeing up barriers to firms' and workers' abilities to start up new firms and to obtain new employment and (2) health and educational programs that free individuals from poor health and ignorance, thereby enhancing their productivity. Some comparisons with the "Mrs. Thatcher-like" reforms that sparked growth in the British economy and several others worldwide are made. Last, the usefulness of our MMM-DA model is stressed and several suggestions for improving it are considered.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/effectsofreedomjpe.pdf

1.46. A. Zellner and T. Ando, “Bayesian and Non-Bayesian Analysis of the Seemingly Unrelated Regression Model with Student-t Errors, 38 pp.

A description of computationally efficient methods for the Bayesian analysis of Student-t seemingly unrelated regression (SUR) models with unknown degrees of freedom is given. The method combines a direct Monte Carlo (DMC) approach with an importance sampling procedure to calculate Bayesian estimation and prediction results using a diffuse prior. This approach is employed to compute Bayesian posterior densities for the parameters, as well as predictive densities for future values of variables and the associated moments, intervals and other quantities that are useful to both forecasters and others. The results obtained using our approach are compared to those yielded by the use of DMC for a standard normal SUR model.

Full text available at: http://www.sciencedirect.com/science/article/pii/S0169207009002131

30 Selected Readings –October 2012 1.47. A. Zellner. “Comments on ‘The Limits of Statistical Modeling’ by David Freedman,” April 2009, invited contribution to be published in the Eurasian Econometric Review.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/CURRENT- PAPERS/commentsonlimitsofstmod2.pdf

No abstract is available.

Full text available at: http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?view=body&id=pdfview_ 1&handle=euclid.ss/1263478379

31 Selected Readings –October 2012 2. BOOKS

2.1. D.A. Berry, K. Chaloner and J.F. Geweke (eds.), Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner, Wiley Series in Probability and Statistics, Wiley, 1996.

2.3. A. Zellner, H. Kuezenkamp and M.McAleer (eds.), Simplicity, Inference and Modeling (Keeping it Sophisticatedly Simple), Cambridge University Press, 2001.

2.5. A. Zellner and F.C. Palm (eds.), The Structural Econometric Modeling, Time Series Analysis (SEMTSA) Approach, Cambridge University Press, 2004.

2.6. A. Zellner, Statistics, Econometrics and Forecasting, invited lectures in honor of Sir Richard Stone presented at Bank of England and National Institute for Economic and Social Research, London, May 2001, and published by Cambridge University Press, 2004.

2.7. A. Zellner, An Introduction to Bayesian Inference in Econometrics. Authorized translation from the English language edition published by John Wiley & Sons.

32 Selected Readings –October 2012 3. INTERVIEWS

3.1. McClure, Michael, Turkington, Darrell and Weber, Ernst Juerg, "A Conversation with Arnold Zellner", 13pp, interview during visit to the University of West Australia, September 2009.

From the early 1960s onwards, Arnold Zellner has been publishing influential papers in the areas of statistical theory, econometric applications and macroeconomic modelling. This conversation canvasses Zellner’s transition from physics to economics, the reason for the renewal of interest in Bayes’s theorem in the 20th century, the empirical methodology of science underpinning the Chicago School and the influence of Alfred Marshall on Zellner’s recent contributions to macroeconomic modelling. The main insights to have emerged in the course of the conversation centre on the historical influences on Zellner’s thinking and his contribution to economic history.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/Interviews/Conversation_with_Ar nold_Zellner.pdf

3.2. Morrissey, Kathy (2006), "Interview with Arnold Zellner, November 2004," Amstat News, September, pp. 12-16.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/Interviews/interview.pdf

3.3. Garcia-Ferrer, Antonio (1998), "Professor Zellner: An Interview for the International Journal of Forecasting," International Journal of Forecasting, 14, pp. 303-312.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/Interviews/ferrerinterview.pdf

3.4. Rossi, Peter (1989), "The ET Interview: Professor Arnold Zellner," in Econometric Theory, 5 (2), pp. 287-317.

No abstract is available.

Full text available at: http://faculty.chicagobooth.edu/arnold.zellner/more/Interviews/rossiinterview.pdf

33 Selected Readings –October 2012