Part 1 Fundamentals
1. Introduction ................................................. 3
Marc Nerlove, Patrick Sevestre and Pietro Balestra
1.1. Introduction ............................................ 3
1.2. Data, Data-Generating Processes (DGP), and Inference .... 4
1.3. History and Dynamics .................................... 8
1.4. A Brief Review of Other Methodological Developments .... 13
1.5. Conclusion ............................................. 21
References .................................................. 21
2. Fixed Effects Models and Fixed Coefficients Models .......... 23
Pietro Balestra and Jayalakshmi Krishnakumar
2.1. The Covariance Model: Individual Effects Only .......... 24
2.1.1. Specification ................................... 24
2.1.2. Estimation ...................................... 25
2.1.3. Inference ....................................... 28
2.2. The Covariance Model: Individual and Time Effects ...... 29
2.2.1. Time Effects Only ............................... 29
2.2.2. Time and Individual Effects ..................... 30
2.2.3. Inference ....................................... 32
2.3. Non-spherical Disturbances ............................. 33
2.3.1. What Variance-Covariance Stucture? .............. 33
2.3.2. Two General Propositions for Fixed Effects
Models .......................................... 34
2.3.3. Individual Fixed Effects and Serial
Correlation ..................................... 36
2.3.4. Heteroscedasticity in Fixed Effects Models ...... 38
2.4. Extensions ............................................. 40
2.4.1. Constant Variables in One Dimension ............. 40
2.4.2. Variable Slope Coefficients ..................... 41
2.4.3. Unbalanced Panels ............................... 44
References .................................................. 48
3. Error Components Models ..................................... 49
Badi H. Baltagi, Laszlo Matyas and Patrick Sevestre
3.1. Introduction ........................................... 49
3.2. The One-Way Error Components Model ..................... 50
3.2.1. Definition/Assumptions of the Model ............. 50
3.2.2. The GLS Estimator ............................... 52
3.2.3. The Feasible GLS Estimator ...................... 55
3.2.4. Some Other Estimators ........................... 58
3.2.5. Prediction ...................................... 63
3.3. More General Structures of the Disturbances ............ 64
3.3.1. The Two-Way Error Components Model .............. 64
3.3.2. Serial Correlation in the Disturbances .......... 70
3.3.3. Two-Way Error Components vs Kmenta's
Approach ........................................ 73
3.3.4. Heteroskedasticity in the Disturbances .......... 74
3.4. Testing ................................................ 78
3.4.1. Testing for the Absence of Individual Effects ... 79
3.4.2. Testing for Uncorrected Effects: Hausman's
Test ............................................ 80
3.4.3. Testing for Serial Correlation .................. 81
3.4.4. Testing for Heteroskedasticity .................. 82
3.5. Estimation Using Unbalanced Panels ..................... 84
References .................................................. 85
4. Endogenous Regressors and Correlated Effects ................ 89
Rachid Boumahdi and Alban Thomas
4.1. Introduction ........................................... 89
4.2. Estimation of Transformed Linear Panel Data Models ..... 90
4.2.1. Error Structures and Filtering Procedures ....... 91
4.2.2. An IV Representation of the Transformed
Linear Model .................................... 93
4.3. Estimation with Time-Invariant Regressors .............. 95
4.3.1. Introduction .................................... 95
4.3.2. Instrumental Variable Estimation ................ 96
4.3.3. More Efficient IV Procedures .................... 98
4.4. A Measure of Instrument Relevance ...................... 99
4.5. Incorporating Time-Varying Regressors ................. 101
4.5.1. Instrumental Variables Estimation .............. 102
4.6. GMM Estimation of Static Panel Data Models ............ 104
4.6.1. Static Model Estimation ........................ 105
4.6.2. GMM Estimation with HT, AM and BMS
Instruments .................................... 107
4.7. Unbalanced Panels ..................................... 108
References ................................................. 110
5. The Chamberlain Approach to Panel Data: An Overview
