木曜日, 3月 03, 2016

Econometric Analysis of Cross Section and Panel Data, 2nd Edition 2010

                       ( 経済学リンク::::::::::
計量経済学及びGMM
http://nam-students.blogspot.jp/2015/12/gmm.html
Econometric Analysis of Cross Section and Panel Data, 2nd Edition 2010
http://nam-students.blogspot.jp/2016/03/econometric-analysis-of-cross-section.html(本頁)
Introductory Econometrics: A Modern Approach, 6th Edition - Jeffrey M.Wooldridge - Cengage Learning - 2016
http://nam-students.blogspot.jp/2016/03/introductory-econometrics-modern.html


Econometric Analysis of Cross Section and Panel Data, 2nd Edition
http://mitpress.mit.edu/sites/default/files/titles/content/9780262232586_toc_0001.pdf 目次


形式: ハードカバー Amazonで購入
国内外の大学院のミクロ計量経済学の講義で使用されているテキスト。
Intoroductoryに続いて懇切丁寧でとてもわかり易い。
計量経済学は大学院レベルになると、漸近理論や線形代数の知識を多用することもあり一気に難しくなる。しかしWooldridgeは様々なモデルを直感的に非常にわかり易く解説している。
このテキストの秀逸な点として、実証への配慮がしっかりとされていることにある。ほとんどの計量経済学のテキストは理論解説に終止しており、モデルの有用性や使い方にはあまり触れられていない(日本語の計量経済学のテキストでは特に顕著であるように思う)。
Wooldridgeは各モデルの後に簡単な例、または有名な論文からの具体例が色々と詳しく載っているため、実証研究の勉強になり、モデル自体の理解も更に深まるはずである。
章末問題はWooldridgeのホームページにいけば、奇数番号のみ解答がある。結構良問が多く、理解を深めるためにも時間の許す限り解いた方がいいと思う。
色々つっこみ所はあるが、ミクロ計量、ミクロ実証をやる人は重宝する一冊。

最後に注意点をいくつか。
first editionに比べsecond editionの方が扱っているテーマが増え分量も約300ページ増えているため、ミクロ計量、ミクロ実証をする人は多少高くともsecond editionを購入した方が良い。
要求される最低限の数学レベルとしては、WooldridgeのIntoroductory の方のMathmatical Appendixくらい。 後は必要に応じて本書で学べば十分だと思う。
タイトルの通りだが、時系列分析についてはない。またnonparametric,semiparametricの話題もほとんどない(semiparaはcorner solutionモデルの所で軽く触れている程度)。
モデルは直感的理解に重きを置いていると思われ、数学的には多少frankなところがある。またベクトルのnotationなどは普通の計量経済学のテキストと異なり、始めは多少混乱するかもしれない。


以下、紀伊国屋書店HPより
 https://www.kinokuniya.co.jp/f/dsg-02-9780262232586

クロスセクション・データとパネル・データ:計量経済学的分析(第2版) Econometric Analysis of Cross Section and Panel Data (2ND)

 https://www.kinokuniya.co.jp/f/dsg-02-9780262232586
クロスセクション・データとパネル・データ:計量経済学的分析(第2版) Econometric Analysis of Cross Section and Panel Data (2ND)

    • Wooldridge, Jeffrey M.

Table of Contents(簡易目次)

Preface                                            xxi
Acknowledgments                                    xxix
  I INTRODUCTION AND BACKGROUND                    1   (50)
    2 Conditional Expectations and Related         13  (24)
    Concepts in Econometrics
    3 Basic Asymptotic Theory                      37  (14)
  II LINEAR MODELS                                 51  (344)
    4 Single-Equation Linear Model and Ordinary    53  (36)
    Least Squares Estimation
    5 Instrumental Variables Estimation of         89  (34)
    Single-Equation Linear Models
    6 Additional Single-Equation Topics            123 (38)
    7 Estimating Systems of Equations by           161 (46)
    Ordinary Least Squares and Generalized
    Least Squares
    8 System Estimation by Instrumental            207 (32)
    Variables
    9 Simultaneous Equations Models                239 (46)
    10 Basic Linear Unobserved Effects and         285 (60)
    Explanatory Variables
    11 More Topics in Linear Unobserved Effects    345 (50)
    Models
  III GENERAL APPROACHES TO NONLINEAR ESTIMATION   395 (164)
    12 M-Estimation, Nonlinear Regression, and     397 (72)
    Quantile Regression
    13 Maximum Likelihood Methods                  469 (56)
    14 Generalized Method of Moments and           525 (34)
    Minimum Distance Estimation
  IV NONLINEAR MODELS AND RELATED TOPICS           559 (466)
    15 Binary Response Models                      561 (82)
    16 Multinomial and Ordered Response Models     643 (24)
    17 Corner Solution Responses                   667 (56)
    18 Count, Fractional, and Other Nonnegative    723 (54)
    Responses
    19 Censored Data, Sample Selection, and        777 (76)
    Attrition
    20 Stratified Sampling and Cluster Sampling    853 (50)
    21 Estimating Average Treatment Effects        903 (80)
    22 Duration Analysis                           983 (42)
References                                         1025(20)
Index                                              1045

