CONTENTS
Preface = ⅴ
Contributors = xix
PART Ⅰ .ENDOGENOUS STRATIFICATION, SEMIPARAMETRIC AND NON-PARAMETRIC ESTIMATION
Ch. 1. Estimation from Endogenously Stratified Samples / S. R. Cosslett = 1
1. .Introduction = 1
2. Notation and definitions = 2
3. Sampling schemes and likelihood functions = 5
4. Estimators based on maximum likelihood = 11
5. Efficient estimation of parametric models = 17
6. Other sample designs = 24
7. Logistic regression = 29
8. Regression models with unknown error distribution = 31
9. Semiparametric models of discrete choice = 36
References = 41
Ch. 2. Semiparametric and Nonparametric Estimation of Quantal Response Models / J. L. Horowitz = 45
1. Introduction = 45
2. Parametric models = 46
3. Effects of misspecifying the distribution of U = 48
4. Identification = 50
5. Rates of convergence and asymptotic efficiency bounds = 57
6. Estimators = 60
7. Estimation from choice-based samples = 68
8. Applications = 69
9. Models for multinominal choice = 70
References = 70
Ch. 3. The Selection Problem in Econometrics and Statistics / C. F. Manski = 73
1. Introduction = 73
2. The selection problem in the absence of prior information = 74
3. The selection problem with prior information = 77
4. Conclusion = 82
References = 83
Ch. 4. General Non-parametric Regression Estimation and Testing in Econometrics / A. Ullah ; H. D. Vinod = 85
1. Introduction = 85
2. Non-parametric model and estimators = 88
3. Estimation of higher order moments and quantities = 93
4. Estimation of derivatives = 94
5. Estimation of error variance = 95
6. Non-parametric regression with a generalized Box-Cox transformation = 96
7. Finite sample properties = 99
8. Misspecification tests = 106
9. Applications = 109
Acknowledgment = 110
References = 110
PART Ⅱ. LIMITED-DEPENDENT VARIABLES
Ch. 5. Simultaneous Microeconometric Models with Censored or Qualitative Dependent Variables / R. W. Blundell ; R. J. Smith = 117
1. Introduction = 117
2. Models = 119
3. The type Ⅰ simultaneous equation model and some consistent estimation procedures = 121
4. Type Ⅱ simultaneous equation models and some consistent estimation procedures = 128
5. An Empirical application = 133
6. Summary and conclusions = 136
Appendix A = 136
Appendix B = 139
Appendix C = 140
Data appendix = 141
References = 142
Ch. 6. Multivariate Tobit Models in Econometrics / L. -F. Lee = 145
1. Introduction 145
2. Some multivariate tobit models = 146
3. Some convex programming models = 149
4. Model coherency in simultaneous equation models = 153
5. Estimation methods = 156
6. Specification error tests = 168
Acknowledgment = 170
References = 170
Ch. 7. Estimation of Limited Dependent Variable Models under Rational Expectations / G. S. Maddala = 175
1. Introduction = 175
2. The tobit model = 175
3. The friction model = 177
4. The disequilibrium model = 178
5. Uniqueness of the rational expectations solution = 183
6. Estimation methods = 184
7. Extensions of the disequilibrium model = 186
8. Applications to exchange rates = 189
9. Prediction problems and policy issues = 191
10. Concluding remarks = 192
Acknowledgment = 192
References = 193
PART Ⅲ. TIME = SERIES
Ch. 8. Nonlinear Time Series and Macroeconometrics / W. A. Brock ; S. M. Potter = 195
1. Introduction = 195
2. Chaos and stochastic nonlinearity = 196
3. The BDS test = 200
4. Tests for nonlinearity = 210
5. Evidence of nonlinearity and chaos = 219
6. Summary and conclusions = 224
Acknowledgment = 225
References = 225
Ch. 9. Estimation, Inference and Forecasting of Time Series Subject to Changes in Regime / J. D. Hamilton = 231
1. Introduction = 231
2. Inferences about the unobserved state = 237
3. Maximum likelihood estimation = 244
4. Forecasting and rational-expectations econometrics = 252