and Some Simulations ....................................... 113
Bruno Crepon and Jacques Mairesse
5.1. Introduction .......................................... 113
5.2. The Chamberlain П Matrix Framework .................... 115
5.2.1. The П Matrix ................................... 115
5.2.2. Relations Between П and the Parameters of
Interest ....................................... 118
5.2.3. Four Important Cases ........................... 120
5.2.4. Restrictions on the Covariance Matrix of
the Disturbances ............................... 124
5.2.5. A Generalization of the Chamberlain Method ..... 125
5.2.6. The Vector Representation of the Chamberlain
Estimating Equations ........................... 126
5.2.7. The Estimation of Matrix П ..................... 127
5.3. Asymptotic Least Squares .............................. 130
5.3.1. ALS Estimation ................................. 130
5.3.2. The Optimal ALS Estimator ...................... 132
5.3.3. Specification Testing in the ALS Framework ..... 135
5.4. The Equivalence of the GMM and the Chamberlain
Methods ............................................... 137
5.4.1. A Reminder on the GMM .......................... 137
5.4.2. Equivalence of the GMM and the Chamberlain
Methods ........................................ 139
5.4.3. Equivalence in Specific Cases .................. 140
5.5. Monte Carlo Simulations ............................... 144
5.5.1. Design of the Simulation Experiments ........... 144
5.5.2. Consistency and Bias ........................... 147
5.5.3. Efficiency and Robustness ...................... 152
5.5.4. Standard Errors ................................ 155
5.5.5. Specification Tests ............................ 158
5.6. Appendix A: An Extended View of the Chamberlain
Method ................................................ 160
5.6.1. Simultaneous Equations Models .................. 160
5.6.2. VAR Models ..................................... 160
5.6.3. Endogenous Attrition ........................... 162
5.7. Appendix B: Vector Representation of the Chamberlain
Estimating Equations .................................. 163
5.7.1. The Vec Operator ............................... 163
5.7.2. Correlated Effects ............................. 164
5.7.3. Errors in Variables ............................ 164
5.7.4. Weak Simultaneity .............................. 166
5.7.5. Combination of the Different Cases ............. 166
5.7.6. Lagged Dependent Variable ...................... 167
5.7.7. Restrictions on the Covariance Matrix of
the Disturbances ............................... 167
5.8. Appendix C: Manipulation of Equations and
Parameters in the ALS Framework ....................... 168
5.8.1. Transformation of the Estimating Equations ..... 168
5.8.2. Eliminating Parameters of Secondary Interest ... 169
5.8.3. Recovering Parameters of Secondary Interest
Once Eliminated ................................ 170
5.8.4. Elimination of Auxiliary Parameters ............ 173
5.9. Appendix D: Equivalence Between Chamberlain's, GMM
and Usual Panel Data Estimators ....................... 174
5.10.Appendix E: Design of Simulation Experiments .......... 177
5.10.1.Generating Process of the Variable x ........... 177
5.10.2.Regression Model ............................... 178
5.10.3.Calibration of Simulations ..................... 179
5.10.4.Three Scenarios ................................ 180
5.10.5.The Chamberlain and GMM Estimators ............. 180
5.10.6.Standard Errors and Specification Tests ........ 181
References ................................................. 181
6. Random Coefficient Models .................................. 185
Cheng Hsiao and M. Hashem Pesaran
6.1. Introduction .......................................... 185
6.2. The Models ............................................ 186
6.3. Sampling Approach ..................................... 189
6.4. Mean Group Estimation ................................. 192
6.5. Bayesian Approach ..................................... 193
6.6. Dynamic Random Coefficients Models .................... 197
6.7. Testing for Heterogeneity Under Weak Exogeneity ....... 199
6.8. A Random Coefficient Simultaneous Equation System ..... 203
6.9. Random Coefficient Models with Cross-Section
Dependence ............................................ 206
6.10.Concluding Remarks .................................... 208
References ................................................. 211