以下、詳細目次

Table of Contents

Preface                                            xxi
Acknowledgments                                    xxix
  I INTRODUCTION AND BACKGROUND                    1   (50)
    1 Introduction                                 3   (10)
      1.1 Causal Relationships and Ceteris         3   (1)
      Paribus Analysis
      1.2 Stochastic Setting and Asymptotic        4   (3)
      Analysis
        1.2.1 Data Structures                      4   (3)
        1.2.2 Asymptotic Analysis                  7   (1)
      1.3 Some Examples                            7   (2)
      1.4 Why Not Fixed Explanatory Variables?     9   (4)
    2 Conditional Expectations and Related         13  (24)
    Concepts in Econometrics
      2.1 Role of Conditional Expectations in      13  (1)
      Econometrics
      2.2 Features of Conditional Expectations     14  (11)
        2.2.1 Definition and Examples              14  (1)
        2.2.2 Partial Effects, Elasticities,       15  (3)
        and Semielasticities
        2.2.3 Error Form of Models of              18  (1)
        Conditional Expectations
        2.2.4 Some Properties of Conditional       19  (3)
        Expectations
        2.2.5 Average Partial Effects              22  (3)
      2.3 Linear Projections                       25  (12)
        Problems                                   27  (3)
        Appendix 2A                                30  (1)
        2.A.1 Properties of Conditional            30  (2)
        Expectations
        2.A.2 Properties of Conditional            32  (2)
        Variances and Covariances
        2.A.3 Properties of Linear Projections     34  (3)
    3 Basic Asymptotic Theory                      37  (14)
      3.1 Convergence of Deterministic Sequences   37  (1)
      3.2 Convergence in Probability and           38  (2)
      Boundedness in Probability
      3.3 Convergence in Distribution              40  (1)
      3.4 Limit Theorems for Random Samples        41  (1)
      3.5 Limiting Behavior of Estimators and      42  (9)
      Test Statistics
        3.5.1 Asymptotic Properties of             42  (3)
        Estimators
        3.5.2 Asymptotic Properties of Test        45  (2)
        Statistics
        Problems                                   47  (4)
  II LINEAR MODELS                                 51  (344)
    4 Single-Equation Linear Model and Ordinary    53  (36)
    Least Squares Estimation
      4.1 Overview of the Single-Equation          53  (2)
      Linear Model
      4.2 Asymptotic Properties of Ordinary        55  (10)
      Least Squares
        4.2.1 Consistency                          56  (3)
        4.2.2 Asymptotic Inference Using           59  (1)
        Ordinary Least Squares
        4.2.3 Heteroskedasticity-Robust            60  (2)
        Inference
        4.2.4 Lagrange Multiplier (Score) Tests    62  (3)
      4.3 Ordinary Least Squares Solutions to      65  (11)
      the Omitted Variables Problem
        4.3.1 Ordinary Least Squares Ignoring      65  (2)
        the Omitted Variables
        4.3.2 Proxy Variable-Ordinary Least        67  (6)
        Squares Solution
        4.3.3 Models with Interactions in          73  (3)
        Unobservables: Random Coefficient Models
      4.4 Properties of Ordinary Least Squares     76  (13)
      under Measurement Error
        4.4.1 Measurement Error in the             76  (2)
        Dependent Variable
        4.4.2 Measurement Error in an              78  (4)
        Explanatory Variable
        Problems                                   82  (7)
    5 Instrumental Variables Estimation of         89  (34)
    Single-Equation Linear Models
      5.1 Instrumental Variables and Two-Stage     89  (9)
      Least Squares
        5.1.1 Motivation for Instrumental          89  (7)
        Variables Estimation
        5.1.2 Multiple Instruments: Two-Stage      96  (2)
        Least Squares
      5.2 General Treatment of Two-Stage Least     98  (14)
      Squares
        5.2.1 Consistency                          98  (3)
        5.2.2 Asymptotic Normality of Two-Stage    101 (2)
        Least Squares
        5.2.3 Asymptotic Efficiency of             103 (1)
        Two-Stage Least Squares
        5.2.4 Hypothesis Testing with Two-Stage    104 (2)
        Least Squares
        5.2.5 Heteroskedasticity-Robust            106 (1)
        Inference for Two-Stage Least Squares
        5.2.6 Potential Pitfalls with Two-Stage    107 (5)
        Least Squares
      5.3 IV Solutions to the Omitted Variables    112 (11)
      and Measurement Error Problems
