Econometric analysis of cross section and panel data:
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2010]
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| Edition: | Second edition |
| Subjects: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020421953&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Physical Description: | xxvii, 1064 Seiten |
| ISBN: | 9780262232586 |
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| 245 | 1 | 0 | |a Econometric analysis of cross section and panel data |c Jeffrey M. Wooldridge |
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| adam_text | Titel: Econometric analysis of cross section and panel data
Autor: Wooldridge, Jeffrey M.
Jahr: 2010
Contents
Preface xxi
Acknowledgments xxix
I INTRODUCTION AND BACKGROUND 1
1 Introduction 3
1.1 Causal Relationships and Ceteris Paribus Analysis 3
1.2 Stochastic Setting and Asymptotic Analysis 4
1.2.1 Data Structures 4
1.2.2 Asymptotic Analysis 7
1.3 Some Examples 7
1.4 Why Not Fixed Explanatory Variables? 9
2 Conditional Expectations and Related Concepts in Econometrics 13
2.1 Role of Conditional Expectations in Econometrics 13
2.2 Features of Conditional Expectations 14
2.2.1 Definition and Examples 14
2.2.2 Partial Effects, Elasticities, and Semielasticities 15
2.2.3 Error Form of Models of Conditional Expectations 18
2.2.4 Some Properties of Conditional Expectations 19
2.2.5 Average Partial Effects 22
2.3 Linear Projections 25
Problems 27
Appendix 2A 30
2.A. 1 Properties of Conditional Expectations 30
2.A.2 Properties of Conditional Variances and Covariances 32
2.A.3 Properties of Linear Projections 34
3 Basic Asymptotic Theory 37
3.1 Convergence of Deterministic Sequences 37
3.2 Convergence in Probability and Boundedness in Probability 38
3.3 Convergence in Distribution 40
3.4 Limit Theorems for Random Samples 41
3.5 Limiting Behavior of Estimators and Test Statistics 42
3.5.1 Asymptotic Properties of Estimators 42
3.5.2 Asymptotic Properties of Test Statistics 45
Problems 47
vi Contents
Ð LINEAR MODELS 51
4 Single-Equation Linear Model and Ordinary Least Squares
Estimation 53
4.1 Overview of the Single-Equation Linear Model 53
4.2 Asymptotic Properties of Ordinary Least Squares 55
4.2.1 Consistency 56
4.2.2 Asymptotic Inference Using Ordinary Least Squares 59
4.2.3 Heteroskedasticity-Robust Inference 60
4.2.4 Lagrange Multiplier (Score) Tests 62
4.3 Ordinary Least Squares Solutions to the Omitted Variables Problem 65
4.3.1 Ordinary Least Squares Ignoring the Omitted Variables 65
4.3.2 Proxy Variable-Ordinary Least Squares Solution 67
4.3.3 Models with Interactions in Unobservables: Random
Coefficient Models 73
4.4 Properties of Ordinary Least Squares under Measurement Error 76
4.4.1 Measurement Error in the Dependent Variable 76
4.4.2 Measurement Error in an Explanatory Variable 78
Problems 82
5 Instrumental Variables Estimation of Single-Equation Linear Models 89
5.1 Instrumental Variables and Two-Stage Least Squares 89
5.1.1 Motivation for Instrumental Variables Estimation 89
5.1.2 Multiple Instruments: Two-Stage Least Squares 96
5.2 General Treatment of Two-Stage Least Squares 98
5.2.1 Consistency 98
5.2.2 Asymptotic Normality of Two-Stage Least Squares 101
5.2.3 Asymptotic Efficiency of Two-Stage Least Squares 103
5.2.4 Hypothesis Testing with Two-Stage Least Squares 104
5.2.5 Heteroskedasticity-Robust Inference for Two-Stage Least
Squares 106
5.2.6 Potential Pitfalls with Two-Stage Least Squares 107
5.3 IV Solutions to the Omitted Variables and Measurement Error
Problems 112
5.3.1 Leaving the Omitted Factors in the Error Term 112
5.3.2 Solutions Using Indicators of the Unobservables 112
Problems 115