7. Parametric Binary Choice Models ............................ 215
Michael Lechner, Stefan Lollivier and Thierry Magnac
7.1. Introduction .......................................... 215
7.2. Random Effects Models Under Strict Exogeneity ......... 217
7.2.1. Errors are Independent Over Time ............... 218
7.2.2. One Factor Error Terms ......................... 219
7.2.3. General Error Structures ....................... 221
7.2.4. Simulation Methods ............................. 223
7.2.5. How to Choose a Random Effects Estimator
for an Application ............................. 228
7.2.6. Correlated Effects ............................. 229
7.3. Fixed Effects Models Under Strict Exogeneity .......... 230
7.3.1. The Model ...................................... 231
7.3.2. The Method of Conditional Likelihood ........... 232
7.3.3. Fixed Effects Maximum Score .................... 235
7.3.4. GMM Estimation ................................. 236
7.3.5. Large-T Approximations ......................... 237
7.4. Dynamic Models ........................................ 238
7.4.1. Dynamic Random Effects Models .................. 238
7.4.2. Dynamic Fixed Effects Models ................... 241
References ................................................. 242
Part II Advanced Topics
8. Dynamic Models for Short Panels ............................ 249
Mark N. Harris, Laszlo Matyas and Patrick Sevestre
8.1. Introduction .......................................... 249
8.2. The Model ............................................. 250
8.3. The Inconsistency of Traditional Estimators ........... 252
8.4. IV and GMM Estimators ................................. 255
8.4.1. Uncorrected Individual Effects: The Original
Balestra-Nerlove Estimator and its
Extensions ..................................... 256
8.4.2. Correlated Individual Effects .................. 257
8.4.3. Some Monte Carlo Evidence ...................... 269
8.5. The Maximum Likelihood Estimator ...................... 270
8.6. Testing in Dynamic Models ............................. 272
8.6.1. Testing the Validity of Instruments ............ 272
8.6.2. Testing for Unobserved Effects ................. 273
8.6.3. Testing for the Absence of Serial
Correlation in ε ............................... 274
8.6.4. Significance Testing in Two-Step Variants ...... 275
References ................................................. 276
9. Unit Roots and Cointegration in Panels ..................... 279
Jorg Breitung and M. Hashem Pesaran
9.1. Introduction .......................................... 279
9.2. First Generation Panel Unit Root Tests ................ 281
9.2.1. The Basic Model ................................ 281
9.2.2. Derivation of the Tests ........................ 282
9.2.3. Null Distribution of the Tests ................. 284
9.2.4. Asymptotic Power of the Tests .................. 287
9.2.5. Heterogeneous Trends ........................... 288
9.2.6. Short-Run Dynamics ............................. 291
9.2.7. Other Approaches to Panel Unit Root Testing .... 293
9.3. Second Generation Panel Unit Root Tests ............... 295
9.3.1. Cross-Section Dependence ....................... 295
9.3.2. Tests Based on GLS Regressions ................. 296
9.3.3. Test Statistics Based on OLS Regressions ....... 297
9.3.4. Other Approaches ............................... 298
9.4. Cross-Unit Cointegration .............................. 299
9.5. Finite Sample Properties of Panel Unit Root Tests ..... 301
9.6. Panel Cointegration: General Considerations ........... 302
9.7. Residual-Based Approaches to Panel Cointegration ...... 306
9.7.1. Spurious Regression ............................ 306
9.7.2. Tests of Panel Cointegration ................... 307
9.8. Tests for Multiple Cointegration ...................... 308
9.9. Estimation of Cointegrating Relations in Panels ....... 309
9.9.1. Single Equation Estimators ..................... 309
9.9.2. System Estimators .............................. 312
9.10.Cross-Section Dependence and the Global VAR ........... 313
9.11.Concluding Remarks .................................... 316
References ................................................. 316
10.Measurement Errors and Simultaneity ........................ 323
Erik Biorn and Jayalakshmi Krishnakumar
10.1.Introduction .......................................... 323
10.2.Measurement Errors and Panel Data ..................... 323