5. Applications = 256
Acknowledgment = 258
References = 259
Ch. 10. Structural Time Series Models / A. C. Harvey ; N. Shephard = 261
1. Introduction = 261
2. Linear state space models and the Kalman filter = 267
3. Explanatory variables = 274
4. Multivariate time series models = 276
5. Simultaneous equation system = 283
6. Nonlinear and non-Gaussian models = 288
References = 299
PART Ⅳ. LIKELIHOOD METHODS AND BAYESIAN INFERENCE
Ch. 11. Bayesian Testing and Testing Bayesians / J. -P. Florens ; M. Mouchart = 303
1. Introduction = 303
2. Bayesian testing = 305
3. Testing Bayesians = 318
4. Other contributions = 330
Acknowledgment = 331
References = 331
Ch. 12. Pseudo-Likelihood Methods / C. Gourieroux ; A. Monfort = 335
1. Introduction = 335
2. General presentation of the pseudo-likelihood methods = 336
3. Pseudo-maximum likelihood methods of order one (PMLl) = 339
4. Quasi-generalized PML methods = 344
5. Pseudo-maximum likelihood estimators of order two (PML2) = 346
6. Hypothesis testing = 347
7. Simulated PML methods = 350
8. Pseudo-likelihood methods and non-nested hypotheses = 354
9. Model selection = 358
10. Concluding remarks = 359
References = 360
Ch. 13. Rao's Score Test : Recent Asymptotic Results / R. Mukerjee = 363
1. Historical introduction' to Rao's score test = 363
2.Comparison of higher-order power = 364
3.Bartlett-type adjustments = 375
4.Concluding remarks = 377
References = 377
PART Ⅴ ALTERNATIVES TO LIKELIHOOD METHODS
Ch. 14. On the Strong Consistency of M-Estimates in Linear Models under a General Discrepancy Function / Z. D. Bai ; Z. J. Liu ; C. R. Rao = 381
1. Introduction = 381
2. Main results = 382
3. Proof of main results = 384
Appendix = 388
Acknowledgement = 390
References = 390
Ch. 15. Some Aspects of Generalized Method of Moments Estimation / A. Hall = 393
1. Introduction = 393
2. Instrumental variables estimation in the linear model = 395
3. Generalized method of moments = 399
4. GMM and Euler equation models = 404
5. Further issues concerning GMM based inference = 407
6. Concluding remarks = 413
Acknowledgment = 415
References = 415
Ch. 16. Efficient Estimation of Models with Conditional Moment Restrictions / W. K. Newey = 419
1. Introduction = 419
2. Conditional moment restrictions and instrumental variables estimation = 421
3. Examples = 425
4. Nearest neighbor estimation of the optimal instruments = 430
5. Series approximation of the optimal instruments = 435
6. Sampling experiment for the heteroskedastic linear model = 441
Appendix = 444
Acknowledgment = 452
References = 453
Ch. 17. Generalized Method of Moments : Econometrics Applications / M. Ogaki = 455
1. Introduction = 455
2. Generalized methods of moments = 456
3. Special cases = 458
4. Extensions = 459
5. Important assumptions = 463
6. Covariance matrix estimation = 464
7. Hypothesis testing and specification tests = 469
8. Empirical applications = 470
9. Further issues = 479
10. Concluding remarks = 481
Acknowledgment = 482
References = 482
Ch. 18. Testing for Heteroskedasticity / A. R. Pagan ; Y. Pak = 489
1. Introduction = 489
2. Conditional moment tests and their properties = 491
3. Testing heteroskedasticity in the regression model = 497
4. Testing for heteroskedasticity in models featuring heteroskedasticity = 504
5. The size and power of heteroskedasticity tests = 511
6. Conclusion = 515
Acknowledgment = 515
References = 515
PART VI. COMPUTER-INTENSIVE METHODS
Ch. 19. Simulation Estimation Methods for Limited Dependent Variable Models / V. A. Hajivassiliou = 519
1. Introduction = 519
2. Developments of simulation techniques for the estimation of LDV models = 521
3. Simulation estimation methods for LDV models = 524
4. Simulators for l, ∂ l／ ∂ θ and ∂ ln l／ ∂ θ = 531
5. Conclusion = 539
