Econometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England


 Cuthbert Thornton
 2 years ago
 Views:
Transcription
1 Econometric Analysis of Cross Section and Panel Data Second Edition Jeffrey M. Wooldridge The MIT Press Cambridge, Massachusetts London, England
2 Preface Acknowledgments xxi xxix I INTRODUCTION AND BACKGROUND 1 1 Introduction Causal Relationships and Ceteris Paribus Analysis Stochastic Setting and Asymptotic Analysis Data Structures Asymptotic Analysis Some Examples Why Not Fixed Explanatory Variables? 9 2 Conditional Expectations and Related Concepts in Econometrics Role of Conditional Expectations in Econometrics Features of Conditional Expectations Definition and Examples Partial Effects, Elasticities, and Semielasticities Error Form of Models of Conditional Expectations Some Properties of Conditional Expectations Average Partial Effects Linear Projections 25 Problems 27 Appendix 2A 30 2.A.I Properties of Conditional Expectations 30 2.A.2 Properties of Conditional Variances and Covariances 32 2.A.3 Properties of Linear Projections 34 3 Basic Asymptotic Theory Convergence of Deterministic Sequences Convergence in Probability and Boundedness in Probability Convergence in Distribution Limit Theorems for Random Samples Limiting Behavior of Estimators and Test Statistics Asymptotic Properties of Estimators Asymptotic Properties of Test Statistics 45 Problems ' 47
3 II LINEAR MODELS 51 4 SingleEquation Linear Model and Ordinary Least Squares Estimation Overview of the SingleEquation Linear Model Asymptotic Properties of Ordinary Least Squares Consistency Asymptotic Inference Using Ordinary Least Squares HeteroskedasticityRobust Inference Lagrange Multiplier (Score) Tests Ordinary Least Squares Solutions to the Omitted Variables Problem Ordinary Least Squares Ignoring the Omitted Variables Proxy VariableOrdinary Least Squares Solution Models with Interactions in Unobservables: Random Coefficient Models Properties of Ordinary Least Squares under Measurement Error Measurement Error in the Dependent Variable Measurement Error in an Explanatory Variable 78 Problems 82 5 Instrumental Variables Estimation of SingleEquation Linear Models Instrumental Variables and TwoStage Least Squares Motivation for Instrumental Variables Estimation Multiple Instruments: TwoStage Least Squares General Treatment of TwoStage Least Squares Consistency Asymptotic Normality of TwoStage Least Squares Asymptotic Efficiency of TwoStage Least Squares Hypothesis Testing with TwoStage Least Squares HeteroskedasticityRobust Inference for TwoStage Least Squares Potential Pitfalls with TwoStage Least Squares IV Solutions to the Omitted Variables and Measurement Error Problems Leaving the Omitted Factors in the Error Term Solutions Using Indicators of the Unobservables 112 Problems 115
4 vii 6 Additional SingleEquation Topics Estimation with Generated Regressors and Instruments Ordinary Least Squares with Generated Regressors TwoStage Least Squares with Generated Instruments Generated Instruments and Regressors Control Function Approach to Endogeneity Some Specification Tests Testing for Endogeneity Testing Overidentifying Restrictions Testing Functional Form Testing for Heteroskedasticity Correlated Random Coefficient Models When Is the Usual IV Estimator Consistent? Control Function Approach Pooled Cross Sections and DifferenceinDifferences Estimation Pooled Cross Sections over Time Policy Analysis and DifferenceinDifferences Estimation 147 Problems 152 Appendix 6A Estimating Systems of Equations by Ordinary Least Squares and Generalized Least Squares Introduction Some Examples System Ordinary Least Squares Estimation of a Multivariate Linear System Preliminaries Asymptotic Properties of System Ordinary Least Squares Testing Multiple Hypotheses Consistency and Asymptotic Normality of Generalized Least Squares Consistency Asymptotic Normality Feasible Generalized Least Squares Asymptotic Properties Asymptotic Variance of Feasible Generalized Least Squares under a Standard Assumption 180
5 viii Contents Properties of Feasible Generalized Least Squares with (Possibly Incorrect) Restrictions on the Unconditional Variance Matrix Testing the Use of Feasible Generalized Least Squares Seemingly Unrelated Regressions, Revisited Comparison between Ordinary Least Squares and Feasible Generalized Least Squares for Seemingly Unrelated Regressions Systems Systems with Cross Equation Restrictions Singular Variance Matrices in Seemingly Unrelated Regressions Systems Linear Panel Data Model, Revisited Assumptions for Pooled Ordinary Least Squares Dynamic Completeness Note on Time Series Persistence Robust Asymptotic Variance Matrix Testing for Serial Correlation and Heteroskedasticity after Pooled Ordinary Least Squares Feasible Generalized Least Squares Estimation under Strict Exogeneity 200 Problems System Estimation by Instrumental Variables Introduction and Examples General Linear System of Equations Generalized Method of Moments Estimation General Weighting Matrix System TwoStage Least Squares Estimator Optimal Weighting Matrix The Generalized Method of Moments ThreeStage Least Squares Estimator Generalized Instrumental Variables Estimator Derivation of the Generalized Instrumental Variables Estimator and Its Asymptotic Properties Comparison of Generalized Method of Moment, Generalized Instrumental Variables, and the Traditional ThreeStage Least Squares Estimator 224