10.2.1.Model and Orthogonality Conditions ............. 325
10.2.2.Identification and the Structure of the
Second Order Moments ........................... 327
10.2.3.Moment Conditions .............................. 328
10.2.4.Estimators Constructed from Period Means ....... 331
10.2.5.GMM Estimation and Testing in the General
Case ........................................... 332
10.2.6.Estimation by GMM, Combining Differences
and Levels ..................................... 335
10.2.7.Extensions: Modifications ...................... 343
10.2.8.Concluding Remarks ............................. 343
10.3.Simultaneity and Panel Data ........................... 344
10.3.1.SEM with EC .................................... 345
10.3.2.Extensions ..................................... 361
10.4.Conclusion ............................................ 364
References ................................................. 365
11.Pseudo-Panels and Repeated Cross-Sections .................. 369
Marno Verbeek
11.1.Introduction .......................................... 369
11.2.Estimation of a Linear Fixed Effects Model ............ 370
11.3.Estimation of a Linear Dynamic Model .................. 376
11.4.Estimation of a Binary Choice Model ................... 380
11.5.Concluding Remarks .................................... 381
References ................................................. 382
12.Attrition, Selection Bias and Censored Regressions ......... 385
Bo Honore, Francis Vella and Marno Verbeek
12.1.Introduction .......................................... 385
12.2.Censoring, Sample Selection and Attrition ............. 386
12.3.Sample Selection and Attrition ........................ 389
12.4.Sample Selection Bias and Robustness of Standard
Estimators ............................................ 391
12.5.Tobit and Censored Regression Models .................. 393
12.5.1.Random Effects Tobit ........................... 394
12.5.2.Random Effects Tobit with Endogenous
Explanatory Variables .......................... 396
12.5.3.Dynamic Random Effects Tobit ................... 398
12.5.4.Fixed Effects Tobit Estimation ................. 399
12.5.5.Semi-parametric Estimation ..................... 401
12.5.6.Semi-parametric Estimation in the Presence
of Lagged Dependent Variables .................. 402
12.6.Models of Sample Selection and Attrition .............. 402
12.6.1.Maximum Likelihood Estimators .................. 403
12.6.2.Two-Step Estimators ............................ 404
12.6.3.Alternative Selection Rules .................... 407
12.6.4.Two-Step Estimators with Fixed Effects ......... 408
12.6.5.Semi-parametric Sample Selection Models ........ 409
12.6.6.Semi-parametric Estimation of a Type-3 Tobit
Model .......................................... 410
12.7.Some Empirical Applications ........................... 412
12.7.1.Attrition in Experimental Data ................. 412
12.7.2.Real Wages Over the Business Cycle ............. 413
12.7.3.Unions and Wages ............................... 415
References ................................................. 416
13.Simulation Techniques for Panels: Efficient Importance
Sampling ................................................... 419
Roman Liesenfeld and Jean-Francois Richard
13.1.Introduction .......................................... 419
13.2.Pseudorandom Number Generation ........................ 420
13.2.1.Univariate Distributions ....................... 421
13.2.2.Multivariate Distributions ..................... 424
13.3.Importance Sampling ................................... 426
13.3.1.General Principle .............................. 426
13.3.2.Efficient Importance Sampling .................. 428
13.3.3.MC Sampling Variance of (E)IS Estimates ........ 431
13.3.4.GHK Simulator .................................. 432
13.3.5.Common Random Numbers .......................... 432
13.4.Simulation-Based Inference Procedures ............ 434
13.4.1.Integration in Panel Data Models ............... 434
13.4.2.Simulated Likelihood ........................... 435
13.4.3.Simulated Method of Moments .................... 435
13.4.4.Bayesian Posterior Moments ..................... 437
13.5.Numerical Properties of Simulated Estimators .......... 437
13.6.EIS Application: Logit Panel with Unobserved
Heterogeneity ......................................... 439
13.6.1.The Model ...................................... 439
13.6.2.EIS Evaluation of the Likelihood ............... 440