Contents
6 Additional Single-Equation Topics 123
6.1 Estimation with Generated Regressors and Instruments 123
6.1.1 Ordinary Least Squares with Generated Regressors 123
6.1.2 Two-Stage Least Squares with Generated Instruments 124
6.1.3 Generated Instruments and Regressors 125
6.2 Control Function Approach to Endogeneity 126
6.3 Some Specification Tests 129
6.3.1 Testing for Endogeneity 129
6.3.2 Testing Overidentifying Restrictions 134
6.3.3 Testing Functional Form 137
6.3.4 Testing for Heteroskedasticity 138
6.4 Correlated Random Coefficient Models 141
6.4.1 When Is the Usual IV Estimator Consistent? 142
6.4.2 Control Function Approach 145
6.5 Pooled Cross Sections and Difference-in-Difierences Estimation 146
6.5.1 Pooled Cross Sections over Time 146
6.5.2 Policy Analysis and Difference-in-Differences Estimation 147
Problems 152
Appendix 6A 157
7 Estimating Systems of Equations by Ordinary Least Squares and
Generalized Least Squares 161
7.1 Introduction 161
7.2 Some Examples 161
7.3 System Ordinary Least Squares Estimation of a Multivariate Linear
System 166
7.3.1 Preliminaries 166
7.3.2 Asymptotic Properties of System Ordinary Least Squares 167
7.3.3 Testing Multiple Hypotheses 172
7.4 Consistency and Asymptotic Normality of Generalized Least Squares 173
7.4.1 Consistency 173
7.4.2 Asymptotic Normality 175
7.5 Feasible Generalized Least Squares 176
7.5.1 Asymptotic Properties 176
7.5.2 Asymptotic Variance of Feasible Generalized Least Squares
under a Standard Assumption 180
viii Contents
7.5.3 Properties of Feasible Generalized Least Squares with
(Possibly Incorrect) Restrictions on the Unconditional
Variance Matrix 182
7.6 Testing the Use of Feasible Generalized Least Squares 183
7.7 Seemingly Unrelated Regressions, Revisited 185
7.7.1 Comparison between Ordinary Least Squares and Feasible
Generalized Least Squares for Seemingly Unrelated
Regressions Systems 185
7.7.2 Systems with Cross Equation Restrictions 188
7.7.3 Singular Variance Matrices in Seemingly Unrelated
Regressions Systems 189
7.8 Linear Panel Data Model, Revisited 191
7.8.1 Assumptions for Pooled Ordinary Least Squares 191
7.8.2 Dynamic Completeness 194
7.8.3 Note on Time Series Persistence 196
7.8.4 Robust Asymptotic Variance Matrix 197
7.8.5 Testing for Serial Correlation and Heteroskedasticity after
Pooled Ordinary Least Squares 198
7.8.6 Feasible Generalized Least Squares Estimation under Strict
Exogeneity 200
Problems 202
8 System Estimation by Instrumental Variables 207
8.1 Introduction and Examples 207
8.2 General Linear System of Equations 210
8.3 Generalized Method of Moments Estimation 213
8.3.1 General Weighting Matrix 213
8.3.2 System Two-Stage Least Squares Estimator 216
8.3.3 Optimal Weighting Matrix 217
8.3.4 The Generalized Method of Moments Three-Stage Least
Squares Estimator 219
8.4 Generalized Instrumental Variables Estimator 222
8.4.1 Derivation of the Generalized Instrumental Variables
Estimator and Its Asymptotic Properties 222
8.4.2 Comparison of Generalized Method of Moment,
Generalized Instrumental Variables, and the Traditional
Three-Stage Least Squares Estimator 224
Contents ix
8.5 Testing Using Generalized Method of Moments 226
8.5.1 Testing Classical Hypotheses 226
8.5.2 Testing Overidentification Restrictions 228
8.6 More Efficient Estimation and Optimal Instruments 229
8.7 Summary Comments on Choosing an Estimator 232
Problems 233
9 Simultaneous Equations Models 239
9.1 Scope of Simultaneous Equations Models 239
9.2 Identification in a Linear System 241
9.2.1 Exclusion Restrictions and Reduced Forms 241
9.2.2 General Linear Restrictions and Structural Equations 245
9.2.3 Unidentified, Just Identified, and Overidentified Equations 251