6 8.5 Testing Using Generalized Method of Moments Testing Classical Hypotheses Testing Overidentification Restrictions More Efficient Estimation and Optimal Instruments Summary Comments on Choosing an Estimator 232 Problems Simultaneous Equations Models Scope of Simultaneous Equations Models Identification in a Linear System Exclusion Restrictions and Reduced Forms General Linear Restrictions and Structural Equations Unidentified, Just Identified, and Overidentified Equations Estimation after Identification RobustnessEfficiency Tradeoff When Are 2SLS and 3SLS Equivalent? Estimating the Reduced Form Parameters Additional Topics in Linear Simultaneous Equations Methods Using Cross Equation Restrictions to Achieve Identification Using Covariance Restrictions to Achieve Identification Subtleties Concerning Identification and Efficiency in Linear Systems Simultaneous Equations Models Nonlinear in Endogenous Variables Identification Estimation Control Function Estimation for Triangular Systems Different Instruments for Different Equations 271 Problems Basic Linear Unobserved Effects Panel Data Models Motivation: Omitted Variables Problem Assumptions about the Unobserved Effects and Explanatory Variables Random or Fixed Effects? Strict Exogeneity Assumptions on the Explanatory Variables Some Examples of Unobserved Effects Panel Data Models 289
7 10.3 Estimating Unobserved Effects Models by Pooled Ordinary Least Squares Random Effects Methods Estimation and Inference under the Basic Random Effects Assumptions Robust Variance Matrix Estimator General Feasible Generalized Least Squares Analysis Testing for the Presence of an Unobserved Effect Fixed Effects Methods Consistency of the Fixed Effects Estimator Asymptotic Inference with Fixed Effects Dummy Variable Regression Serial Correlation and the Robust Variance Matrix Estimator Fixed Effects Generalized Least Squares Using Fixed Effects Estimation for Policy Analysis First Differencing Methods Inference Robust Variance Matrix Testing for Serial Correlation Policy Analysis Using First Differencing Comparison of Estimators Fixed Effects versus First Differencing Relationship between the Random Effects and Fixed Effects Estimators Hausman Test Comparing Random Effects and Fixed Effects Estimators 328 Problems More Topics in Linear Unobserved Effects Models Generalized Method of Moments Approaches to the Standard Linear Unobserved Effects Model Equivalance between GMM 3SLS and Standard Estimators Chamberlain's Approach to Unobserved Effects Models Random and Fixed Effects Instrumental Variables Methods Hausman and TaylorType Models First Differencing Instrumental Variables Methods 361
8 xi 11.5 Unobserved Effects Models with Measurement Error Estimation under Sequential Exogeneity General Framework Models with Lagged Dependent Variables Models with IndividualSpecific Slopes Random Trend Model General Models with IndividualSpecific Slopes Robustness of Standard Fixed Effects Methods Testing for Correlated Random Slopes 384 Problems 387 III GENERAL APPROACHES TO NONLINEAR ESTIMATION MEstimation, Nonlinear Regression, and Quantile Regression Introduction Identification, Uniform Convergence, and Consistency Asymptotic Normality TwoStep MEstimators Consistency Asymptotic Normality Estimating the Asymptotic Variance Estimation without Nuisance Parameters Adjustments for TwoStep Estimation Hypothesis Testing Wald Tests Score (or Lagrange Multiplier) Tests Tests Based on the Change in the Objective Function Behavior of the Statistics under Alternatives Optimization Methods NewtonRaphson Method Berndt, Hall, Hall, and Hausman Algorithm Generalized GaussNewton Method Concentrating Parameters out of the Objective Function Simulation and Resampling Methods Monte Carlo Simulation Bootstrapping 438
9 12.9 Multivariate Nonlinear Regression Methods Multivariate Nonlinear Least Squares Weighted Multivariate Nonlinear Least Squares Quantile Estimation Quantiles, the Estimation Problem, and Consistency Asymptotic Inference Quantile Regression for Panel Data 459 Problems Maximum Likelihood Methods Introduction Preliminaries and Examples General Framework for Conditional Maximum Likelihood Estimation Consistency of Conditional Maximum Likelihood Estimation Asymptotic Normality and Asymptotic Variance Estimation Asymptotic Normality Estimating the Asymptotic Variance Hypothesis Testing Specification Testing Partial (or Pooled) Likelihood Methods for Panel Data Setup for Panel Data Asymptotic Inference Inference with Dynamically Complete Models Panel Data Models with Unobserved Effects Models with Strictly Exogenous Explanatory Variables Models with Lagged Dependent Variables TwoStep Estimators Involving Maximum Likelihood SecondStep Estimator Is Maximum Likelihood Estimator Surprising Efficiency Result When the FirstStep Estimator Is Conditional Maximum Likelihood Estimator QuasiMaximum Likelihood Estimation General Misspecification Model Selection Tests QuasiMaximum Likelihood Estimation in the Linear Exponential Family 509
10 Generalized Estimating Equations for Panel Data 514 Problems 517 Appendix 13A Generalized Method of Moments and Minimum Distance Estimation Asymptotic Properties of Generalized Method of Moments Estimation under Orthogonality Conditions Systems of Nonlinear Equations Efficient Estimation General Efficiency Framework Efficiency of Maximum Likelihood Estimator Efficient Choice of Instruments under Conditional Moment Restrictions Classical Minimum Distance Estimation Panel Data Applications Nonlinear Dynamic Models Minimum Distance Approach to the Unobserved Effects Model Models with TimeVarying Coefficients on the Unobserved Effects 551 Problems 555 Appendix 14A 558 IV NONLINEAR MODELS AND RELATED TOPICS Binary Response Models Introduction Linear Probability Model for Binary Response Index Models for Binary Response: Probit and Logit Maximum Likelihood Estimation of Binary Response Index Models Testing in Binary Response Index Models Testing Multiple Exclusion Restrictions Testing Nonlinear Hypotheses about fi Tests against More General Alternatives 571