13.6.3.Empirical Application .......................... 443
13.7.Conclusion ............................................ 445
13.8.Appendix: Implementation of EIS for the Logit Panel
Model ................................................. 446
References ................................................. 448
14.Semi-parametric and Non-parametric Methods in Panel Data
Models ..................................................... 451
Chunrong Ai and Qi Li
14.1.Introduction .......................................... 451
14.2.Linear Panel Data Model ............................... 452
14.2.1.Additive Effect ................................ 452
14.2.2.Multiplicative Effect .......................... 460
14.3.Nonlinear Panel Data Model ............................ 462
14.3.1.Censored Regression Model ...................... 462
14.3.2.Discrete Choice Model .......................... 470
14.3.3.Sample Selection Model ......................... 474
14.4.Conclusion ............................................ 475
References ................................................. 476
15.Panel Data Modeling and Inference: A Bayesian Primer ....... 479
Siddhartha Chib
15.1.Introduction .......................................... 479
15.1.1.Hierarchical Prior Modeling .................... 480
15.1.2.Elements of Markov Chain Monte Carlo ........... 483
15.1.3.Some Basic Bayesian Updates .................... 486
15.1.4.Basic Variate Generators ....................... 488
15.2.Continuous Responses .................................. 489
15.2.1.Gaussian-Gaussian Model ........................ 490
15.2.2.Robust Modeling of b1: Student-Student
and Student-Mixture Models ..................... 492
15.2.3.Heteroskedasticity ............................. 495
15.2.4.Serial Correlation ............................. 496
15.3.Binary Responses ...................................... 497
15.4.Other Outcome Types ................................... 501
15.4.1.Censored Outcomes .............................. 501
15.4.2.Count Responses ................................ 502
15.4.3.Multinomial Responses .......................... 503
15.5.Binary Endogenous Regressor ........................... 504
15.6.Informative Missingness ............................... 507
15.7.Prediction ............................................ 508
15.8.Residual Analysis ..................................... 509
15.9.Model Comparisons ..................................... 509
15.9.1.Gaussian-Gaussian Model ........................ 512
15.9.2.Gaussian-Gaussian Tobit model .................. 512
15.9.3.Panel Poisson Model ............................ 513
15.10.Conclusion ........................................... 513
References ................................................. 514
16.To Pool or Not to Pool? .................................... 517
Badi H. Baltagi, Georges Bresson and Alain Pirotte
16.1.Introduction .......................................... 517
16.2.Tests for Poolability, Pretesting and Stein-Rule
Methods ............................................... 521
16.2.1.Tests for Poolability .......................... 521
16.2.2.Pretesting and Stein-Rule Methods .............. 525
16.2.3.Example ........................................ 526
16.3.Heterogeneous Estimators .............................. 527
16.3.1.Averaging Estimators ........................... 529
16.3.2.Bayesian Framework ............................. 530
16.3.3.An Example ..................................... 538
16.4.Comments on the Predictive Approach ................... 541
16.4.1.From the Post-sample Predictive Density ........ 541
16.4.2. ... to the Good Forecast Performance of
the Hierarchical Bayes Estimator: An Example ... 542
16.5.Conclusion ............................................ 544
References ................................................. 545
17.Duration Models and Point Processes ........................ 547
Jean-Pierre Florens, Denis Fougere and Michel
Mouchart
17.1.Marginal Duration Models .............................. 548
17.1.1.Distribution, Survivor and Density Functions ... 548
17.1.2.Truncated Distributions and Hazard Functions ... 550
17.2.Conditional Models .................................... 552
17.2.1.General Considerations ......................... 552
17.2.2.The Proportional Hazard or Cox Model ........... 555
17.2.3.The Accelerated Time Model ..................... 557
17.2.4.Aggregation and Heterogeneity .................. 558
