Econometric Methods for Program Evaluation
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Biometrics & Biostatistics
Hanley and Moodie, J Biomet Biostat 2011, 2:5 Biometrics & Biostatistics http://dx.doi.org/10.4172/2155-6180.1000124 Research Article Article OpenOpen Access Access Sample Size, Precision and Power Calculations: A Unified Approach James A Hanley* and Erica EM Moodie Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Canada Abstract The sample size formulae given in elementary biostatistics textbooks deal only with simple situations: estimation of one, or a comparison of at most two, mean(s) or proportion(s). While many specialized textbooks give sample formulae/tables for analyses involving odds and rate ratios, few deal explicitly with statistical considera tions for slopes (regression coefficients), for analyses involving confounding variables or with the fact that most analyses rely on some type of generalized linear model. Thus, the investigator is typically forced to use “black-box” computer programs or tables, or to borrow from tables in the social sciences, where the emphasis is on cor- relation coefficients. The concern in the – usually very separate – modules or stand alone software programs is more with user friendly input and output. The emphasis on numerical exactness is particularly unfortunate, given the rough, prospective, and thus uncertain, nature of the exercise, and that different textbooks and software may give different sample sizes for the same design. In addition, some programs focus on required numbers per group, others on an overall number. We present users with a single universal (though sometimes approximate) formula that explicitly isolates the impacts of the various factors one from another, and gives some insight into the determinants for each factor. -
UNEMPLOYMENT and LABOR FORCE PARTICIPATION: a PANEL COINTEGRATION ANALYSIS for EUROPEAN COUNTRIES OZERKEK, Yasemin Abstract This
Applied Econometrics and International Development Vol. 13-1 (2013) UNEMPLOYMENT AND LABOR FORCE PARTICIPATION: A PANEL COINTEGRATION ANALYSIS FOR EUROPEAN COUNTRIES OZERKEK, Yasemin* Abstract This paper investigates the long-run relationship between unemployment and labor force participation and analyzes the existence of added/discouraged worker effect, which has potential impact on economic growth and development. Using panel cointegration techniques for a panel of European countries (1983-2009), the empirical results show that this long-term relation exists for only females and there is discouraged worker effect for them. Thus, female unemployment is undercount. Keywords: labor-force participation rate, unemployment rate, discouraged worker effect, panel cointegration, economic development JEL Codes: J20, J60, O15, O52 1. Introduction The link between labor force participation and unemployment has long been a key concern in the literature. There is general agreement that unemployment tends to cause workers to leave the labor force (Schwietzer and Smith, 1974). A discouraged worker is one who stopped actively searching for jobs because he does not think he can find work. Discouraged workers are out of the labor force and hence are not taken into account in the calculation of unemployment rate. Since unemployment rate disguises discouraged workers, labor-force participation rate has a central role in giving clues about the employment market and the overall health of the economy.1 Murphy and Topel (1997) and Gustavsson and Österholm (2006) mention that discouraged workers, who have withdrawn from labor force for market-driven reasons, can considerably affect the informational value of the unemployment rate as a macroeconomic indicator. The relationship between unemployment and labor-force participation is an important concern in the fields of labor economics and development economics as well. -
What Is Econometrics?