11 15.6 Reporting the Results for Probit and Logit Specification Issues in Binary Response Models Neglected Heterogeneity Continuous Endogenous Explanatory Variables Binary Endogenous Explanatory Variable Heteroskedasticity and Nonnormality in the Latent Variable Model Estimation under Weaker Assumptions Binary Response Models for Panel Data Pooled Probit and Logit Unobserved Effects Probit Models under Strict Exogeneity Unobserved Effects Logit Models under Strict Exogeneity Dynamic Unobserved Effects Models Probit Models with Heterogeneity and Endogenous Explanatory Variables Semiparametric Approaches 632 Problems Multinomial and Ordered Response Models Introduction Multinomial Response Models Multinomial Logit Probabilistic Choice Models Endogenous Explanatory Variables Panel Data Methods Ordered Response Models Ordered Logit and Ordered Probit Specification Issues in Ordered Models Endogenous Explanatory Variables Panel Data Methods 662 Problems Corner Solution Responses Motivation and Examples Useful Expressions for Type I Tobit 671
12 xv 17.3 Estimation and Inference with the Type I Tobit Model Reporting the Results Specification Issues in Tobit Models Neglected Heterogeneity Endogenous Explanatory Models Heteroskedasticity and Nonnormality in the Latent Variable Model Estimating Parameters with Weaker Assumptions TwoPart Models and Type II Tobit for Corner Solutions Truncated Normal Hurdle Model Lognormal Hurdle Model and Exponential Conditional Mean Exponential Type II Tobit Model TwoLimit Tobit Model Panel Data Methods Pooled Methods Unobserved Effects Models under Strict Exogeneity Dynamic Unobserved Effects Tobit Models 713 Problems Count, Fractional, and Other Nonnegative Responses Introduction Poisson Regression Assumptions Used for Poisson Regression and Quantities of Interest Consistency of the Poisson QMLE Asymptotic Normality of the Poisson QMLE Hypothesis Testing Specification Testing Other Count Data Regression Models Negative Binomial Regression Models Binomial Regression Models Gamma (Exponential) Regression Model Endogeneity with an Exponential Regression Function Fractional Responses 748
13 xvi Contents Exogenous Explanatory Variables Endogenous Explanatory Variables Panel Data Methods Pooled QMLE Specifying Models of Conditional Expectations with Unobserved Effects Random Effects Methods Fixed Effects Poisson Estimation Relaxing the Strict Exogeneity Assumption Fractional Response Models for Panel Data 766 Problems Censored Data, Sample Selection, and Attrition Introduction Data Censoring Binary Censoring Interval Coding Censoring from Above and Below Overview of Sample Selection When Can Sample Selection Be Ignored? Linear Models: Estimation by OLS and 2SLS Nonlinear Models Selection on the Basis of the Response Variable: Truncated Regression Incidental Truncation: A Probit Selection Equation Exogenous Explanatory Variables Endogenous Explanatory Variables Binary Response Model with Sample Selection An Exponential Response Function Incidental Truncation: A Tobit Selection Equation Exogenous Explanatory Variables Endogenous Explanatory Variables Estimating Structural Tobit Equations with Sample Selection Inverse Probability Weighting for Missing Data 821
14 xvii 19.9 Sample Selection and Attrition in Linear Panel Data Models Fixed and Random Effects Estimation with Unbalanced Panels Testing and Correcting for Sample Selection Bias Attrition 837 Problems Stratified Sampling and Cluster Sampling Introduction Stratified Sampling Standard Stratified Sampling and Variable Probability Sampling Weighted Estimators to Account for Stratification Stratification Based on Exogenous Variables Cluster Sampling Inference with a Large Number of Clusters and Small Cluster Sizes Cluster Samples with UnitSpecific Panel Data Should We Apply ClusterRobust Inference with Large Group Sizes? Inference When the Number of Clusters Is Small Complex Survey Sampling 894 Problems Estimating Average Treatment Effects Introduction A Counterfactual Setting and the SelfSelection Problem Methods Assuming Ignorability (or Unconfoundedness) of Treatment Identification Regression Adjustment Propensity Score Methods Combining Regression Adjustment and Propensity Score Weighting Matching Methods 934
15 21.4 Instrumental Variables Methods Estimating the Average Treatment Effect Using IV Correction and Control Function Approaches Estimating the Local Average Treatment Effect by IV Regression Discontinuity Designs The Sharp Regression Discontinuity Design The Fuzzy Regression Discontinuity Design Unconfoundedness versus the Fuzzy Regression Discontinuity Further Issues Special Considerations for Responses with Discreteness or Limited Range Multivalued Treatments Multiple Treatments Panel Data 968 Problems Duration Analysis Introduction Hazard Functions Hazard Functions without Covariates Hazard Functions Conditional on TimeInvariant Covariates Hazard Functions Conditional on TimeVarying Covariates Analysis of SingleSpell Data with TimeInvariant Covariates Flow Sampling Maximum Likelihood Estimation with Censored Flow Data Stock Sampling Unobserved Heterogeneity Analysis of Grouped Duration Data TimeInvariant Covariates TimeVarying Covariates Unobserved Heterogeneity 1017