9.3 Estimation after Identification 252
9.3.1 Robustness-Efficiency Trade-off 252
9.3.2 When Are 2SLS and 3SLS Equivalent? 254
9.3.3 Estimating the Reduced Form Parameters 255
9.4 Additional Topics in Linear Simultaneous Equations Methods 256
9.4.1 Using Cross Equation Restrictions to Achieve Identification 256
9.4.2 Using Covariance Restrictions to Achieve Identification 257
9.4.3 Subtleties Concerning Identification and Efficiency in Linear
Systems 260
9.5 Simultaneous Equations Models Nonlinear in Endogenous
Variables 262
9.5.1 Identification 262
9.5.2 Estimation 266
9.5.3 Control Function Estimation for Triangular Systems 268
9.6 Different Instruments for Different Equations 271
Problems 273
10 Basic Linear Unobserved Effects Panel Data Models 281
10.1 Motivation: Omitted Variables Problem 281
10.2 Assumptions about the Unobserved Effects and Explanatory
Variables 285
10.2.1 Random or Fixed Effects? 285
10.2.2 Strict Exogeneity Assumptions on the Explanatory Variables 287
10.2.3 Some Examples of Unobserved Effects Panel Data Models 289
Contents
10.3 Estimating Unobserved Effects Models by Pooled Ordinary Least
Squares 291
10.4 Random Effects Methods 291
10.4.1 Estimation and Inference under the Basic Random Effects
Assumptions 291
10.4.2 Robust Variance Matrix Estimator 297
10.4.3 General Feasible Generalized Least Squares Analysis 298
10.4.4 Testing for the Presence of an Unobserved Effect 299
10.5 Fixed Effects Methods 300
10.5.1 Consistency of the Fixed Effects Estimator 300
10.5.2 Asymptotic Inference with Fixed Effects 304
10.5.3 Dummy Variable Regression 307
10.5.4 Serial Correlation and the Robust Variance Matrix
Estimator 310
10.5.5 Fixed Effects Generalized Least Squares 312
10.5.6 Using Fixed Effects Estimation for Policy Analysis 315
10.6 First Differencing Methods 315
10.6.1 Inference 315
10.6.2 Robust Variance Matrix 318
10.6.3 Testing for Serial Correlation 319
10.6.4 Policy Analysis Using First Differencing 320
10.7 Comparison of Estimators 321
10.7.1 Fixed Effects versus First Differencing 321
10.7.2 Relationship between the Random Effects and Fixed
Effects Estimators 326
10.7.3 Hausman Test Comparing Random Effects and Fixed
Effects Estimators 328
Problems 334
11 More Topics in Linear Unobserved Effects Models 345
11.1 Generalized Method of Moments Approaches to the Standard
Linear Unobserved Effects Model 345
11.1.1 Equivalance between GMM 3SLS and Standard Estimators 345
11.1.2 Chamberlain s Approach to Unobserved Effects Models 347
11.2 Random and Fixed Effects Instrumental Variables Methods 349
11.3 Hausman and Taylor-Type Models 358
11.4 First Differencing Instrumental Variables Methods 361
Contents xi
11.5 Unobserved Effects Models with Measurement Error 365
11.6 Estimation under Sequential Exogeneity 368
11.6.1 General Framework 368
11.6.2 Models with Lagged Dependent Variables 371
11.7 Models with Individual-Specific Slopes 374
11.7.1 Random Trend Model 375
11.7.2 General Models with Individual-Specific Slopes 377
11.7.3 Robustness of Standard Fixed Effects Methods 382
11.7.4 Testing for Correlated Random Slopes 384
Problems · 387
?? GENERAL APPROACHES TO NONLINEAR ESTIMATION 395
12 M-Estimation, Nonlinear Regression, and Quantité Regression 397
12.1 Introduction 397
12.2 Identification, Uniform Convergence, and Consistency 401
12.3 Asymptotic Normality 405
12.4 Two-Step M-Estimators 409
12.4.1 Consistency 410
12.4.2 Asymptotic Normality 411
12.5 Estimating the Asymptotic Variance 413
12.5.1 Estimation without Nuisance Parameters 413
12.5.2 Adjustments for Two-Step Estimation 418
12.6 Hypothesis Testing 420
12.6.1 Wald Tests 420
12.6.2 Score (or Lagrange Multiplier) Tests 421