        5.3.1 Leaving the Omitted Factors in       112 (1)
        the Error Term
        5.3.2 Solutions Using Indicators of the    112 (3)
        Unobservables
        Problems                                   115 (8)
    6 Additional Single-Equation Topics            123 (38)
      6.1 Estimation with Generated Regressors     123 (3)
      and Instruments
        6.1.1 Ordinary Least Squares with          123 (1)
        Generated Regressors
        6.1.2 Two-Stage Least Squares with         124 (1)
        Generated Instruments
        6.1.3 Generated Instruments and            125 (1)
        Regressors
      6.2 Control Function Approach to             126 (3)
      Endogeneity
      6.3 Some Specification Tests                 129 (12)
        6.3.1 Testing for Endogeneity              129 (5)
        6.3.2 Testing Overidentifying              134 (3)
        Restrictions
        6.3.3 Testing Functional Form              137 (1)
        6.3.4 Testing for Heteroskedasticity       138 (3)
      6.4 Correlated Random Coefficient Models     141 (5)
        6.4.1 When Is the Usual IV Estimator       142 (3)
        Consistent?
        6.4.2 Control Function Approach            145 (1)
      6.5 Pooled Cross Sections and                146 (15)
      Difference-in-Differences Estimation
        6.5.1 Pooled Cross Sections over Time      146 (1)
        6.5.2 Policy Analysis and                  147 (5)
        Difference-in-Differences Estimation
        Problems                                   152 (5)
        Appendix 6A                                157 (4)
    7 Estimating Systems of Equations by           161 (46)
    Ordinary Least Squares and Generalized
    Least Squares
      7.1 Introduction                             161 (1)
      7.2 Some Examples                            161 (5)
      7.3 System Ordinary Least Squares            166 (7)
      Estimation of a Multivariate Linear System
        7.3.1 Preliminaries                        166 (1)
        7.3.2 Asymptotic Properties of System      167 (5)
        Ordinary Least Squares
        7.3.3 Testing Multiple Hypotheses          172 (1)
      7.4 Consistency and Asymptotic Normality     173 (3)
      of Generalized Least Squares
        7.4.1 Consistency                          173 (2)
        7.4.2 Asymptotic Normality                 175 (1)
      7.5 Feasible Generalized Least Squares       176 (7)
        7.5.1 Asymptotic Properties                176 (4)
        7.5.2 Asymptotic Variance of Feasible      180 (2)
        Generalized Least Squares under a
        Standard Assumption
        7.5.3 Properties of Feasible               182 (1)
        Generalized Least Squares with
        (Possibly Incorrect) Restrictions on
        the Unconditional Variance Matrix
      7.6 Testing the Use of Feasible              183 (2)
      Generalized Least Squares
      7.7 Seemingly Unrelated Regressions,         185 (6)
      Revisited
        7.7.1 Comparison between Ordinary Least    185 (3)
        Squares and Feasible Generalized Least
        Squares for Seemingly Unrelated
        Regressions Systems
        7.7.2 Systems with Cross Equation          188 (1)
        Restrictions
        7.7.3 Singular Variance Matrices in        189 (2)
        Seemingly Unrelated Regressions Systems
      7.8 Linear Panel Data Model, Revisited       191 (16)
        7.8.1 Assumptions for Pooled Ordinary      191 (3)
        Least Squares
        7.8.2 Dynamic Completeness                 194 (2)
        7.8.3 Note on Time Series Persistence      196 (1)
        7.8.4 Robust Asymptotic Variance Matrix    197 (1)
        7.8.5 Testing for Serial Correlation       198 (2)
        and Heteroskedasticity after Pooled
        Ordinary Least Squares
        7.8.6 Feasible Generalized Least           200 (2)
        Squares Estimation under Strict
        Exogeneity
        Problems                                   202 (5)
    8 System Estimation by Instrumental            207 (32)
    Variables
      8.1 Introduction and Examples                207 (3)
      8.2 General Linear System of Equations       210 (3)
      8.3 Generalized Method of Moments            213 (9)
      Estimation
        8.3.1 General Weighting Matrix             213 (3)
        8.3.2 System Two-Stage Least Squares       216 (1)
        Estimator
        8.3.3 Optimal Weighting Matrix             217 (2)
        8.3.4 The Generalized Method of Moments    219 (3)
        Three-Stage Least Squares Estimator
      8.4 Generalized Instrumental Variables       222 (4)
      Estimator