16 xix 22.5 Further Issues Cox's Partial Likelihood Method for the Proportional Hazard Model MultipleSpell Data Competing Risks Models Problems References Index
Analysis of Microdata
Rainer Winkelmann Stefan Boes Analysis of Microdata With 38 Figures and 41 Tables 4y Springer Contents 1 Introduction 1 1.1 What Are Microdata? 1 1.2 Types of Microdata 4 1.2.1 Qualitative Data 4 1.2.2
More informationEconometric Analysis of Cross Section and Panel Data
Econometric Analysis of Cross Section and Panel Data Je rey M. Wooldridge The MIT Press Cambridge, Massachusetts London, England ( 2002 Massachusetts Institute of Technology All rights reserved. No part
More informationC: LEVEL 800 {MASTERS OF ECONOMICS( ECONOMETRICS)}
C: LEVEL 800 {MASTERS OF ECONOMICS( ECONOMETRICS)} 1. EES 800: Econometrics I Simple linear regression and correlation analysis. Specification and estimation of a regression model. Interpretation of regression
More informationWhat s New in Econometrics? Lecture 8 Cluster and Stratified Sampling
What s New in Econometrics? Lecture 8 Cluster and Stratified Sampling Jeff Wooldridge NBER Summer Institute, 2007 1. The Linear Model with Cluster Effects 2. Estimation with a Small Number of Groups and
More informationRegression Modeling Strategies
Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions
More informationHURDLE AND SELECTION MODELS Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics July 2009
HURDLE AND SELECTION MODELS Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics July 2009 1. Introduction 2. A General Formulation 3. Truncated Normal Hurdle Model 4. Lognormal
More information1. LinearinParameters Models: IV versus Control Functions
What s New in Econometrics? NBER, Summer 2007 Lecture 6, Tuesday, July 31st, 9.0010.30 am Control Function and Related Methods These notes review the control function approach to handling endogeneity
More informationSimultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the
Simultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the explanatory variables is jointly determined with the dependent
More informationMicroeconometrics Blundell Lecture 1 Overview and Binary Response Models
Microeconometrics Blundell Lecture 1 Overview and Binary Response Models Richard Blundell http://www.ucl.ac.uk/~uctp39a/ University College London FebruaryMarch 2016 Blundell (University College London)
More informationFrom the help desk: Bootstrapped standard errors
The Stata Journal (2003) 3, Number 1, pp. 71 80 From the help desk: Bootstrapped standard errors Weihua Guan Stata Corporation Abstract. Bootstrapping is a nonparametric approach for evaluating the distribution
More informationWhat s New in Econometrics? Lecture 10 DifferenceinDifferences Estimation
What s New in Econometrics? Lecture 10 DifferenceinDifferences Estimation Jeff Wooldridge NBER Summer Institute, 2007 1. Review of the Basic Methodology 2. How Should We View Uncertainty in DD Settings?
More informationSYSTEMS OF REGRESSION EQUATIONS
SYSTEMS OF REGRESSION EQUATIONS 1. MULTIPLE EQUATIONS y nt = x nt n + u nt, n = 1,...,N, t = 1,...,T, x nt is 1 k, and n is k 1. This is a version of the standard regression model where the observations
More informationCorrelated Random Effects Panel Data Models
INTRODUCTION AND LINEAR MODELS Correlated Random Effects Panel Data Models IZA Summer School in Labor Economics May 1319, 2013 Jeffrey M. Wooldridge Michigan State University 1. Introduction 2. The Linear
More informationECON 523 Applied Econometrics I /Masters Level American University, Spring 2008. Description of the course
ECON 523 Applied Econometrics I /Masters Level American University, Spring 2008 Instructor: Maria Heracleous Lectures: M 8:1010:40 p.m. WARD 202 Office: 221 Roper Phone: 2028853758 Office Hours: M W
More informationINTRODUCTORY STATISTICS
INTRODUCTORY STATISTICS FIFTH EDITION Thomas H. Wonnacott University of Western Ontario Ronald J. Wonnacott University of Western Ontario WILEY JOHN WILEY & SONS New York Chichester Brisbane Toronto Singapore
More informationFIXED EFFECTS AND RELATED ESTIMATORS FOR CORRELATED RANDOM COEFFICIENT AND TREATMENT EFFECT PANEL DATA MODELS
FIXED EFFECTS AND RELATED ESTIMATORS FOR CORRELATED RANDOM COEFFICIENT AND TREATMENT EFFECT PANEL DATA MODELS Jeffrey M. Wooldridge Department of Economics Michigan State University East Lansing, MI 488241038
More informationON THE ROBUSTNESS OF FIXED EFFECTS AND RELATED ESTIMATORS IN CORRELATED RANDOM COEFFICIENT PANEL DATA MODELS
ON THE ROBUSTNESS OF FIXED EFFECTS AND RELATED ESTIMATORS IN CORRELATED RANDOM COEFFICIENT PANEL DATA MODELS Jeffrey M. Wooldridge THE INSTITUTE FOR FISCAL STUDIES DEPARTMENT OF ECONOMICS, UCL cemmap working
More informationInstrumental Variables & 2SLS
Instrumental Variables & 2SLS y 1 = β 0 + β 1 y 2 + β 2 z 1 +... β k z k + u y 2 = π 0 + π 1 z k+1 + π 2 z 1 +... π k z k + v Economics 20  Prof. Schuetze 1 Why Use Instrumental Variables? Instrumental
More informationfifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson
fifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson Contents What Are These Demos About? How to Use These Demos If This Is Your First Time Using Fathom Tutorial: An Extended Example
More informationInstrumental Variables Regression. Instrumental Variables (IV) estimation is used when the model has endogenous s.
