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<P>Contents<br>Preface to the Second Edition page xiii<br>Preface to the First Edition xv<br>Chapter 1. Introduction 1<br>1.1 Advantages of Panel Data 1<br>1.2 Issues Involved in Utilizing Panel Data 8<br>1.2.1 Heterogeneity Bias 8<br>1.2.2 Selectivity Bias 9<br>1.3 Outline of the Monograph 11<br>Chapter 2. Analysis of Covariance 14<br>2.1 Introduction 14<br>2.2 Analysis of Covariance 15<br>2.3 An Example 21<br>Chapter 3. Simple Regression with Variable Intercepts 27<br>3.1 Introduction 27<br>3.2 Fixed-Effects Models: Least-Squares Dummy-Variable<br>Approach 30<br>3.3 Random-Effects Models: Estimation of<br>Variance-Components Models 34<br>3.3.1 Covariance Estimation 35<br>3.3.2 Generalized-Least-Squares Estimation 35<br>3.3.3 Maximum Likelihood Estimation 39<br>3.4 Fixed Effects or Random Effects 41<br>3.4.1 An Example 41<br>3.4.2 Conditional Inference or Unconditional (Marginal)<br>Inference 43<br>3.4.2.a Mundlak’s Formulation 44<br>3.4.2.b Conditional and Unconditional Inferences in the<br>Presence or Absence of Correlation between<br>Individual Effects and Attributes 46<br>viii Contents<br>3.5 Tests for Misspecification 49<br>3.6 Models with Specific Variables and Both Individual- and<br>Time-Specific Effects 51<br>3.6.1 Estimation of Models with Individual-Specific<br>Variables 51<br>3.6.2 Estimation of Models with Both Individual and<br>Time Effects 53<br>3.7 Heteroscedasticity 55<br>3.8 Models with Serially Correlated Errors 57<br>3.9 Models with Arbitrary Error Structure – Chamberlain π<br>Approach 60<br>Appendix 3A: Consistency and Asymptotic Normality of the<br>Minimum-Distance Estimator 65<br>Appendix 3B: Characteristic Vectors and the Inverse of the<br>Variance–Covariance Matrix of a<br>Three-Component Model 67<br>Chapter 4. Dynamic Models with Variable Intercepts 69<br>4.1 Introduction 69<br>4.2 The Covariance Estimator 71<br>4.3 Random-Effects Models 73<br>4.3.1 Bias in the OLS Estimator 73<br>4.3.2 Model Formulation 75<br>4.3.3 Estimation of Random-Effects Models 78<br>4.3.3.a Maximum Likelihood Estimator 78<br>4.3.3.b Generalized-Least-Squares Estimator 84<br>4.3.3.c Instrumental-Variable Estimator 85<br>4.3.3.d Generalized Method of Moments<br>Estimator 86<br>4.3.4 Testing Some Maintained Hypotheses on Initial<br>Conditions 90<br>4.3.5 Simulation Evidence 91<br>4.4 An Example 92<br>4.5 Fixed-Effects Models 95<br>4.5.1 Transformed Likelihood Approach 96<br>4.5.2 Minimum-Distance Estimator 98<br>4.5.3 Relations between the Likelihood-Based<br>Estimator and the Generalized Method of<br>Moments Estimator (GMM) 99<br>4.5.4 Random- versus Fixed-Effects Specification 101<br>4.6 Estimation of Dynamic Models with Arbitary Correlations<br>in the Residuals 103<br>4.7 Fixed-Effects Vector Autoregressive Models 105<br>4.7.1 Model Formulation 105<br>4.7.2 Generalized Method of Moments (GMM) Estimation 107<br>Contents ix<br>4.7.3 (Transformed) Maximum Likelihood Estimator 109<br>4.7.4 Minimum-Distance Estimator 109<br>Appendix 4A: Derivation of the Asymptotic Covariance Matrix<br>of the Feasible MDE 111<br>Chapter 5. Simultaneous-Equations Models 113<br>5.1 Introduction 113<br>5.2 Joint Generalized-Least-Squares Estimation Technique 116<br>5.3 Estimation of Structural Equations 119<br>5.3.1 Estimation of a Single Equation in the Structural Model 119<br>5.3.2 Estimation of the Complete Structural System 124<br>5.4 Triangular System 127<br>5.4.1 Identification 127<br>5.4.2 Estimation 129<br>5.4.2.a Instrumental-Variable Method 130<br>5.4.2.b Maximum-Likelihood Method 133<br>5.4.3 An Example 136<br>Appendix 5A 138<br>Chapter 6. Variable-Coefficient Models 141<br>6.1 Introduction 141<br>6.2 Coefficients That Vary over Cross-Sectional Units 143<br>6.2.1 Fixed-Coefficient Model 144<br>6.2.2 Random-Coefficient Model 144<br>6.2.2.a The Model 144<br>6.2.2.b Estimation 145<br>6.2.2.c Predicting Individual Coefficients 147<br>6.2.2.d Testing for Coefficient Variation 147<br>6.2.2.e Fixed or Random Coefficients 149<br>6.2.2.f An Example 150<br>6.3 Coefficients That Vary over Time and Cross-Sectional Units 151<br>6.3.1 The Model 151<br>6.3.2 Fixed-Coefficient Model 153<br>6.3.3 Random-Coefficient Model 153<br>6.4 Coefficients That Evolve over Time 156<br>6.4.1 The Model 156<br>6.4.2 Predicting