        8.4.1 Derivation of the Generalized        222 (2)
        Instrumental Variables Estimator and
        Its Asymptotic Properties
        8.4.2 Comparison of Generalized Method     224 (2)
        of Moment, Generalized Instrumental
        Variables, and the Traditional
        Three-Stage Least Squares Estimator
      8.5 Testing Using Generalized Method of      226 (3)
      Moments
        8.5.1 Testing Classical Hypotheses         226 (2)
        8.5.2 Testing Overidentification           228 (1)
        Restrictions
      8.6 More Efficient Estimation and Optimal    229 (3)
      Instruments
      8.7 Summary Comments on Choosing an          232 (7)
      Estimator
        Problems                                   233 (6)
    9 Simultaneous Equations Models                239 (46)
      9.1 Scope of Simultaneous Equations Models   239 (2)
      9.2 Identification in a Linear System        241 (11)
        9.2.1 Exclusion Restrictions and           241 (4)
        Reduced Forms
        9.2.2 General Linear Restrictions and      245 (6)
        Structural Equations
        9.2.3 Unidentified, Just Identified,       251 (1)
        and Overidentified Equations
      9.3 Estimation after Identification          252 (4)
        9.3.1 Robustness-Efficiency Trade-off      252 (2)
        9.3.2 When Are 2SLS and 3SLS Equivalent?   254 (1)
        9.3.3 Estimating the Reduced Form          255 (1)
        Parameters
      9.4 Additional Topics in Linear              256 (6)
      Simultaneous Equations Methods
        9.4.1 Using Cross Equation Restrictions    256 (1)
        to Achieve Identification
        9.4.2 Using Covariance Restrictions to     257 (3)
        Achieve Identification
        9.4.3 Subtleties Concerning                260 (2)
        Identification and Efficiency in Linear
        Systems
      9.5 Simultaneous Equations Models            262 (9)
      Nonlinear in Endogenous Variables
        9.5.1 Identification                       262 (4)
        9.5.2 Estimation                           266 (2)
        9.5.3 Control Function Estimation for      268 (3)
        Triangular Systems
      9.6 Different Instruments for Different      271 (14)
      Equations
        Problems                                   273 (12)
    10 Basic Linear Unobserved Effects and         285 (60)
    Explanatory Variables
      10.1 Motivation: Omitted Variables Problem   281 (4)
      10.2 Assumptions about the Unobserved        285 (6)
      Effects and Explanatory Variables
        10.2.1 Random or Fixed Effects?            285 (2)
        10.2.2 Strict Exogeneity Assumptions on    287 (2)
        the Explanatory Variables
        10.2.3 Some Examples of Unobserved         289 (2)
        Effects Panel Data Models
      10.3 Estimating Unobserved Effects Models    291 (1)
      by Pooled Ordinary Least Squares
      10.4 Random Effects Methods                  291 (9)
        10.4.1 Estimation and Inference under      291 (6)
        the Basic Random Effects Assumptions
        10.4.2 Robust Variance Matrix Estimator    297 (1)
        10.4.3 General Feasible Generalized        298 (1)
        Least Squares Analysis
        10.4.4 Testing for the Presence of an      299 (1)
        Unobserved Effect
      10.5 Fixed Effects Methods                   300 (15)
        10.5.1 Consistency of the Fixed Effects    300 (4)
        Estimator
        10.5.2 Asymptotic Inference with Fixed     304 (3)
        Effects
        10.5.3 Dummy Variable Regression           307 (3)
        10.5.4 Serial Correlation and the          310 (2)
        Robust Variance Matrix Estimator
        10.5.5 Fixed Effects Generalized Least     312 (3)
        Squares
        10.5.6 Using Fixed Effects Estimation      315 (1)
        for Policy Analysis
      10.6 First Differencing Methods              315 (6)
        10.6.1 Inference                           315 (3)
        10.6.2 Robust Variance Matrix              318 (1)
        10.6.3 Testing for Serial Correlation      319 (1)
        10.6.4 Policy Analysis Using First         320 (1)
        Differencing
      10.7 Comparison of Estimators                321 (24)
        10.7.1 Fixed Effects versus First          321 (5)
        Differencing
        10.7.2 Relationship between the Random     326 (2)
        Effects and Fixed Effects Estimators
        10.7.3 Hausman Test Comparing Random       328 (6)
        Effects and Fixed Effects Estimators
        Problems                                   334 (11)
    11 More Topics in Linear Unobserved Effects    345 (50)
    Models
