Estimation and inference in econometrics:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York, NY [u.a.]
Oxford Univ. Press
1993
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| Subjects: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=004243936&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Physical Description: | XX, 875 S. graph. Darst. |
| ISBN: | 0195060113 9780195060119 |
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| adam_text | Contents
1 The Geometry of Least Squares 3
1.1 Introduction 3
1.2 The Geometry of Least Squares 4
1.3 Restrictions and Reparametrizations 16
1.4 The Frisch-Waugh-Lovell Theorem 19
1.5 Computing OLS Estimates 25
1.6 Influential Observations and Leverage 32
1.7 Further Reading and Conclusion 39
2 Nonlinear Regression Models and Nonlinear Least Squares 41
2.1 Introduction 41
2.2 The Geometry of Nonlinear Least Squares 43
2.3 Identification in Nonlinear Regression Models 48
2.4 Models and Data-Generating Processes 51
2.5 Linear and Nonlinear Regression Functions 55
2.6 Error Terms 58
2.7 Conclusion 64
3 Inference in Nonlinear Regression Models 66
3.1 Introduction 66
3.2 Covariance Matrix Estimation 67
3.3 Confidence Intervals and Confidence Regions 71
3.4 Hypothesis Testing: Introduction 78
3.5 Hypothesis Testing in Linear Regression Models 81
3.6 Hypothesis Testing in Nonlinear Regression Models 88
3.7 Restrictions and Pretest Estimators 94
3.8 Conclusion 98
4 Introduction to Asymptotic Theory and Methods 99
4.1 Introduction 99
4.2 Sequences, Limits, and Convergence 100
4.3 Rates of Convergence 108
4.4 Data-Generating Processes and Asymptotic Theory 113
4.5 Consistency and Laws of Large Numbers 118
4.6 Asymptotic Normality and Central Limit Theorems 125
4.7 Some Useful Results 130
4.8 Conclusion 137
XVI
Contents
5 Asymptotic Methods and Nonlinear Least Squares
5.1 Introduction
5.2 Asymptotic Identifiability
5.3 Consistency of the NLS Estimator
5.4 Asymptotic Normality of the NLS Estimator
5.5 Asymptotic Efficiency of Nonlinear Least Squares
5.6 Properties of Nonlinear Least Squares Residuals
5.7 Test Statistics Based on NLS Estimates
5.8 Further Reading and Conclusion
6 The Gauss-Newton Regression
6.1 Introduction
6.2 Computing Covariance Matrices
6.3 Collinearity in Nonlinear Regression Models
6.4 Testing Restrictions
6.5 Diagnostic Tests for Linear Regression Models
6.6 One-Step Efficient Estimation
6.7 Hypothesis Tests Using Any Consistent Estimates
6.8 Nonlinear Estimation Using the GNR
6.9 Further Reading
7 Instrumental Variables
7.1 Introduction
7.2 Errors in Variables
7.3 Simultaneous Equations
7.4 Instrumental Variables: The Linear Case
7.5 Two-Stage Least Squares
7.6 Instrumental Variables: The Nonlinear Case
7.7 Hypothesis Tests Based on the GNR
7.8 Identification and Overidentifying Restrictions
7.9 DurbinAVu-Hausman Tests
7.10 Conclusion
8 The Method of Maximum Likelihood
8Л Introduction
8.2 Fundamental Concepts and Notation
8.3 Transformations and Re paramétrez at ions
8.4 Consistency
8.5 The Asymptotic Distribution of the ML Estimator
8.6 The Information Matrix Equality
8.7 Concentrating the Loglikelihood Function
8.8 Asymptotic Efficiency of the ML Estimator
8.9 The Three Classical Test Statistics
8.10 Nonlinear Regression Models
8.11 Conclusion *
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Contents
хѵи
9 Maximum Likelihood and Generalized Least Squares 288
9.1 Introduction 288
9.2 Generalized Least Squares 289
9.3 The Geometry of GLS 292
9.4 The Gauss-Newton Regression 295
9.5 Feasible Generalized Least Squares 298
9.6 Maximum Likelihood and GNLS 301
9.7 Introduction to Multivariate Regression Models 305
9.8 GLS Estimation of Multivariate Regression Models 309
9.9 ML Estimation of Multivariate Regression Models 315
