Econometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England
|
|
- Cuthbert Thornton
- 7 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 Single-Equation Linear Model and Ordinary Least Squares Estimation Overview of the Single-Equation Linear Model Asymptotic Properties of Ordinary Least Squares Consistency Asymptotic Inference Using Ordinary Least Squares Heteroskedasticity-Robust Inference Lagrange Multiplier (Score) Tests Ordinary Least Squares Solutions to the Omitted Variables Problem Ordinary Least Squares Ignoring the Omitted Variables Proxy Variable-Ordinary 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 Single-Equation Linear Models Instrumental Variables and Two-Stage Least Squares Motivation for Instrumental Variables Estimation Multiple Instruments: Two-Stage Least Squares General Treatment of Two-Stage Least Squares Consistency Asymptotic Normality of Two-Stage Least Squares Asymptotic Efficiency of Two-Stage Least Squares Hypothesis Testing with Two-Stage Least Squares Heteroskedasticity-Robust Inference for Two-Stage Least Squares Potential Pitfalls with Two-Stage 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 Single-Equation Topics Estimation with Generated Regressors and Instruments Ordinary Least Squares with Generated Regressors Two-Stage 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 Difference-in-Differences Estimation Pooled Cross Sections over Time Policy Analysis and Difference-in-Differences 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 Two-Stage Least Squares Estimator Optimal Weighting Matrix The Generalized Method of Moments Three-Stage 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 Three-Stage 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 Robustness-Efficiency Trade-off 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 Taylor-Type 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 Individual-Specific Slopes Random Trend Model General Models with Individual-Specific Slopes Robustness of Standard Fixed Effects Methods Testing for Correlated Random Slopes 384 Problems 387 III GENERAL APPROACHES TO NONLINEAR ESTIMATION M-Estimation, Nonlinear Regression, and Quantile Regression Introduction Identification, Uniform Convergence, and Consistency Asymptotic Normality Two-Step M-Estimators Consistency Asymptotic Normality Estimating the Asymptotic Variance Estimation without Nuisance Parameters Adjustments for Two-Step 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 Newton-Raphson Method Berndt, Hall, Hall, and Hausman Algorithm Generalized Gauss-Newton 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 Two-Step Estimators Involving Maximum Likelihood Second-Step Estimator Is Maximum Likelihood Estimator Surprising Efficiency Result When the First-Step Estimator Is Conditional Maximum Likelihood Estimator Quasi-Maximum Likelihood Estimation General Misspecification Model Selection Tests Quasi-Maximum 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 Time-Varying 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 Two-Part Models and Type II Tobit for Corner Solutions Truncated Normal Hurdle Model Lognormal Hurdle Model and Exponential Conditional Mean Exponential Type II Tobit Model Two-Limit 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 Unit-Specific Panel Data Should We Apply Cluster-Robust 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 Self-Selection 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 Time-Invariant Covariates Hazard Functions Conditional on Time-Varying Covariates Analysis of Single-Spell Data with Time-Invariant Covariates Flow Sampling Maximum Likelihood Estimation with Censored Flow Data Stock Sampling Unobserved Heterogeneity Analysis of Grouped Duration Data Time-Invariant Covariates Time-Varying Covariates Unobserved Heterogeneity 1017
16 xix 22.5 Further Issues Cox's Partial Likelihood Method for the Proportional Hazard Model Multiple-Spell Data Competing Risks Models Problems References Index
Econometric 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 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 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 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 48824-1038
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 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 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 8:10-10:40 p.m. WARD 202 Office: 221 Roper Phone: 202-885-3758 Office Hours: M W
More informationCorrelated Random Effects Panel Data Models
INTRODUCTION AND LINEAR MODELS Correlated Random Effects Panel Data Models IZA Summer School in Labor Economics May 13-19, 2013 Jeffrey M. Wooldridge Michigan State University 1. Introduction 2. The Linear
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 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 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 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 informationEmployer-Provided Health Insurance and Labor Supply of Married Women
Upjohn Institute Working Papers Upjohn Research home page 2011 Employer-Provided Health Insurance and Labor Supply of Married Women Merve Cebi University of Massachusetts - Dartmouth and W.E. Upjohn Institute
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 Take-home final Distributed Friday 19 May Due Tuesday 23 May (Paper or emailed PDF ok; no Word, Excel,
More informationStatistics Graduate Courses
Statistics Graduate Courses STAT 7002--Topics in Statistics-Biological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.
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 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 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 informationEfficient and Practical Econometric Methods for the SLID, NLSCY, NPHS
Efficient and Practical Econometric Methods for the SLID, NLSCY, NPHS Philip Merrigan ESG-UQAM, CIRPÉE Using Big Data to Study Development and Social Change, Concordia University, November 2103 Intro Longitudinal
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 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 information1. THE LINEAR MODEL WITH CLUSTER EFFECTS
What s New in Econometrics? NBER, Summer 2007 Lecture 8, Tuesday, July 31st, 2.00-3.00 pm Cluster and Stratified Sampling These notes consider estimation and inference with cluster samples and samples
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 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 simulation-based method for estimating the parameters of economic models. Its
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 information1. When Can Missing Data be Ignored?