Robert D. Coleman, PhD © 2006 [email protected] What Is Econometrics? Econometrics means the measure of things in economics such as economies, economic systems, markets, and so forth. Likewise, there is biometrics, sociometrics, anthropometrics, psychometrics and similar sciences devoted to the theory and practice of measure in a particular field of study. Here is how others describe econometrics. Gujarati (1988), Introduction, Section 1, What Is Econometrics?, page 1, says: Literally interpreted, econometrics means “economic measurement.” Although measurement is an important part of econometrics, the scope of econometrics is much broader, as can be seen from the following quotations. Econometrics, the result of a certain outlook on the role of economics, consists of the application of mathematical statistics to economic data to lend empirical support to the models constructed by mathematical economics and to obtain numerical results. ~~ Gerhard Tintner, Methodology of Mathematical Economics and Econometrics, University of Chicago Press, Chicago, 1968, page 74. Econometrics may be defined as the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference. ~~ P. A. Samuelson, T. C. Koopmans, and J. R. N. Stone, “Report of the Evaluative Committee for Econometrica,” Econometrica, vol. 22, no. 2, April 1954, pages 141-146. Econometrics may be defined as the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena. ~~ Arthur S. Goldberger, Econometric Theory, John Wiley & Sons, Inc., New York, 1964, page 1. 1 Robert D. Coleman, PhD © 2006 [email protected] Econometrics is concerned with the empirical determination of economic laws. -
Biostatistics Student Paper Competition the Department of Biostatistics at BUSPH Is Soliciting Applications for the 2013 Student Paper Competition
Biostatistics Student Paper Competition The Department of Biostatistics at BUSPH is soliciting applications for the 2013 student paper competition. The competition is open to all students enrolled in the Biostatistics Program at Boston University in the spring term of 2013. The winner of the competition will receive $750 as a travel award to attend the summer Joint Statistical Meetings. Travel to other conferences (i.e. ENAR, Society for Clinical Trials, American Society of Human Genetics, International Genetic Epidemiology Society) is possible in negotiation with the award committee. All students are strongly encouraged to submit the abstracts from their papers to the Joint Statistical Meetings, which has a deadline of February 4th. Students would also be encouraged to consider entering the competitions sponsored by sections of the Joint Statistical Meetings (e.g. The Biometrics section Byar Young Investigator award, for details see http://www.bio.ri.ccf.org/Biometrics/winner.html). TIMETABLE: January 11th, 2013 at noon: Deadline for students to submit pdf of manuscript January 25th, 2013: Announcement of awards REQUIREMENTS: The paper should be prepared double spaced, in manuscript format, using guidelines for authors typical of a biostatistical journal (e.g. Biometrics, Statistics in Medicine, or Biostatistics). A pdf copy of the manuscript should be submitted to the chair of the award committee, Ching-Ti Liu ([email protected]). The reported work should be relevant to biostatistics and must be the work of the student, although the manuscript can be co-authored with a faculty advisor or a small number of collaborators. The student must be the first author of the manuscript. -
Biometrical Applications in Biological Sciences-A Review on the Agony for Their Practical Efficiency- Problems and Perspectives
Biometrics & Biostatistics International Journal Review Article Open Access Biometrical applications in biological sciences-A review on the agony for their practical efficiency- Problems and perspectives Abstract Volume 7 Issue 5 - 2018 The effect of the three biological sciences- agriculture, environment and medicine S Tzortzios in the people’s life is of the greatest importance. The chain of the influence of the University of Thessaly, Greece environment to the form and quality of the agricultural production and the effect of both of them to the people’s health and welfare consists in an integrated system Correspondence: S Tzortzios, University of Thessaly, Greece, that is the basic substance of the human life. Agriculture has a great importance in Email [email protected] the World’s economy; however, the available resources for research and technology development are limited. Moreover, the environmental and productive conditions are Received: August 12, 2018 | Published: October 05, 2018 very different from one country to another, restricting the generalized transferring of technology. The statistical methods should play a paramount role to insure both the objectivity of the results of agricultural research as well as the optimum usage of the limited resources. An inadequate or improper use of statistical methods may result in wrong conclusions and in misuse of the available resources with important scientific and economic consequences. Many times, Statistics is used as a basis to justify conclusions of research work without considering in advance the suitability of the statistical methods to be used. The obvious question is: What importance do biological researchers give to the statistical methods? The answer is out of any doubt and the fact that most of the results published in specialized journals includes statistical considerations, confirms its importance. -
An Econometric Examination of the Trend Unemployment Rate in Canada