Instrumental Variables Regression Instrumental Variables (IV) estimation is used when the model has endogenous s. IV can thus be used to address the following important threats to internal validity: Omitted
More informationImplementations of tests on the exogeneity of selected. variables and their Performance in practice ACADEMISCH PROEFSCHRIFT
Implementations of tests on the exogeneity of selected variables and their Performance in practice ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag
More informationPractical. I conometrics. data collection, analysis, and application. Christiana E. Hilmer. Michael J. Hilmer San Diego State University
Practical I conometrics data collection, analysis, and application Christiana E. Hilmer Michael J. Hilmer San Diego State University Mi Table of Contents PART ONE THE BASICS 1 Chapter 1 An Introduction
More informationWooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares
Wooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares Many economic models involve endogeneity: that is, a theoretical relationship does not fit
More informationReview of Econometric Theory and Methods. Russell Davidson and James G. MacKinnon. Oxford University Press, Reviewed by Richard Startz *
Review of Econometric Theory and Methods Russell Davidson and James G. MacKinnon Oxford University Press, 2004 Reviewed by Richard Startz * University of Washington revised September 2004 * Address correspondence
More informationESTIMATING AVERAGE TREATMENT EFFECTS: IV AND CONTROL FUNCTIONS, II Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics
ESTIMATING AVERAGE TREATMENT EFFECTS: IV AND CONTROL FUNCTIONS, II Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics July 2009 1. Quantile Treatment Effects 2. Control Functions
More informationEmployerProvided Health Insurance and Labor Supply of Married Women
Upjohn Institute Working Papers Upjohn Research home page 2011 EmployerProvided Health Insurance and Labor Supply of Married Women Merve Cebi University of Massachusetts  Dartmouth and W.E. Upjohn Institute
More informationComparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors
Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors Arthur Lewbel, Yingying Dong, and Thomas Tao Yang Boston College, University of California Irvine, and Boston
More informationAdvanced Econometrics JEM005 : Course Syllabus
Advanced Econometrics JEM005 : Course Syllabus Fall 2013/2014 Course Supervisor: Jozef Baruník ( barunik at fsv.cuni.cz ) Lecturer: Jozef Baruník Lectures: Wednesday 2:00 PM  3:20 PM, room 109 (23.10.
More informationChapter 10: Basic Linear Unobserved Effects Panel Data. Models:
Chapter 10: Basic Linear Unobserved Effects Panel Data Models: Microeconomic Econometrics I Spring 2010 10.1 Motivation: The Omitted Variables Problem We are interested in the partial effects of the observable
More informationInstitute of Actuaries of India Subject CT3 Probability and Mathematical Statistics
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
More informationRegression with a Binary Dependent Variable
Regression with a Binary Dependent Variable Chapter 9 Michael Ash CPPA Lecture 22 Course Notes Endgame Takehome final Distributed Friday 19 May Due Tuesday 23 May (Paper or emailed PDF ok; no Word, Excel,
More information1. THE LINEAR MODEL WITH CLUSTER EFFECTS
What s New in Econometrics? NBER, Summer 2007 Lecture 8, Tuesday, July 31st, 2.003.00 pm Cluster and Stratified Sampling These notes consider estimation and inference with cluster samples and samples
More informationStatistics Graduate Courses
Statistics Graduate Courses STAT 7002Topics in StatisticsBiological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.
More informationMULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS
MULTIPLE REGRESSION AND ISSUES IN REGRESSION ANALYSIS MSR = Mean Regression Sum of Squares MSE = Mean Squared Error RSS = Regression Sum of Squares SSE = Sum of Squared Errors/Residuals α = Level of Significance
More information1. When Can Missing Data be Ignored?
What s ew in Econometrics? BER, Summer 2007 Lecture 12, Wednesday, August 1st, 1112.30 pm Missing Data These notes discuss various aspects of missing data in both pure cross section and panel data settings.
More informationCHAPTER 12 EXAMPLES: MONTE CARLO SIMULATION STUDIES
Examples: Monte Carlo Simulation Studies CHAPTER 12 EXAMPLES: MONTE CARLO SIMULATION STUDIES Monte Carlo simulation studies are often used for methodological investigations of the performance of statistical
More informationComparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors
Comparing Features of Convenient Estimators for Binary Choice Models With Endogenous Regressors Arthur Lewbel, Yingying Dong, and Thomas Tao Yang Boston College, University of California Irvine, and Boston
More informationMATHEMATICAL METHODS OF STATISTICS
MATHEMATICAL METHODS OF STATISTICS By HARALD CRAMER TROFESSOK IN THE UNIVERSITY OF STOCKHOLM Princeton PRINCETON UNIVERSITY PRESS 1946 TABLE OF CONTENTS. First Part. MATHEMATICAL INTRODUCTION. CHAPTERS
More informationEfficient and Practical Econometric Methods for the SLID, NLSCY, NPHS
Efficient and Practical Econometric Methods for the SLID, NLSCY, NPHS Philip Merrigan ESGUQAM, CIRPÉE Using Big Data to Study Development and Social Change, Concordia University, November 2103 Intro Longitudinal
More informationCHAPTER 6 EXAMPLES: GROWTH MODELING AND SURVIVAL ANALYSIS
Examples: Growth Modeling And Survival Analysis CHAPTER 6 EXAMPLES: GROWTH MODELING AND SURVIVAL ANALYSIS Growth models examine the development of individuals on one or more outcome variables over time.
More informationINDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition)
INDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition) Abstract Indirect inference is a simulationbased method for estimating the parameters of economic models. Its
More informationInstrumental Variables & 2SLS
Instrumental Variables & 2SLS y 1 = β 0 + β 1 y 2 + β 2 z 1 +... β k z k + u y 2 = π 0 + π 1 z k+1 + π 2 z 1 +... π k z k + v Economics 20  Prof. Schuetze 1 Why Use Instrumental Variables? Instrumental
More informationApplied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences Third Edition Jacob Cohen (deceased) New York University Patricia Cohen New York State Psychiatric Institute and Columbia University
More informationCURRICULUM VITAE. September Robert M. de Jong Tel: (614) Ohio State University Fax: (614)
CURRICULUM VITAE September 2013 Robert M. de Jong Tel: (614) 2922051 Ohio State University Fax: (614) 2923906 Department of Economics Email: dejong.8@osu.edu 444 Arps Hall Columbus, Ohio 43210, USA
More informationOn Marginal Effects in Semiparametric Censored Regression Models
On Marginal Effects in Semiparametric Censored Regression Models Bo E. Honoré September 3, 2008 Introduction It is often argued that estimation of semiparametric censored regression models such as the
More informationRegression for nonnegative skewed dependent variables
Regression for nonnegative skewed dependent variables July 15, 2010 Problem Solutions Link to OLS Alternatives Introduction Nonnegative skewed outcomes y, e.g. labor earnings medical expenditures trade
More informationTESTING THE ONEPART FRACTIONAL RESPONSE MODEL AGAINST AN ALTERNATIVE TWOPART MODEL
TESTING THE ONEPART FRACTIONAL RESPONSE MODEL AGAINST AN ALTERNATIVE TWOPART MODEL HARALD OBERHOFER AND MICHAEL PFAFFERMAYR WORKING PAPER NO. 201101 Testing the OnePart Fractional Response Model against
More informationClustering in the Linear Model
Short Guides to Microeconometrics Fall 2014 Kurt Schmidheiny Universität Basel Clustering in the Linear Model 2 1 Introduction Clustering in the Linear Model This handout extends the handout on The Multiple
More informationPS 271B: Quantitative Methods II. Lecture Notes
PS 271B: Quantitative Methods II Lecture Notes Langche Zeng zeng@ucsd.edu The Empirical Research Process; Fundamental Methodological Issues 2 Theory; Data; Models/model selection; Estimation; Inference.
More informationCHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS
Examples: Regression And Path Analysis CHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS Regression analysis with univariate or multivariate dependent variables is a standard procedure for modeling relationships
More informationCHAPTER 8 EXAMPLES: MIXTURE MODELING WITH LONGITUDINAL DATA
Examples: Mixture Modeling With Longitudinal Data CHAPTER 8 EXAMPLES: MIXTURE MODELING WITH LONGITUDINAL DATA Mixture modeling refers to modeling with categorical latent variables that represent subpopulations
More informationEconometrics Simple Linear Regression
Econometrics Simple Linear Regression Burcu Eke UC3M Linear equations with one variable Recall what a linear equation is: y = b 0 + b 1 x is a linear equation with one variable, or equivalently, a straight
More informationOverview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written
More informationUnivariate and Multivariate Methods PEARSON. Addison Wesley
Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston
More informationWHITNEY K. NEWEY August 2015
WHITNEY K. NEWEY August 2015 PERSONAL DATA Address: Department of Economics Massachusetts Institute of Technology Cambridge, MA 02139 Telephone: (617) 2536420, Fax: (617) 2531330, Email: wnewey@mit.edu.