What s ew in Econometrics? BER, Summer 2007 Lecture 12, Wednesday, August 1st, 11-12.30 pm Missing Data These notes discuss various aspects of missing data in both pure cross section and panel data settings.
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 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 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 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 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 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 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 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 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 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 informationMultivariate Statistical Inference and Applications
Multivariate Statistical Inference and Applications ALVIN C. RENCHER Department of Statistics Brigham Young University A Wiley-Interscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim
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 informationHow To Understand Multivariate Models
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 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, Ludwig-Maximilians-University Munich
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 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 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 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 informationMaster programme in Statistics
Master programme in Statistics Björn Holmquist 1 1 Department of Statistics Lund University Cramérsällskapets årskonferens, 2010-03-25 Master programme Vad är ett Master programme? Breddmaster vs Djupmaster
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 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 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 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 informationMortgage Loan Approvals and Government Intervention Policy
Mortgage Loan Approvals and Government Intervention Policy Dr. William Chow 18 March, 214 Executive Summary This paper introduces an empirical framework to explore the impact of the government s various
More informationEstimating the effect of state dependence in work-related training participation among British employees. Panos Sousounis
Estimating the effect of state dependence in work-related training participation among British employees. Panos Sousounis University of the West of England Abstract Despite the extensive empirical literature
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 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 R-squared? R-squared Published in Agricultural Economics 0.45 Best article of the
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 informationMarketing Mix Modelling and Big Data P. M Cain
1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored
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 informationMODELS FOR PANEL DATA Q
Greene-2140242 book November 23, 2010 12:28 11 MODELS FOR PANEL DATA Q 11.1 INTRODUCTION Data sets that combine time series and cross sections are common in economics. The published statistics of the OECD
More informationproblem arises when only a non-random 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 non-random
More informationHow To Understand The Theory Of Probability
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 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 information1. Review of the Basic Methodology
What s New in Econometrics? NBER, Summer 2007 Lecture 10, Tuesday, July 31st, 4.30-5.30 pm Difference-in-Differences Estimation These notes provide an overview of standard difference-in-differences methods
More informationHandling attrition and non-response in longitudinal data
Longitudinal and Life Course Studies 2009 Volume 1 Issue 1 Pp 63-72 Handling attrition and non-response in longitudinal data Harvey Goldstein University of Bristol Correspondence. Professor H. Goldstein
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 non-linear regression models, such as the heteroskedastic
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 informationA course on Longitudinal data analysis : what did we learn? COMPASS 11/03/2011
A course on Longitudinal data analysis : what did we learn? COMPASS 11/03/2011 1 Abstract We report back on methods used for the analysis of longitudinal data covered by the course We draw lessons for
More informationMortgage Lending Discrimination and Racial Differences in Loan Default
Mortgage Lending Discrimination and Racial Differences in Loan Default 117 Journal of Housing Research Volume 7, Issue 1 117 Fannie Mae Foundation 1996. All Rights Reserved. Mortgage Lending Discrimination
More informationAuxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
More informationPoisson Models for Count Data
Chapter 4 Poisson Models for Count Data In this chapter we study log-linear models for count data under the assumption of a Poisson error structure. These models have many applications, not only to the
More informationModeling and Analysis of Call Center Arrival Data: A Bayesian Approach
Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach Refik Soyer * Department of Management Science The George Washington University M. Murat Tarimcilar Department of Management Science
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 informationNote 2 to Computer class: Standard mis-specification tests
Note 2 to Computer class: Standard mis-specification tests Ragnar Nymoen September 2, 2013 1 Why mis-specification testing of econometric models? As econometricians we must relate to the fact that the
More informationThe impact of risk management standards on the frequency of MRSA infections in NHS hospitals 1
The impact of risk management standards on the frequency of MRSA infections in NHS hospitals 1 Paul Fenn Nottingham University Business School Alastair Gray Health Economics Research Centre, University
More informationAccounting for Time-Varying Unobserved Ability Heterogeneity within Education Production Functions
Accounting for Time-Varying Unobserved Ability Heterogeneity within Education Production Functions Weili Ding Queen s University Steven F. Lehrer Queen s University and NBER July 2008 Abstract Traditional
More informationMultivariate Analysis of Health Survey Data
10 Multivariate Analysis of Health Survey Data The most basic description of health sector inequality is given by the bivariate relationship between a health variable and some indicator of socioeconomic
More informationElements of statistics (MATH0487-1)
Elements of statistics (MATH0487-1) Prof. Dr. Dr. K. Van Steen University of Liège, Belgium December 10, 2012 Introduction to Statistics Basic Probability Revisited Sampling Exploratory Data Analysis -
More informationOrder Statistics: Theory & Methods. N. Balakrishnan Department of Mathematics and Statistics McMaster University Hamilton, Ontario, Canada. C. R.