Working Paper 96-7 / Document de travail 96-7 An Econometric Examination of the Trend Unemployment Rate in Canada by Denise Côté and Doug Hostland Bank of Canada Banque du Canada May 1996 AN ECONOMETRIC EXAMINATION OF THE TREND UNEMPLOYMENT RATE IN CANADA by Denise Côté and Doug Hostland Research Department E-mail: [email protected] Hostland.Douglas@fin.gc.ca This paper is intended to make the results of Bank research available in preliminary form to other economists to encourage discussion and suggestions for revision. The views expressed are those of the authors. No responsibility for them should be attributed to the Bank of Canada. ACKNOWLEDGMENTS The authors would like to thank Pierre Duguay, Irene Ip, Paul Jenkins, David Longworth, Tiff Macklem, Brian O’Reilly, Ron Parker, David Rose and Steve Poloz for many helpful comments and suggestions, and Sébastien Sherman for his assistance in preparing the graphs. We would also like to thank the participants of a joint Research Department/UQAM Macro-Labour Workshop for their comments and Helen Meubus for her editorial suggestions. ISSN 1192-5434 ISBN 0-662-24596-2 Printed in Canada on recycled paper iii ABSTRACT This paper attempts to identify the trend unemployment rate, an empirical concept, using cointegration theory. The authors examine whether there is a cointegrating relationship between the observed unemployment rate and various structural factors, focussing neither on the non-accelerating-inflation rate of unemployment (NAIRU) nor on the natural rate of unemployment, but rather on the trend unemployment rate, which they define in terms of cointegration. They show that, given the non stationary nature of the data, cointegration represents a necessary condition for analysing the NAIRU and the natural rate but not a sufficient condition for defining them. -
Nine Lives of Neoliberalism
A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Plehwe, Dieter (Ed.); Slobodian, Quinn (Ed.); Mirowski, Philip (Ed.) Book — Published Version Nine Lives of Neoliberalism Provided in Cooperation with: WZB Berlin Social Science Center Suggested Citation: Plehwe, Dieter (Ed.); Slobodian, Quinn (Ed.); Mirowski, Philip (Ed.) (2020) : Nine Lives of Neoliberalism, ISBN 978-1-78873-255-0, Verso, London, New York, NY, https://www.versobooks.com/books/3075-nine-lives-of-neoliberalism This Version is available at: http://hdl.handle.net/10419/215796 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative -
The Ends of Four Big Inflations
This PDF is a selection from an out-of-print volume from the National Bureau of Economic Research Volume Title: Inflation: Causes and Effects Volume Author/Editor: Robert E. Hall Volume Publisher: University of Chicago Press Volume ISBN: 0-226-31323-9 Volume URL: http://www.nber.org/books/hall82-1 Publication Date: 1982 Chapter Title: The Ends of Four Big Inflations Chapter Author: Thomas J. Sargent Chapter URL: http://www.nber.org/chapters/c11452 Chapter pages in book: (p. 41 - 98) The Ends of Four Big Inflations Thomas J. Sargent 2.1 Introduction Since the middle 1960s, many Western economies have experienced persistent and growing rates of inflation. Some prominent economists and statesmen have become convinced that this inflation has a stubborn, self-sustaining momentum and that either it simply is not susceptible to cure by conventional measures of monetary and fiscal restraint or, in terms of the consequent widespread and sustained unemployment, the cost of eradicating inflation by monetary and fiscal measures would be prohibitively high. It is often claimed that there is an underlying rate of inflation which responds slowly, if at all, to restrictive monetary and fiscal measures.1 Evidently, this underlying rate of inflation is the rate of inflation that firms and workers have come to expect will prevail in the future. There is momentum in this process because firms and workers supposedly form their expectations by extrapolating past rates of inflation into the future. If this is true, the years from the middle 1960s to the early 1980s have left firms and workers with a legacy of high expected rates of inflation which promise to respond only slowly, if at all, to restrictive monetary and fiscal policy actions. -
Gravity Models and Panel Econometrics” 21 – 25 September 2020
World Trade Institute, University of Bern “Gravity Models and Panel Econometrics” 21 – 25 September 2020 Course Goals and content Course Content Grading The objective of this course is to serve as a A Trade Theory and the Gravity-Model Class participation (20 %); take-home exam practical guide for trade policy analysis with (80 %). Participants taking this course for 1. The Anderson and van Wincoop and the gravity model offering a balanced credit must attend all lectures and complete the Krugman Model approach between trade theory and econ- the take home exam. 2. The Eaton and Kortum Model metrics. We will discuss recent developments 3 Trade with Heterogeneous Firms in the economic foundation of gravity models 4. Zero Trade and the Helpman-Melitz- and describe best practices for quantifying the Rubinstein Model Organization general equilibrium impact of changes in trade 5. Measuring the Gains from Trade barriers and, especially, trade policies. The course is intended for PhD students. A 6. Universal Gravity limited number of people with relevant The course is organized in three modules. professional or academic interest may be also Module A concentrates on recent contri- B The Econometrics of Gravity Models of admitted. butions from trade theory that motivate Trade gravity models and form the basis of policy Lecture hours: 24 ECTS : 4 trade policy analysis. The second module will 1. Estimation of Linear High-dimensional take a closer look at the econometric issues of Fixed Effects models the estimation of gravity models and of 2. The Incidental Parameter Problem Timetable and Registration performing policy simulations. Starting point is 3. -