Acknowledgment = 540
References = 540
Ch. 20. Simulation Estimation for Panel Data Models with Limited Dependent Variables / M. P. Keane = 545
1. Introduction = 545
2. Methods for estimating panel data models with serial correlation in the linear case = 546
3. The problem of estimating LDV models with serial correlation = 547
4. MSM estimation for LDV models = 548
5. Practical simulation estimators for the panel data probit model = 550
6. Extensions to more general models = 559
7. Estimating the serial correlation structure in employment and wage data = 562
8. Conclusion = 569
References = 570
Ch. 21.A Perspective on Application of Bootstrap Methods in Econometrics / J. Jeong ; G. S. Maddala = 573
1. Introduction = 573
2. Bootstrap methods with III) errors = 576
3. Computational advances in bootstrap methods = 582
4. Bootstrap methods with non-1113 errors : Applications in econometrics = 585
5. Conclusions = 601
Acknowledgement = 603
References = 603
Ch. 22.Stochastic Simulations for Inference in Nonlinear Errors-in-Variables Models / R. S. Mariano ; B. W. Brown = 611
1. Introduction and summary = 611
2. Probit errors-in-variables = 613
3. Tobit errors-in-variables = 616
4. Inference in nonlinear systems with errors in variables = 617
5 Concluding remarks = 623
References = 623
Ch. 23. Bootstrap Methods : Applications in Econometrics / H. D. Vinod = 629
1. Introduction and motivation for resampling methods in econometrics = 629
2. Econometrics applications of the bootstrap = 630
3. Definition and properties of the bootstrap in the regression context = 633
4. Definition and properties of the jackknife in the regression context = 636
5. The pivot and the bootstrap sampling distribution for a biased estimator = 637
6. Improved estimation of the bias in the regression context by the post hoc method = 645
7. Efron's bias-corrected accelerated BC. interval = 646
8. Confidence interval : Coverage correction and adjustments = 649
9. Practical bootstrap computations from sorted re-sampling estimates = 650
10. Post hoc computational method for confidence intervals = 652
11. Bootstrap for dynamic and simultaneous equation models in econometrics = 654
12. Final remarks = 656
Acknowledgment = 656
References = 657
PART Ⅶ. OTHER PROBLEMS
Ch. 24. Identifying Outliers and Influential Observations in Econometrics Models / S. G. Donald ; G. S. Maddala = 663
1. Introduction = 663
2. Linear regression models = 664
3. Relationship between influence diagnostics, tests of linear hypotheses and specification sets = 673
4. What do we do with outliers = 676
5. Bayesian and decision theoretic approaches = 681
6. Generalizing the results = 683
7. Nonlinear regression models = 688
8. Limited dependent variable models = 691
9. Dynamic models and panel data = 696
10. Conclusion = 698
Acknowledgment = 698
References = 699
Ch. 25. Statistical Aspects of Calibration in Macroeconomics / A. W. Gregory ; G. W. Smith = 703
1. Introduction = 703
2. Background = 703
3. Estimation and calibration = 705
4. Model evaluation and testing = 712
5. Further topics in model evaluation = 715
6. Conclusion = 716
Acknowledgment = 717
References = 717
Ch. 26. Panel Data Models with Rational Expectations / K. Lahiri = 721
1. Introduction = 721
2. Efficient estimation = 723
3. Specification tests = 727
4. Simultaneous equations = 729
5. Empirical illustration = 730
6. Conclusion = 733
Data appendix = 734
Acknowledgment = 734
References = 735
Ch. 27. Continuous Time Financial Models : Statistical Applications of Stochastic Processes / K. R. Sawyer = 739
1. Introduction = 739
2. Theoretical issues = 742
3. Estimation and inference = 757
4. Concluding remarks = 762
References = 762
Subject Index = 765
Contents of Previous Volumes = 773