Order Statistics: Theory & Methods Edited by N. Balakrishnan Department of Mathematics and Statistics McMaster University Hamilton, Ontario, Canada C. R. Rao Center for Multivariate Analysis Department
More informationDepartment of Economics Session 2012/2013. EC352 Econometric Methods. Solutions to Exercises from Week 10 + 0.0077 (0.052)
Department of Economics Session 2012/2013 University of Essex Spring Term Dr Gordon Kemp EC352 Econometric Methods Solutions to Exercises from Week 10 1 Problem 13.7 This exercise refers back to Equation
More informationCIDE XXIV Corso Residenziale di Econometria
CIDE XXIV Corso Residenziale di Econometria 8-13 Settembre 2014 - Palermo Topics in Microeconometrics with Applications to Energy & Environmental Economics General Information Students requiring accommodation
More informationModelling Irregularly Spaced Financial Data
Nikolaus Hautsch Modelling Irregularly Spaced Financial Data Theory and Practice of Dynamic Duration Models Springer C Contents Introduction 1 Point Processes 9 2.1 Basic Concepts of Point Processes 9
More informationMultivariate analysis of health data: General issues
Multivariate analysis of health data: General issues Introduction The most basic description of health sector inequality is given by the bivariate relationship between a health variable and some measure
More informationIntroduction to Financial Models for Management and Planning
CHAPMAN &HALL/CRC FINANCE SERIES Introduction to Financial Models for Management and Planning James R. Morris University of Colorado, Denver U. S. A. John P. Daley University of Colorado, Denver U. S.
More informationCHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA
Examples: Multilevel Modeling With Complex Survey Data CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA Complex survey data refers to data obtained by stratification, cluster sampling and/or
More informationA Subset-Continuous-Updating Transformation on GMM Estimators for Dynamic Panel Data Models
Article A Subset-Continuous-Updating Transformation on GMM Estimators for Dynamic Panel Data Models Richard A. Ashley 1, and Xiaojin Sun 2,, 1 Department of Economics, Virginia Tech, Blacksburg, VA 24060;
More informationWeak instruments: An overview and new techniques
Weak instruments: An overview and new techniques July 24, 2006 Overview of IV IV Methods and Formulae IV Assumptions and Problems Why Use IV? Instrumental variables, often abbreviated IV, refers to an
More informationModelling spousal mortality dependence: evidence of heterogeneities and implications
1/23 Modelling spousal mortality dependence: evidence of heterogeneities and implications Yang Lu Scor and Aix-Marseille School of Economics Lyon, September 2015 2/23 INTRODUCTION 3/23 Motivation It has
More informationOnline Appendix The Earnings Returns to Graduating with Honors - Evidence from Law Graduates
Online Appendix The Earnings Returns to Graduating with Honors - Evidence from Law Graduates Ronny Freier Mathias Schumann Thomas Siedler February 13, 2015 DIW Berlin and Free University Berlin. Universität
More informationEconomics of Education Review
Economics of Education Review 31 (2012) 19 32 Contents lists available at ScienceDirect Economics of Education Review jou rn al h om epa ge: www. elsevier.com/locate/econedurev Observed characteristics
More informationEconometric Methods for Panel Data
Based on the books by Baltagi: Econometric Analysis of Panel Data and by Hsiao: Analysis of Panel Data Robert M. Kunst robert.kunst@univie.ac.at University of Vienna and Institute for Advanced Studies
More informationProbability and Statistics
Probability and Statistics Syllabus for the TEMPUS SEE PhD Course (Podgorica, April 4 29, 2011) Franz Kappel 1 Institute for Mathematics and Scientific Computing University of Graz Žaneta Popeska 2 Faculty
More informationSTATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
More informationGLM I An Introduction to Generalized Linear Models
GLM I An Introduction to Generalized Linear Models CAS Ratemaking and Product Management Seminar March 2009 Presented by: Tanya D. Havlicek, Actuarial Assistant 0 ANTITRUST Notice The Casualty Actuarial
More informationFixed Effects Bias in Panel Data Estimators
DISCUSSION PAPER SERIES IZA DP No. 3487 Fixed Effects Bias in Panel Data Estimators Hielke Buddelmeyer Paul H. Jensen Umut Oguzoglu Elizabeth Webster May 2008 Forschungsinstitut zur Zukunft der Arbeit
More informationApplied Regression Analysis and Other Multivariable Methods
THIRD EDITION Applied Regression Analysis and Other Multivariable Methods David G. Kleinbaum Emory University Lawrence L. Kupper University of North Carolina, Chapel Hill Keith E. Muller University of
More information