17.2.5.Endogeneity .................................... 560
17.3.Competing Risks and Multivariate Duration Models ...... 561
17.3.1.Multivariate Durations ......................... 561
17.3.2.Competing Risks Models: Definitions ............ 563
17.3.3.Identifiability of Competing Risks Models ...... 566
17.3.4.Right-Censoring ................................ 568
17.4.Inference in Duration Models .......................... 570
17.4.1.Introduction ................................... 570
17.4.2.Parametric Models .............................. 570
17.4.3.Non-parametric and Semi-parametric Models ...... 576
17.5.Counting Processes and Point Processes ................ 579
17.5.1.Definitions .................................... 579
17.5.2.Stochastic Intensity, Compensator and
Likelihood of a Counting Process ............... 581
17.6.Poisson, Markov and Semi-Markov Processes ............. 584
17.6.1.Poisson Processes .............................. 584
17.6.2.Markov Processes ............................... 585
17.6.3.Semi-Markov Processes .......................... 592
17.7.Statistical Analysis of Counting Processes ............ 594
17.7.1.The Cox Likelihood ............................. 596
17.7.2.The Martingale Estimation of the Integrated
Baseline Intensity ............................. 597
17.8.Conclusions ........................................... 600
References ................................................. 600
18.GMM for Panel Data Count Models ............................ 603
Frank Windmeijer
18.1.Introduction .......................................... 603
18.2.GMM in Cross-Sections ................................. 604
18.3.Panel Data Models ..................................... 606
18.3.1.Strictly Exogenous Regressors .................. 607
18.3.2.Predetermined Regressors ....................... 608
18.3.3.Endogenous Regressors .......................... 609
18.3.4.Dynamic Models ................................. 610
18.4.GMM ................................................... 612
18.5.Applications and Software ............................. 614
18.6.Finite Sample Inference ............................... 615
18.6.1.Wald Test and Finite Sample Variance
Correction ..................................... 615
18.6.2.Criterion-Based Tests .......................... 617
18.6.3.Continuous Updating Estimator .................. 618
18.6.4.Monte Carlo Results ............................ 619
References ................................................. 623
19.Spatial Panel Econometrics ................................. 625
Luc Anselin, Julie Le Gallo and Hubert Jayet
19.1.Introduction .......................................... 625
19.2.Spatial Effects ....................................... 626
19.2.1.Spatial Weights and Spatial Lag Operator ....... 628
19.2.2.Spatial Lag Model .............................. 630
19.2.3.Spatial Error Model ............................ 632
19.3.A Taxonomy of Spatial Panel Model Specifications ...... 636
19.3.1.Temporal Heterogeneity ......................... 637
19.3.2.Spatial Heterogeneity .......................... 639
19.3.3.Spatio-Temporal Models ......................... 644
19.4.Estimation of Spatial Panel Models .................... 648
19.4.1.Maximum Likelihood Estimation .................. 648
19.4.2.Instrumental Variables and GMM ................. 652
19.5.Testing for Spatial Dependence ........................ 654
19.5.1.Lagrange Multiplier Tests for Spatial Lag
and Spatial Error Dependence in Pooled
Models ......................................... 655
19.5.2.Testing for Spatial Error Correlation
in Panel Data Models ........................... 655
19.6.Conclusions ........................................... 656
References ................................................. 657
Part III Applications
20.Foreign Direct Investment: Lessons from Panel Data ......... 663
Pierre Blanchard, Carl Gaigne and Claude Mathieu
20.1.Introduction .......................................... 663
20.2.A Simple Model of FDI ................................. 664
20.2.1.Assumptions and Preliminary Results ............ 665
20.2.2.Technology and Country Characteristics
as Determinants of FDI ......................... 666
20.3.Econometric Implementation and Data ................... 668
20.3.1.A General Econometric Model .................... 669
20.3.2.FDI and Data Issues ............................ 670
20.4.Empirical Estimations: Selected Applications .......... 672