      11.1 Generalized Method of Moments           345 (4)
      Approaches to the Standard Linear
      Unobserved Effects Model
        11.1.1 Equivalance between GMM 3SLS and    345 (2)
        Standard Estimators
        11.1.2 Chamberlain's Approach to           347 (2)
        Unobserved Effects Models
      11.2 Random and Fixed Effects                349 (9)
      Instrumental Variables Methods
      11.3 Hausman and Taylor-Type Models          358 (3)
      11.4 First Differencing Instrumental         361 (4)
      Variables Methods
      11.5 Unobserved Effects Models with          365 (3)
      Measurement Error
      11.6 Estimation under Sequential             368 (6)
      Exogeneity
        11.6.1 General Framework                   368 (3)
        11.6.2 Models with Lagged Dependent        371 (3)
        Variables
      11.7 Models with Individual-Specific         374 (21)
      Slopes
        11.7.1 Random Trend Model                  375 (2)
        11.7.2 General Models with                 377 (5)
        Individual-Specific Slopes
        11.7.3 Robustness of Standard Fixed        382 (2)
        Effects Methods
        11.7.4 Testing for Correlated Random       384 (3)
        Slopes
        Problems                                   387 (8)
  III GENERAL APPROACHES TO NONLINEAR ESTIMATION   395 (164)
    12 M-Estimation, Nonlinear Regression, and     397 (72)
    Quantile Regression
      12.1 Introduction                            397 (4)
      12.2 Identification, Uniform Convergence,    401 (4)
      and Consistency
      12.3 Asymptotic Normality                    405 (4)
      12.4 Two-Step M-Estimators                   409 (4)
        12.4.1 Consistency                         410 (1)
        12.4.2 Asymptotic Normality                411 (2)
      12.5 Estimating the Asymptotic Variance      413 (7)
        12.5.1 Estimation without Nuisance         413 (5)
        Parameters
        12.5.2 Adjustments for Two-Step            418 (2)
        Estimation
      12.6 Hypothesis Testing                      420 (11)
        12.6.1 Wald Tests                          420 (1)
        12.6.2 Score (or Lagrange Multiplier)      421 (7)
        Tests
        12.6.3 Tests Based on the Change in the    428 (2)
        Objective Function
        12.6.4 Behavior of the Statistics under    430 (1)
        Alternatives
      12.7 Optimization Methods                    431 (5)
        12.7.1 Newton-Raphson Method               432 (1)
        12.7.2 Berndt, Hall, Hall, and Hausman     433 (1)
        Algorithm
        12.7.3 Generalized Gauss-Newton Method     434 (1)
        12.7.4 Concentrating Parameters out of     435 (1)
        the Objective Function
      12.8 Simulation and Resampling Methods       436 (6)
        12.8.1 Monte Carlo Simulation              436 (2)
        12.8.2 Bootstrapping                       438 (4)
      12.9 Multivariate Nonlinear Regression       442 (7)
      Methods
        12.9.1 Multivariate Nonlinear Least        442 (2)
        Squares
        12.9.2 Weighted Multivariate Nonlinear     444 (5)
        Least Squares
      12.10 Quantile Estimation                    449 (20)
        12.10.1 Quantiles, the Estimation          449 (5)
        Problem, and Consistency
        12.10.2 Asymptotic Inference               454 (5)
        12.10.3 Quantile Regression for Panel      459 (3)
        Data
        Problems                                   462 (7)
    13 Maximum Likelihood Methods                  469 (56)
      13.1 Introduction                            469 (1)
      13.2 Preliminaries and Examples              470 (3)
      13.3 General Framework for Conditional       473 (2)
      Maximum Likelihood Estimation
      13.4 Consistency of Conditional Maximum      475 (1)
      Likelihood Estimation
      13.5 Asymptotic Normality and Asymptotic     476 (5)
      Variance Estimation
        13.5.1 Asymptotic Normality                476 (3)
        13.5.2 Estimating the Asymptotic           479 (2)
        Variance
      13.6 Hypothesis Testing                      481 (1)
      13.7 Specification Testing                   482 (3)
      13.8 Partial (or Pooled) Likelihood          485 (9)
      Methods for Panel Data
        13.8.1 Setup for Panel Data                486 (4)
        13.8.2 Asymptotic Inference                490 (2)
        13.8.3 Inference with Dynamically          492 (2)
        Complete Models
      13.9 Panel Data Models with Unobserved       494 (5)
      Effects
        13.9.1 Models with Strictly Exogenous      494 (3)
        Explanatory Variables