9.10 Modeling Time-Series/Cross-Section Data 320
9.11 Conclusion 325
10 Serial Correlation 327
10.1 Introduction 327
10.2 Serial Correlation and Least Squares Estimation 329
10.3 Estimating Regression Models with AR(1) Errors 331
10.4 Standard Errors and Covariance Matrices 338
10.5 Higher-Order AR Processes 341
10.6 Initial Observations in Models with AR Errors 343
10.7 Moving Average and ARMA Processes 351
10.8 Testing for Serial Correlation 357
10.9 Common Factor Restrictions 364
10.10 Instrumental Variables and Serial Correlation 369
10.11 Serial Correlation and Multivariate Models 371
10.12 Conclusion 373
11 Tests Based on the Gauss-Newton Regression 374
11.1 Introduction 374
11.2 Tests for Equality of Two Parameter Vectors 375
11.3 Testing Nonnested Regression Models 381
11.4 Tests Based on Comparing Two Sets of Estimates 389
11.5 Testing for Heteroskedasticity 396
11.6 A Heteroskedasticity-Robust Version of the GNR 399
11.7 Conclusion 402
12 Interpreting Tests in Regression Directions 403
12.1 Introduction 403
12.2 Size and Power 405
12.3 Drifting DGPs 409
12.4 The Asymptotic Distribution of Test Statistics 411
12.5 The Geometry of Test Power 415
12.6 Asymptotic Relative Efficiency 421
12.7 Interpreting Test Statistics that Reject the Null 423
xviii
Contents
12.8 Test Statistics that Do Not Reject the Null
12.9 Conclusion
428
433
13 The
13.1
13.2
13.3
13.4
13.5
13.6
13.7
13.8
Classical Hypothesis Tests
Introduction
The Geometry of the Classical Test Statistics
Asymptotic Equivalence of the Classical Tests
Classical Tests and Linear Regression Models
Alternative Covariance Matrix Estimators
Classical Test Statistics and Reparametrization
The Outer-Product-of-the-Gradient Regression
Further Reading and Conclusion
435
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14 Transforming the Dependent Variable
14.1 Introduction
14.2 The Box-Cox Transformation
14.3 The Role of Jacobian Terms in ML Estimation
14.4 Double-Length Artificial Regressions
14.5 The DLR and Models Involving Transformations
14.6 Testing Linear and Loglinear Regression Models
14.7 Other Transformations
14.8 Conclusion
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15 Qualitative and Limited Dependent Variables 511
15.1 Introduction 511
15.2 Binary Response Models 512
15.3 Estimation of Binary Response Models 517
15.4 An Artificial Regression 523
15.5 Models for More than Two Discrete Responses 529
15.6 Models for Truncated Data 534
15.7 Models for Censored Data 537
15.8 Sample Selectivity 542
15.9 Conclusion 545
18 Heteroskedasticity and Related Topics 547
16.1 Introduction 547
16.2 Least Squares and Heteroskedasticity 548
16.3 Covariance Matrix Estimation 552
16.4 Autoregressive Conditional Heteroskedasticity 556
16.5 Testing for Heteroskedasticity 560
16.6 Skedastic Directions and Regression Directions 564
16.7 Tests for Skewness and Excess Kurtosis 567
16.8 Conditional Moment Tests 571
16.9 Information Matrix Tests 57g
16.10 Conclusion t֊Q0
Contents xix
17 The Generalized Method of Moments 583
17.1 Introduction and Definitions 583
17.2 Criterion Functions and M-Estimators 587
17.3 Efficient GMM Estimators 597
17.4 Estimation with Conditional Moments 602
17.5 Covariance Matrix Estimation 607
17.6 Inference with GMM Models 614
17.7 Conclusion 620
18 Simultaneous Equations Models 622
18.1 Introduction 622
18.2 Exogeneity and Causality 624
18.3 Identification in Simultaneous Equations Models 631
18.4 Full-Information Maximum Likelihood 637
18.5 Limited-Information Maximum Likelihood 644
18.6 Three-Stage Least Squares 651
18.7 Nonlinear Simultaneous Equations Models 661
18.8 Conclusion 667
19 Regression Models for Time-Series Data 669
19.1 Introduction 669
19.2 Spurious Regressions 669
19.3 Distributed Lags 673
19.4 Dynamic Regression Models 680