More informationWhen to Use Which Statistical Test
When to Use Which Statistical Test Rachel Lovell, Ph.D., Senior Research Associate Begun Center for Violence Prevention Research and Education Jack, Joseph, and Morton Mandel School of Applied Social Sciences
More informationPanel Data Models. Chapter 5. Financial Econometrics. Michael Hauser WS15/16
1 / 63 Panel Data Models Chapter 5 Financial Econometrics Michael Hauser WS15/16 2 / 63 Content Data structures: Times series, cross sectional, panel data, pooled data Static linear panel data models:
More informationEconometric analysis of the Belgian car market
Econometric analysis of the Belgian car market By: Prof. dr. D. Czarnitzki/ Ms. Céline Arts Tim Verheyden Introduction In contrast to typical examples from microeconomics textbooks on homogeneous goods
More informationESTIMATING NONLINEAR CROSS SECTION AND PANEL DATA MODELS WITH ENDOGENEITY AND HETEROGENEITY. Hoa Bao Nguyen
ESTIMATING NONLINEAR CROSS SECTION AND PANEL DATA MODELS WITH ENDOGENEITY AND HETEROGENEITY by Hoa Bao Nguyen A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements
More informationSampling Theory for Discrete Data
Sampling Theory for Discrete Data * Economic survey data are often obtained from sampling protocols that involve stratification, censoring, or selection. Econometric estimators designed for random samples
More informationRegression Analysis Using ArcMap. By Jennie Murack
Regression Analysis Using ArcMap By Jennie Murack Regression Basics How is Regression Different from other Spatial Statistical Analyses? With other tools you ask WHERE something is happening? Are there
More informationANNUITY LAPSE RATE MODELING: TOBIT OR NOT TOBIT? 1. INTRODUCTION
ANNUITY LAPSE RATE MODELING: TOBIT OR NOT TOBIT? SAMUEL H. COX AND YIJIA LIN ABSTRACT. We devise an approach, using tobit models for modeling annuity lapse rates. The approach is based on data provided
More informationPlease follow the directions once you locate the Stata software in your computer. Room 114 (Business Lab) has computers with Stata software
STATA Tutorial Professor Erdinç Please follow the directions once you locate the Stata software in your computer. Room 114 (Business Lab) has computers with Stata software 1.Wald Test Wald Test is used
More informationPANEL DATA METHODS FOR FRACTIONAL RESPONSE VARIABLES WITH AN APPLICATION TO TEST PASS RATES
PANEL DATA METHODS FOR FRACTIONAL RESPONSE VARIABLES WITH AN APPLICATION TO TEST PASS RATES Leslie E. Papke Department of Economics Michigan State University East Lansing, MI 488241038 papke@msu.edu Jeffrey
More informationService courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.
Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are
More informationMultivariate Statistical Inference and Applications
Multivariate Statistical Inference and Applications ALVIN C. RENCHER Department of Statistics Brigham Young University A WileyInterscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim
More informationApplied Multivariate Analysis
Neil H. Timm Applied Multivariate Analysis With 42 Figures Springer Contents Preface Acknowledgments List of Tables List of Figures vii ix xix xxiii 1 Introduction 1 1.1 Overview 1 1.2 Multivariate Models
More informationSAS Software to Fit the Generalized Linear Model
SAS Software to Fit the Generalized Linear Model Gordon Johnston, SAS Institute Inc., Cary, NC Abstract In recent years, the class of generalized linear models has gained popularity as a statistical modeling
More informationWooldridge, Introductory Econometrics, 3d ed. Chapter 12: Serial correlation and heteroskedasticity in time series regressions
Wooldridge, Introductory Econometrics, 3d ed. Chapter 12: Serial correlation and heteroskedasticity in time series regressions What will happen if we violate the assumption that the errors are not serially
More informationBayesX  Software for Bayesian Inference in Structured Additive Regression
BayesX  Software for Bayesian Inference in Structured Additive Regression Thomas Kneib Faculty of Mathematics and Economics, University of Ulm Department of Statistics, LudwigMaximiliansUniversity Munich
More informationFULLY MODIFIED OLS FOR HETEROGENEOUS COINTEGRATED PANELS
FULLY MODIFIED OLS FOR HEEROGENEOUS COINEGRAED PANELS Peter Pedroni ABSRAC his chapter uses fully modified OLS principles to develop new methods for estimating and testing hypotheses for cointegrating
More informationUsing instrumental variables techniques in economics and finance
Using instrumental variables techniques in economics and finance Christopher F Baum 1 Boston College and DIW Berlin German Stata Users Group Meeting, Berlin, June 2008 1 Thanks to Mark Schaffer for a number
More informationThe University of British Columbia Faculty of Forestry Forestry Advanced Regression Analysis Course Outline for 2011
The University of British Columbia Faculty of Forestry Forestry 530  Advanced Regression Analysis Course Outline for 2011 Calendar Description: FRST 530 (3) Multiple Regression Methods. Matrix algebra;
More informationEconometrics of Survey Data
Econometrics of Survey Data Manuel Arellano CEMFI September 2014 1 Introduction Suppose that a population is stratified by groups to guarantee that there will be enough observations to permit suffi ciently
More informationCOURSES: 1. Short Course in Econometrics for the Practitioner (P000500) 2. Short Course in Econometric Analysis of Cointegration (P000537)
Get the latest knowledge from leading global experts. Financial Science Economics Economics Short Courses Presented by the Department of Economics, University of Pretoria WITH 2015 DATES www.ce.up.ac.za
More informationMaster programme in Statistics
Master programme in Statistics Björn Holmquist 1 1 Department of Statistics Lund University Cramérsällskapets årskonferens, 20100325 Master programme Vad är ett Master programme? Breddmaster vs Djupmaster
More informationEstimating the effect of state dependence in workrelated training participation among British employees. Panos Sousounis
Estimating the effect of state dependence in workrelated training participation among British employees. Panos Sousounis University of the West of England Abstract Despite the extensive empirical literature
More informationOnline Appendices to the Corporate Propensity to Save