        13.9.2 Models with Lagged Dependent        497 (2)
        Variables
      13.10 Two-Step Estimators Involving          499 (3)
      Maximum Likelihood
        13.10.1 Second-Step Estimator Is           499 (1)
        Maximum Likelihood Estimator
        13.10.2 Surprising Efficiency Result       500 (2)
        When the First-Step Estimator Is
        Conditional Maximum Likelihood Estimator
      13.11 Quasi-Maximum Likelihood Estimation    502 (23)
        13.11.1 General Misspecification           502 (3)
        13.11.2 Model Selection Tests              505 (4)
        13.11.3 Quasi-Maximum Likelihood           509 (5)
        Estimation in the Linear Exponential
        Family
        13.11.4 Generalized Estimating             514 (3)
        Equations for Panel Data
        Problems                                   517 (5)
        Appendix 13A                               522 (3)
    14 Generalized Method of Moments and           525 (34)
    Minimum Distance Estimation
      14.1 Asymptotic Properties of Generalized    525 (5)
      Method of Moments
      14.2 Estimation under Orthogonality          530 (2)
      Conditions
      14.3 Systems of Nonlinear Equations          532 (6)
      14.4 Efficient Estimation                    538 (7)
        14.4.1 General Efficiency Framework        538 (2)
        14.4.2 Efficiency of Maximum Likelihood    540 (2)
        Estimator
        14.4.3 Efficienct Choice of Instruments    542 (3)
        under Conditional Moment Restrictions
      14.5 Classical Minimum Distance Estimation   545 (2)
      14.6 Panel Data Applications                 547 (12)
        14.6.1 Nonlinear Dynamic Models            547 (2)
        14.6.2 Minimum Distance Approach to the    549 (2)
        Unobserved Effects Model
        14.6.3 Models with Time-Varying            551 (4)
        Coefficients on the Unobserved Effects
        Problems                                   555 (3)
        Appendix 14A                               558 (1)
  IV NONLINEAR MODELS AND RELATED TOPICS           559 (466)
    15 Binary Response Models                      561 (82)
      15.1 Introduction                            561 (1)
      15.2 Linear Probability Model for Binary     562 (3)
      Response
      15.3 Index Models for Binary Response:       565 (2)
      Probit and Logit
      15.4 Maximum Likelihood Estimation of        567 (2)
      Binary Response Index Models
      15.5 Testing in Binary Response Index        569 (4)
      Models
        15.5.1 Testing Multiple Exclusion          570 (1)
        Restrictions
        15.5.2 Testing Nonlinear Hypotheses        571 (1)
        about β
        15.5.3 Tests against More General          571 (2)
        Alternatives
      15.6 Reporting the Results for Probit and    573 (9)
      Logit
      15.7 Specification Issues in Binary          582 (26)
      Response Models
        15.7.1 Neglected Heterogeneity             582 (3)
        15.7.2 Continuous Endogenous               585 (9)
        Explanatory Variables
        15.7.3 Binary Endogenous Explanatory       594 (5)
        Variable
        15.7.4 Heteroskedasticity and              599 (5)
        Nonnormality in the Latent Variable
        Model
        15.7.5 Estimation under Weaker             604 (4)
        Assumptions
      15.8 Binary Response Models for Panel Data   608 (35)
        15.8.1 Pooled Probit and Logit             609 (1)
        15.8.2 Unobserved Effects Probit Models    610 (9)
        under Strict Exogeneity
        15.8.3 Unobserved Effects Logit Models     619 (6)
        under Strict Exogeneity
        15.8.4 Dynamic Unobserved Effects Models   625 (5)
        15.8.5 Probit Models with Heterogeneity    630 (2)
        and Endogenous Explanatory Variables
        15.8.6 Semiparametric Approaches           632 (3)
        Problems                                   635 (8)
    16 Multinomial and Ordered Response Models     643 (24)
      16.1 Introduction                            643 (1)
      16.2 Multinomial Response Models             643 (12)
        16.2.1 Multinomial Logit                   643 (3)
        16.2.2 Probabilistic Choice Models         646 (5)
        16.2.3 Endogenous Explanatory Variables    651 (2)
        16.2.4 Panel Data Methods                  653 (2)
      16.3 Ordered Response Models                 655 (12)
        16.3.1 Ordered Logit and Ordered Probit    655 (3)
        16.3.2 Specification Issues in Ordered     658 (2)
        Models
        16.3.3 Endogenous Explanatory Variables    660 (2)
        16.3.4 Panel Data Methods                  662 (1)