Econometric Theory
ECONOMETRIC THEORY MODULE – I Lecture - 1 Introduction to Econometrics Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Econometrics deals with the measurement of economic relationships. It is an integration of economic theories, mathematical economics and statistics with an objective to provide numerical values to the parameters of economic relationships. The relationships of economic theories are usually expressed in mathematical forms and combined with empirical economics. The econometrics methods are used to obtain the values of parameters which are essentially the coefficients of mathematical form of the economic relationships. The statistical methods which help in explaining the economic phenomenon are adapted as econometric methods. The econometric relationships depict the random behaviour of economic relationships which are generally not considered in economics and mathematical formulations. It may be pointed out that the econometric methods can be used in other areas like engineering sciences, biological sciences, medical sciences, geosciences, agricultural sciences etc. In simple words, whenever there is a need of finding the stochastic relationship in mathematical format, the econometric methods and tools help. The econometric tools are helpful in explaining the relationships among variables. 3 Econometric models A model is a simplified representation of a real world process. It should be representative in the sense that it should contain the salient features of the phenomena under study. In general, one of the objectives in modeling is to have a simple model to explain a complex phenomenon. Such an objective may sometimes lead to oversimplified model and sometimes the assumptions made are unrealistic. In practice, generally all the variables which the experimenter thinks are relevant to explain the phenomenon are included in the model. -
Chapter 5. Structural Vector Autoregression
Chapter 5. Structural Vector Autoregression Contents 1 Introduction 1 2 The structural moving average model 1 2.1 The impulse response function . 2 2.2 Variance decomposition . 3 3 The structural VAR representation 4 3.1 Connection between VAR and VMA . 5 3.2 The invertibility requirement . 5 4 Identi…cation of the structural VAR 8 4.1 The order condition . 9 4.2 What shouldn’tbe done . 9 4.3 A common normalization that provides n(n - 1)/2 restrictions . 11 4.4 Identi…cation through short run restrictions . 11 4.5 Identi…cation through long run restrictions . 12 5 Estimation and inference 13 5.1 Estimating exactly identi…ed models . 13 5.2 Estimating overly identi…ed models . 13 5.3 Inference . 14 6 Structural VAR versus fully speci…ed economic models 14 1. Introduction Following the work of Sims (1980), vector autoregressions have been extensively used by economists for data description, forecasting and structural inference. The discussion here focuses on structural inference. The key idea, as put forward by Sims (1980), is to estimate a model with minimal parametric restrictions and then subsequently test economic hypothesis based on such a model. This idea has attracted a great deal of attention since it promises to deliver an alternative framework to testing economic theory without relying on elaborately parametrized dynamic general equilibrium models. The material in this chapter is based on Watson (1994) and Fernandez-Villaverde, Rubio-Ramirez, Sargent and Watson (2007). We begin the discussion by introducing the structural moving average model, and show that this model provides answers to the “impulse” and “propagation” questions often asked by macroeconomists. -
Department of Statistics and Data Science Promotion and Tenure Guidelines
Approved by Department 12/05/2013 Approved by Faculty Relations May 20, 2014 UFF Notified May 21, 2014 Effective Spring 2016 2016-17 Promotion Cycle Department of Statistics and Data Science Promotion and Tenure Guidelines The purpose of these guidelines is to give explicit definitions of what constitutes excellence in teaching, research and service for tenure-earning and tenured faculty. Research: The most common outlet for scholarly research in statistics is in journal articles appearing in refereed publications. Based on the five-year Impact Factor (IF) from the ISI Web of Knowledge Journal Citation Reports, the top 50 journals in Probability and Statistics are: 1. Journal of Statistical Software 26. Journal of Computational Biology 2. Econometrica 27. Annals of Probability 3. Journal of the Royal Statistical Society 28. Statistical Applications in Genetics and Series B – Statistical Methodology Molecular Biology 4. Annals of Statistics 29. Biometrical Journal 5. Statistical Science 30. Journal of Computational and Graphical Statistics 6. Stata Journal 31. Journal of Quality Technology 7. Biostatistics 32. Finance and Stochastics 8. Multivariate Behavioral Research 33. Probability Theory and Related Fields 9. Statistical Methods in Medical Research 34. British Journal of Mathematical & Statistical Psychology 10. Journal of the American Statistical 35. Econometric Theory Association 11. Annals of Applied Statistics 36. Environmental and Ecological Statistics 12. Statistics in Medicine 37. Journal of the Royal Statistical Society Series C – Applied Statistics 13. Statistics and Computing 38. Annals of Applied Probability 14. Biometrika 39. Computational Statistics & Data Analysis 15. Chemometrics and Intelligent Laboratory 40. Probabilistic Engineering Mechanics Systems 16. Journal of Business & Economic Statistics 41.