20.4.1.Testing the Trade-Off Between FDI and
Exports ........................................ 672
20.4.2.Testing the Role of Trade Policy in FDI ........ 677
20.4.3.Testing the Relationship Between FDI
and Exchange Rate .............................. 683
20.5.Some Recent Econometric Issues ........................ 690
20.5.1.FDI, Panel Data and Spatial Econometrics ....... 690
20.5.2.Exchange Rate, Unit Roots and Cointegration .... 691
References ................................................. 693
21.Stochastic Frontier Analysis and Efficiency Estimation ..... 697
Christopher Cornwell and Peter Schmidt
21.1.Measurement of Firm Efficiency ........................ 698
21.2.Introduction to SFA ................................... 700
21.2.1.The Basic SFA Empirical Framework .............. 700
21.2.2.Stochastic vs Deterministic Frontiers .......... 700
21.2.3.Other Frontier Functions ....................... 702
21.2.4.SFA with Cross-Section Data .................... 703
21.3.SFA with Panel Data ................................... 704
21.3.1.Models with Time-Invariant Inefficiency ........ 704
21.3.2.Models with Time-Varying Inefficiency .......... 714
21.4.Applications .......................................... 718
21.4.1.Egyptian Tile Manufacturers .................... 718
21.4.2.Indonesian Rice Farmers ........................ 720
21.5.Concluding Remarks .................................... 723
References ................................................. 723
22.Econometric Analyses of Linked Employer-Employee Data ...... 727
John M. Abowd, Francis Kramarz and Simon Woodcock
22.1.Introduction .......................................... 727
22.2.A Prototypical Longitudinal Linked Data Set ........... 729
22.2.1.Missing Data ................................... 730
22.2.2.Sampling from Linked Data ...................... 732
22.3.Linear Statistical Models with Person and Firm
Effects ............................................... 733
22.3.1.A General Specification ........................ 733
22.3.2.The Pure Person and Firm Effects
Specification .................................. 734
22.4.Definition of Effects of Interest ..................... 735
22.4.1.Person Effects and Unobservable Personal
Heterogeneity .................................. 735
22.4.2.Firm Effects and Unobservable Firm
Heterogeneity .................................. 736
22.4.3.Firm-Average Person Effect ..................... 737
22.4.4.Person-Average Firm Effect ..................... 737
22.4.5.Industry Effects ............................... 738
22.4.6.Other Firm Characteristic Effects .............. 739
22.4.7.Occupation Effects and Other Person ×
Firm Interactions .............................. 739
22.5.Estimation by Fixed Effects Methods ................... 739
22.5.1.Estimation of the Fixed Effects Model
by Direct Least Squares ........................ 739
22.5.2.Consistent Methods for β and γ (The Firm-
Specific Returns to Seniority) ................. 743
22.6.The Mixed Model ....................................... 744
22.6.1.REML Estimation of the Mixed Model ............. 746
22.6.2.Estimating the Fixed Effects and Realized
Random Effects ................................. 747
22.6.3.Mixed Models and Correlated Random Effects
Models ......................................... 748
22.7.Models of Heterogeneity Biases in Incomplete Models ... 750
22.7.1.Omission of the Firm Effects ................... 750
22.7.2.Omission of the Person Effects ................. 751
22.7.3.Inter-industry Wage Differentials .............. 752
22.8.Endogenous Mobility ................................... 753
22.8.1.A Generalized Linear Mixed Model ............... 754
22.8.2.A Model of Wages, Endogenous Mobility and
Participation with Person and Firm Effects ..... 755
22.8.3.Stochastic Assumptions ......................... 756
22.9.Conclusion ............................................ 758
References ................................................. 758
23.Life Cycle Labor Supply and Panel Data: A Survey ........... 761
Bertrand Koebel, Francois Laisney, Winfried Pohlmeier
and Matthias Staat
23.1.Introduction .......................................... 761
23.2.The Basic Model of Life Cycle Labor Supply ............ 762
23.2.1.The Framework .................................. 763
23.2.2.First Specifications of the Utility Function ... 765
23.3.Taking Account of Uncertainty and Risk ................ 768