        Problems                                   663 (4)
    17 Corner Solution Responses                   667 (56)
      17.1 Motivation and Examples                 667 (4)
      17.2 Useful Expressions for Type I Tobit     671 (5)
      17.3 Estimation and Inference with the       676 (1)
      Type I Tobit Model
      17.4 Reporting the Results                   677 (3)
      17.5 Specification Issues in Tobit Models    680 (10)
        17.5.1 Neglected Heterogeneity             680 (1)
        17.5.2 Endogenous Explanatory Models       681 (4)
        17.5.3 Heteroskedasticity and              685 (2)
        Nonnormality in the Latent Variable
        Model
        17.5.4 Estimating Parameters with          687 (3)
        Weaker Assumptions
      17.6 Two-Part Models and Type II Tobit       690 (13)
      for Corner Solutions
        17.6.1 Truncated Normal Hurdle Model       692 (2)
        17.6.2 Lognormal Hurdle Model and          694 (3)
        Exponential Conditional Mean
        17.6.3 Exponential Type II Tobit Model     697 (6)
      17.7 Two-Limit Tobit Model                   703 (2)
      17.8 Panel Data Methods                      705 (18)
        17.8.1 Pooled Methods                      705 (2)
        17.8.2 Unobserved Effects Models under     707 (6)
        Strict Exogeneity
        17.8.3 Dynamic Unobserved Effects Tobit    713 (2)
        Models
        Problems                                   715 (8)
    18 Count, Fractional, and Other Nonnegative    723 (54)
    Responses
      18.1 Introduction                            723 (1)
      18.2 Poisson Regression                      724 (12)
        18.2.1 Assumptions Used for Poission       724 (3)
        Regression and Quantities of Interest
        18.2.2 Consistency of the Poisson QMLE     727 (1)
        18.2.3 Asymptotic Normality of the         728 (4)
        Poisson QMLE
        18.2.4 Hypothesis Testing                  732 (2)
        18.2.5 Specification Testing               734 (2)
      18.3 Other Count Data Regression Models      736 (4)
        18.3.1 Negative Binomial Regression        736 (3)
        Models
        18.3.2 Binomial Regression Models          739 (1)
      18.4 Gamma (Exponential) Regression Model    740 (2)
      18.5 Endogeneity with an Exponential         742 (6)
      Regression Function
      18.6 Fractional Responses                    748 (7)
        18.6.1 Exogenous Explanatory Variables     748 (5)
        18.6.2 Endogenous Explanatory Variables    753 (2)
      18.7 Panel Data Methods                      755 (22)
        18.7.1 Pooled QMLE                         756 (2)
        18.7.2 Specifying Models of Conditional    758 (1)
        Expectations with Unobserved Effects
        18.7.3 Random Effects Methods              759 (3)
        18.7.4 Fixed Effects Poisson Estimation    762 (2)
        18.7.5 Relaxing the Strict Exogeneity      764 (2)
        Assumption
        18.7.6 Fractional Response Models for      766 (3)
        Panel Data
        Problems                                   769 (8)
    19 Censored Data, Sample Selection, and        777 (76)
    Attrition
      19.1 Introduction                            777 (1)
      19.2 Data Censoring                          778 (12)
        19.2.1 Binary Censoring                    780 (3)
        19.2.2 Interval Coding                     783 (2)
        19.2.3 Censoring from Above and Below      785 (5)
      19.3 Overview of Sample Selection            790 (2)
      19.4 When Can Sample Selection Be Ignored?   792 (7)
        19.4.1 Linear Models: Estimation by OLS    792 (6)
        and 2SLS
        19.4.2 Nonlinear Models                    798 (1)
      19.5 Selection on the Basis of the           799 (3)
      Response Variable: Truncated Regression
      19.6 Incidental Truncation: A Probit         802 (13)
      Selection Equation
        19.6.1 Exogenous Explanatory Variables     802 (7)
        19.6.2 Endogenous Explanatory Variables    809 (4)
        19.6.3 Binary Response Model with          813 (1)
        Sample Selection
        19.6.4 An Exponential Response Function    814 (1)
      19.7 Incidental Truncation: A Tobit          815 (6)
      Selection Equation
        19.7.1 Exogenous Explanatory Variables     815 (2)
        19.7.2 Endogenous Explanatory Variables    817 (2)
        19.7.3 Estimating Structural Tobit         819 (2)
        Equations with Sample Selection
      19.8 Inverse Probability Weighting for       821 (6)
      Missing Data
      19.9 Sample Selection and Attrition in       827 (26)