Online Appendices to the Corporate Propensity to Save Appendix A: Monte Carlo Experiments In order to allay skepticism of empirical results that have been produced by unusual estimators on fairly small
More informationIntroduction to Regression and Data Analysis
Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it
More informationDATA ANALYTICS USING R
DATA ANALYTICS USING R Duration: 90 Hours Intended audience and scope: The course is targeted at fresh engineers, practicing engineers and scientists who are interested in learning and understanding data
More information1. Review of the Basic Methodology
What s New in Econometrics? NBER, Summer 2007 Lecture 10, Tuesday, July 31st, 4.305.30 pm DifferenceinDifferences Estimation These notes provide an overview of standard differenceindifferences methods
More informationRegression analysis in practice with GRETL
Regression analysis in practice with GRETL Prerequisites You will need the GNU econometrics software GRETL installed on your computer (http://gretl.sourceforge.net/), together with the sample files that
More informationSolución del Examen Tipo: 1
Solución del Examen Tipo: 1 Universidad Carlos III de Madrid ECONOMETRICS Academic year 2009/10 FINAL EXAM May 17, 2010 DURATION: 2 HOURS 1. Assume that model (III) verifies the assumptions of the classical
More informationStructural Equation Modeling
Structural Equation Modeling Kosuke Imai Princeton University POL572 Quantitative Analysis II Spring 2016 Kosuke Imai (Princeton) Structural Equation Modeling POL572 Spring 2016 1 / 39 Causal Mediation
More informationChapter 2. Dynamic panel data models
Chapter 2. Dynamic panel data models Master of Science in Economics  University of Geneva Christophe Hurlin, Université d Orléans Université d Orléans April 2010 Introduction De nition We now consider
More informationRobust Inferences from Random Clustered Samples: Applications Using Data from the Panel Survey of Income Dynamics
Robust Inferences from Random Clustered Samples: Applications Using Data from the Panel Survey of Income Dynamics John Pepper Assistant Professor Department of Economics University of Virginia 114 Rouss
More informationLongitudinal and Panel Data
Longitudinal and Panel Data Analysis and Applications in the Social Sciences EDWARD W. FREES University of Wisconsin Madison PUBLISHED BY THE PRESS SYNDICATE OF THE UNIVERSITY OF CAMBRIDGE The Pitt Building,
More informationThe primary goal of this thesis was to understand how the spatial dependence of
5 General discussion 5.1 Introduction The primary goal of this thesis was to understand how the spatial dependence of consumer attitudes can be modeled, what additional benefits the recovering of spatial
More informationClass 6: Chapter 12. Key Ideas. Explanatory Design. Correlational Designs
Class 6: Chapter 12 Correlational Designs l 1 Key Ideas Explanatory and predictor designs Characteristics of correlational research Scatterplots and calculating associations Steps in conducting a correlational
More informationCentre for Central Banking Studies
Centre for Central Banking Studies Technical Handbook No. 4 Applied Bayesian econometrics for central bankers Andrew Blake and Haroon Mumtaz CCBS Technical Handbook No. 4 Applied Bayesian econometrics
More informationAPPLIED MISSING DATA ANALYSIS
APPLIED MISSING DATA ANALYSIS Craig K. Enders Series Editor's Note by Todd D. little THE GUILFORD PRESS New York London Contents 1 An Introduction to Missing Data 1 1.1 Introduction 1 1.2 Chapter Overview
More informationIAPRI Quantitative Analysis Capacity Building Series. Multiple regression analysis & interpreting results
IAPRI Quantitative Analysis Capacity Building Series Multiple regression analysis & interpreting results How important is Rsquared? Rsquared Published in Agricultural Economics 0.45 Best article of the
More information2 Testing under Normality Assumption
1 Hypothesis testing A statistical test is a method of making a decision about one hypothesis (the null hypothesis in comparison with another one (the alternative using a sample of observations of known
More informationThe Bootstrap. 1 Introduction. The Bootstrap 2. Short Guides to Microeconometrics Fall Kurt Schmidheiny Unversität Basel
Short Guides to Microeconometrics Fall 2016 The Bootstrap Kurt Schmidheiny Unversität Basel The Bootstrap 2 1a) The asymptotic sampling distribution is very difficult to derive. 1b) The asymptotic sampling
More informationStandard errors of marginal effects in the heteroskedastic probit model
Standard errors of marginal effects in the heteroskedastic probit model Thomas Cornelißen Discussion Paper No. 320 August 2005 ISSN: 0949 9962 Abstract In nonlinear regression models, such as the heteroskedastic
More informationIntroduction to Regression Models for Panel Data Analysis. Indiana University Workshop in Methods October 7, 2011. Professor Patricia A.
Introduction to Regression Models for Panel Data Analysis Indiana University Workshop in Methods October 7, 2011 Professor Patricia A. McManus Panel Data Analysis October 2011 What are Panel Data? Panel
More informationGraduate Programs in Statistics
Graduate Programs in Statistics Course Titles STAT 100 CALCULUS AND MATR IX ALGEBRA FOR STATISTICS. Differential and integral calculus; infinite series; matrix algebra STAT 195 INTRODUCTION TO MATHEMATICAL
More informationTypes of Specification Errors 1. Omitted Variables. 2. Including an irrelevant variable. 3. Incorrect functional form. 2
Notes on Model Specification To go with Gujarati, Basic Econometrics, Chapter 13 Copyright  Jonathan Nagler; April 4, 1999 Attributes of a Good Econometric Model A.C. Harvey: (in Gujarati, p. 4534) ffl
More informationproblem arises when only a nonrandom sample is available differs from censored regression model in that x i is also unobserved
4 Data Issues 4.1 Truncated Regression population model y i = x i β + ε i, ε i N(0, σ 2 ) given a random sample, {y i, x i } N i=1, then OLS is consistent and efficient problem arises when only a nonrandom
More informationPanel Data: Linear Models
Panel Data: Linear Models Laura Magazzini University of Verona laura.magazzini@univr.it http://dse.univr.it/magazzini Laura Magazzini (@univr.it) Panel Data: Linear Models 1 / 45 Introduction Outline What
More information