23.3.1.First Developments ............................. 768
23.3.2.Recent Contributions ........................... 770
23.3.3.Empirical Results .............................. 773
23.3.3.Empirical Results .............................. 773
23.4.Voluntary and Involuntary Non-participation ........... 774
23.4.1.Accounting for the Participation Decision ...... 775
23.4.2.Unemployment ................................... 778
23.5.Alternative Parameterization and Implications ......... 779
23.6.Relaxing Separability Assumptions ..................... 783
23.6.1.Relaxing Within-Period Additive Separability ... 783
23.6.2.Relaxing Intertemporal Separability in
Preferences .................................... 784
23.7.Conclusion ............................................ 790
References ................................................. 791
24.Dynamic Policy Analysis .................................... 795
Jaap H. Abbring and James J. Heckman
24.1.Introduction .......................................... 795
24.2.Policy Evaluation and Treatment Effects ............... 796
24.2.1.The Evaluation Problem ......................... 796
24.2.2.The Treatment Effect Approach .................. 800
24.2.3.Dynamic Policy Evaluation ...................... 801
24.3.Dynamic Treatment Effects and Sequential
Randomization ......................................... 803
24.3.1.Dynamic Treatment Effects ...................... 803
24.3.2.Policy Evaluation and Dynamic Discrete-
Choice Analysis ................................ 810
24.3.3.The Information Structure of Policies .......... 813
24.3.4.Selection on Unobservables ..................... 815
24.4.The Event-History Approach to Policy Analysis ......... 816
24.4.1.Treatment Effects in Duration Models ........... 817
24.4.2.Treatment Effects in More General Event-
History Models ................................. 823
24.4.3.A Structural Perspective ............................ 828
24.5.Dynamic Discrete Choice and Dynamic Treatment
Effects ............................................... 829
24.5.1.Semi-parametric Duration Models and
Counterfactuals ................................ 831
24.5.2.A Sequential Structural Model with Option
Values ......................................... 844
24.5.3.Identification at Infinity ..................... 850
24.5.4.Comparing Reduced-Form and Structural Models ... 851
24.5.5.A Short Survey of Dynamic Discrete-Choice
Models ......................................... 853
24.6.Conclusion ............................................ 857
References ................................................. 857
25.Econometrics of Individual Labor Market Transitions ........ 865
Denis Fougere and Thierry Kamionka
25.1.Introduction .......................................... 865
25.2.Multi-spell Multi-state Models ........................ 867
25.2.1.General framework .............................. 867
25.2.2.Non-parametric and Parametric Estimation ....... 872
25.2.3.Unobserved Heterogeneity ....................... 878
25.3.Markov Processes Using Discrete-Time Observations ..... 882
25.3.1.The Time-Homogeneous Markovian Model ........... 883
25.3.2.The Mover-Stayer Model ......................... 893
25.4.Concluding Remarks .................................... 901
References ................................................. 902
26.Software Review ............................................ 907
Pierre Blanchard
26.1.Introduction .......................................... 907
26.2.General-Purpose Econometric Packages .................. 908
26.2.1.EViews (v.5.1) ................................. 908
26.2.2.LIMDEP (v.8) with NLOGIT (v.3) ................. 912
26.2.3.RATS (v.6) ..................................... 916
26.2.4.SAS (v.9.1) .................................... 920
26.2.5.Stata (v.9) .................................... 923
26.2.6.TSP (v.5) ...................................... 927
26.3.High-Level Matrix Programming Languages ............... 930
26.3.1.GAUSS (v.5) .................................... 930
26.3.2.Ox (v.3.4) ..................................... 936
26.4.Performance Hints and Numerical Accuracy
Evaluation ............................................ 941
26.4.1.Speed Comparison ............................... 941
26.4.2.Numerical Accuracy Evaluations ................. 944
References ................................................. 949
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