      Linear Panel Data Models
        19.9.1 Fixed and Random Effects            828 (4)
        Estimation with Unbalanced Panels
        19.9.2 Testing and Correcting for          832 (5)
        Sample Selection Bias
        19.9.3 Attrition                           837 (8)
        Problems                                   845 (8)
    20 Stratified Sampling and Cluster Sampling    853 (50)
      20.1 Introduction                            853 (1)
      20.2 Stratified Sampling                     854 (9)
        20.2.1 Standard Stratified Sampling and    854 (2)
        Variable Probability Sampling
        20.2.2 Weighted Estimators to Account      856 (5)
        for Stratification
        20.2.3 Stratification Based on             861 (2)
        Exogenous Variables
      20.3 Cluster Sampling                        863 (31)
        20.3.1 Inference with a Large Number of    864 (12)
        Clusters and Small Cluster Sizes
        20.3.2 Cluster Samples with                876 (7)
        Unit-Specific Panel Data
        20.3.3 Should We Apply Cluster-Robust      883 (1)
        Inference with Large Group Sizes?
        20.3.4 Inference When the Number of        884 (10)
        Clusters Is Small
      20.4 Complex Survey Sampling                 894 (9)
        Problems                                   899 (4)
    21 Estimating Average Treatment Effects        903 (80)
      21.1 Introduction                            903 (1)
      21.2 A Counterfactual Setting and the        904 (4)
      Self-Selection Problem
      21.3 Methods Assuming Ignorability (or       908 (29)
      Unconfoundedness) of Treatment
        21.3.1 Identification                      911 (4)
        21.3.2 Regression Adjustment               915 (5)
        21.3.3 Propensity Score Methods            920 (10)
        21.3.4 Combining Regression Adjustment     930 (4)
        and Propensity Score Weighting
        21.3.5 Matching Methods                    934 (3)
      21.4 Instrumental Variables Methods          937 (17)
        21.4.1 Estimating the Average Treatment    937 (8)
        Effect Using IV
        21.4.2 Correction and Control Function     945 (6)
        Approaches
        21.4.3 Estimating the Local Average        951 (3)
        Treatment Effect by IV
      21.5 Regression Discontinuity Designs        954 (6)
        21.5.1 The Sharp Regression                954 (3)
        Discontinuity Design
        21.5.2 The Fuzzy Regression                957 (2)
        Discontinuity Design
        21.5.3 Unconfoundedness versus the         959 (1)
        Fuzzy Regression Discontinuity
      21.6 Further Issues                          960 (23)
        21.6.1 Special Considerations for          960 (1)
        Responses with Discreteness or Limited
        Range
        21.6.2 Multivalued Treatments              961 (3)
        21.6.3 Multiple Treatments                 964 (4)
        21.6.4 Panel Data                          968 (7)
        Problems                                   975 (8)
    22 Duration Analysis                           983 (42)
      22.1 Introduction                            983 (1)
      22.2 Hazard Functions                        984 (7)
        22.2.1 Hazard Functions without            984 (4)
        Covariates
        22.2.2 Hazard Functions Conditional on     988 (1)
        Time-Invariant Covariates
        22.2.3 Hazard Functions Conditional on     989 (2)
        Time-Varying Covariates
      22.3 Analysis of Single-Spell Data with      991 (19)
      Time-Invariant Covariates
        22.3.1 Flow Sampling                       992 (1)
        22.3.2 Maximum Likelihood Estimation       993 (7)
        with Censored Flow Data
        22.3.3 Stock Sampling                      1000(3)
        22.3.4 Unobserved Heterogeneity            1003(7)
      22.4 Analysis of Grouped Duration Data       1010(8)
        22.4.1 Time-Invariant Covariates           1011(4)
        22.4.2 Time-Varying Covariates             1015(2)
        22.4.3 Unobserved Heterogeneity            1017(1)
      22.5 Further Issues                          1018(7)
        22.5.1 Cox's Partial Likelihood Method     1018(1)
        for the Proportional Hazard Model
        22.5.2 Multiple-Spell Data                 1018(1)
        22.5.3 Competing Risks Models              1019(1)
        Problems                                   1019(6)
References                                         1025(20)
Index                                              1045

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