19.5 Vector Autoregressions 684
19.6 Seasonal Adjustment 687
19.7 Modeling Seasonality 696
19.8 Conclusion 699
20 Unit Roots and Cointegration 700
20.1 Introduction 700
20.2 Testing for Unit Roots 702
20.3 Asymptotic Theory for Unit Root Tests 705
20.4 Serial Correlation and Other Problems 710
20.5 Cointegration 715
20.6 Testing for Cointegration 720
20.7 Model-Building with Cointegrated Variables 723
20.8 Vector Autoregressions and Cointegration 726
20.9 Conclusion 730
21 Monte Carlo Experiments 731
21.1 Introduction 731
21.2 Generating Pseudo-Random Numbers 734
21.3 Generating Pseudo-Random Variates 735
XX
Contents
21.4 Designing Monte Carlo Experiments 738
21.5 Variance Reduction: Antithetic Variates 744
21.6 Variance Reduction: Control Variates 747
21.7 Response Surfaces 755
21.8 The Bootstrap and Related Methods 763
21.9 Conclusion 768
Appendices
A Matrix Algebra 770
A.l Introduction 770
A.2 Elementary Facts about Matrices 770
A.3 The Geometry of Vectors 775
A.4 Matrices as Mappings of Linear Spaces 777
A.5 Partitioned Matrices 779
A.6 Determinants 782
A.7 Positive Definite Matrices 787
A. 8 Eigenvalues and Eigenvectors 789
B Results from Probability Theory 793
D.l Introduction 793
B. 2 Random Variables and Probability Distributions 793
B.3 Moments of Random Variables 797
B.4 Some Standard Probability Distributions 802
References g!2
Author Index 352
Subject Index g57
Corrections Via the Internet 375
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| indexdate | 2024-12-20T08:52:10Z |
| institution | BVB |
| isbn | 0195060113 9780195060119 |
| language | English |
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| publisher | Oxford Univ. Press |
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| spellingShingle | Davidson, Russell MacKinnon, James G. Estimation and inference in econometrics econometria - [manuale] tessin-TR Econometrics Schätzung (DE-588)4193791-0 gnd Statistik (DE-588)4056995-0 gnd Inferenzstatistik (DE-588)4247120-5 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4193791-0 (DE-588)4056995-0 (DE-588)4247120-5 (DE-588)4132280-0 (DE-588)4151278-9 |
| title | Estimation and inference in econometrics |
| title_auth | Estimation and inference in econometrics |
| title_exact_search | Estimation and inference in econometrics |
| title_full | Estimation and inference in econometrics Russell Davidson ; James G. MacKinnon |
| title_fullStr | Estimation and inference in econometrics Russell Davidson ; James G. MacKinnon |
| title_full_unstemmed | Estimation and inference in econometrics Russell Davidson ; James G. MacKinnon |
| title_short | Estimation and inference in econometrics |
| title_sort | estimation and inference in econometrics |
| topic | econometria - [manuale] tessin-TR Econometrics Schätzung (DE-588)4193791-0 gnd Statistik (DE-588)4056995-0 gnd Inferenzstatistik (DE-588)4247120-5 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | econometria - [manuale] Econometrics Schätzung Statistik Inferenzstatistik Ökonometrie Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=004243936&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT davidsonrussell estimationandinferenceineconometrics AT mackinnonjamesg estimationandinferenceineconometrics |
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| Call Number: |
0001 04.2003 A 7229
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| Copy 1 | Available for loan Ausgeliehen – Due: 31.12.9999 |
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| Call Number: |
0048 01.2004 A 257
Floor plan 0048 WIR 017f 2010 A 9834 Floor plan |
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| Copy 1 | On permanent loan Ausgeliehen – Due: 31.12.9999 |
| Copy 2 | On permanent loan Ausgeliehen – Due: 31.12.9999 |