t by the Kalman Filter 158<br>6.4.3 Maximum Likelihood Estimation 161<br>6.4.4 Tests for Parameter Constancy 162<br>6.5 Coefficients That Are Functions of Other Exogenous<br>Variables 163<br>6.6 A Mixed Fixed- and Random-Coefficients Model 165<br>6.6.1 Model Formulation 165<br>6.6.2 A Bayes Solution 168<br>6.6.3 An Example 170<br>x Contents<br>6.6.4 Random or Fixed Parameters 172<br>6.6.4.a An Example 172<br>6.6.4.b Model Selection 173<br>6.7 Dynamic Random-Coefficient Models 175<br>6.8 An Example – Liquidity Constraints and Firm Investment<br>Expenditure 180<br>Appendix 6A: Combination of Two Normal Distributions 185<br>Chapter 7. Discrete Data 188<br>7.1 Introduction 188<br>7.2 Some Discrete-Response Models 188<br>7.3 Parametric Approach to Static Models with Heterogeneity 193<br>7.3.1 Fixed-Effects Models 194<br>7.3.1.a Maximum Likelihood Estimator 194<br>7.3.1.b Conditions for the Existence of a Consistent<br>Estimator 195<br>7.3.1.c Some Monte Carlo Evidence 198<br>7.3.2 Random-Effects Models 199<br>7.4 Semiparametric Approach to Static Models 202<br>7.4.1 Maximum Score Estimator 203<br>7.4.2 A Root-N Consistent Semiparametric Estimator 205<br>7.5 Dynamic Models 206<br>7.5.1 The General Model 206<br>7.5.2 Initial Conditions 208<br>7.5.3 A Conditional Approach 211<br>7.5.4 State Dependence versus Heterogeneity 216<br>7.5.5 Two Examples 218<br>7.5.5.a Female Employment 218<br>7.5.5.b Household Brand Choices 221<br>Chapter 8. Truncated and Censored Data 225<br>8.1 Introduction 225<br>8.2 An Example – Nonrandomly Missing Data 234<br>8.2.1 Introduction 234<br>8.2.2 A Probability Model of Attrition and Selection Bias 235<br>8.2.3 Attrition in the Gary Income-Maintenance Experiment 238<br>8.3 Tobit Models with Random Individual Effects 240<br>8.4 Fixed-Effects Estimator 243<br>8.4.1 Pairwise Trimmed Least-Squares and<br>Least-Absolute-Deviation Estimators for<br>Truncated and Censored Regressions 243<br>8.4.1.a Truncated Regression 243<br>8.4.1.b Censored Regressions 249<br>8.4.2 A Semiparametric Two-Step Estimator for the<br>Endogenously Determined Sample Selection Model 253<br>Contents xi<br>8.5 An Example: Housing Expenditure 255<br>8.6 Dynamic Tobit Models 259<br>8.6.1 Dynamic Censored Models 259<br>8.6.2 Dynamic Sample Selection Models 265<br>Chapter 9. Incomplete Panel Data 268<br>9.1 Estimating Distributed Lags in Short Panels 268<br>9.1.1 Introduction 268<br>9.1.2 Common Assumptions 270<br>9.1.3 Identification Using Prior Structure of the Process<br>of the Exogenous Variable 271<br>9.1.4 Identification Using Prior Structure of the Lag<br>Coefficients 275<br>9.1.5 Estimation and Testing 277<br>9.2 Rotating or Randomly Missing Data 279<br>9.3 Pseudopanels (or Repeated Cross-Sectional<br>Data) 283<br>9.4 Pooling of a Single Cross-Sectional and a Single<br>Time-Series Data Set 285<br>9.4.1 Introduction 285<br>9.4.2 The Likelihood Approach to Pooling Cross-Sectional<br>and Time-Series Data 287<br>9.4.3 An Example 288<br>Chapter 10. Miscellaneous Topics 291<br>10.1 Simulation Methods 291<br>10.2 Panels with Large N and T 295<br>10.3 Unit-Root Tests 298<br>10.4 Data with Multilevel Structures 302<br>10.5 Errors of Measurement 304<br>10.6 Modeling Cross-Sectional Dependence 309<br>Chapter 11. A Summary View 311<br>11.1 Introduction 311<br>11.2 Benefits and Limitations of Panel Data 311<br>11.2.1 Increasing Degrees of Freedom and Lessening<br>the Problem of Multicollinearity 311<br>11.2.2 Identification and Discrimination between<br>Competing Hypotheses 312<br>11.2.3 Reducing Estimation Bias 313<br>11.2.3.a Omitted-Variable Bias 313<br>11.2.3.b Bias Induced by the Dynamic Structure<br>of a Model 315<br>11.2.3.c Simultaneity Bias 316<br>11.2.3.d Bias Induced by Measurement Errors 316<br>xii Contents<br>11.2.4 Providing Micro Foundations for Aggregate Data<br>Analysis 316<br>11.3 Efficiency of the Estimates 317<br>Notes 319<br>References 331<br>Author Index 353<br>Subject Index 359</P></DIV>
<P><br>pdf 版本下载 见第 8楼; (原售价 $2 已被斑竹改成 $100了;我想这样做才对得起这本书啊)</P>
<P>djvu 版本下载 见第 5楼 </P>
<P>两者差别在于清晰度,前者优</P>
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