12.6.3 Tests Based on the Change in the Objective Function 428
12.6.4 Behavior of the Statistics under Alternatives 430
12.7 Optimization Methods 431
12.7.1 Newton-Raphson Method 432
12.7.2 Berndt, Hall, Hall, and Hausman Algorithm 433
12.7.3 Generalized Gauss-Newton Method 434
12.7.4 Concentrating Parameters out of the Objective Function 435
12.8 Simulation and Resampling Methods 436
12.8.1 Monte Carlo Simulation 436
12.8.2 Bootstrapping 438
xii Contents
12.9 Multivariate Nonlinear Regression Methods 442
12.9.1 Multivariate Nonlinear Least Squares 442
12.9.2 Weighted Multivariate Nonlinear Least Squares 444
12.10 Quantile Estimation 449
12.10.1 Quantiles, the Estimation Problem, and Consistency 449
12.10.2 Asymptotic Inference 454
12.10.3 Quantile Regression for Panel Data 459
Problems 462
13 Maximum Likelihood Methods 469
13.1 Introduction 469
13.2 Preliminaries and Examples 470
13.3 General Framework for Conditional Maximum Likelihood
Estimation 473
13.4 Consistency of Conditional Maximum Likelihood Estimation 475
13.5 Asymptotic Normality and Asymptotic Variance Estimation 476
13.5.1 Asymptotic Normality 476
13.5.2 Estimating the Asymptotic Variance 479
13.6 Hypothesis Testing 481
13.7 Specification Testing 482
13.8 Partial (or Pooled) Likelihood Methods for Panel Data 485
13.8.1 Setup for Panel Data 486
13.8.2 Asymptotic Inference 490
13.8.3 Inference with Dynamically Complete Models 492
13.9 Panel Data Models with Unobserved Effects 494
13.9.1 Models with Strictly Exogenous Explanatory Variables 494
13.9.2 Models with Lagged Dependent Variables 497
13.10 Two-Step Estimators Involving Maximum Likelihood 499
13.10.1 Second-Step Estimator Is Maximum Likelihood Estimator 499
13.10.2 Surprising Efficiency Result When the First-Step
Estimator Is Conditional Maximum Likelihood Estimator 500
13.11 Quasi-Maximum Likelihood Estimation 502
13.11.1 General Misspecification 502
13.11.2 Model Selection Tests 505
13.11.3 Quasi-Maximum Likelihood Estimation in the Linear
Exponential Family 509
Contents xiii
13.11.4 Generalized Estimating Equations for Panel Data 514
Problems 517
Appendix 13A 522
14 Generalized Method of Moments and Minimum Distance Estimation 525
14.1 Asymptotic Properties of Generalized Method of Moments 525
14.2 Estimation under Orthogonality Conditions 530
14.3 Systems of Nonlinear Equations 532
14.4 Efficient Estimation 538
14.4.1 General Efficiency Framework 538
14.4.2 Efficiency of Maximum Likelihood Estimator 540
14.4.3 Efficient Choice of Instruments under Conditional Moment
Restrictions 542
14.5 Classical Minimum Distance Estimation 545
14.6 Panel Data Applications 547
14.6.1 Nonlinear Dynamic Models 547
14.6.2 Minimum Distance Approach to the Unobserved Effects
Model 549
14.6.3 Models with Time-Varying Coefficients on the Unobserved
Effects 551
Problems 555
Appendix 14A 558
IV NONLINEAR MODELS AND RELATED TOPICS 559
15 Binary Response Models 561
15.1 Introduction 561
15.2 Linear Probability Model for Binary Response 562
15.3 Index Models for Binary Response: Probit and Logit 565
15.4 Maximum Likelihood Estimation of Binary Response Index
Models 567
15.5 Testing in Binary Response Index Models 569
15.5.1 Testing Multiple Exclusion Restrictions 570
15.5.2 Testing Nonlinear Hypotheses about â 571
15.5.3 Tests against More General Alternatives 571
xiv Contents
15.6 Reporting the Results for Probit and Logit 573
15.7 Specification Issues in Binary Response Models 582
15.7.1 Neglected Heterogeneity 582
15.7.2 Continuous Endogenous Explanatory Variables 585
15.7.3 Binary Endogenous Explanatory Variable 594
15.7.4 Heteroskedasticity and Nonnormality in the Latent
Variable Model 599
15.7.5 Estimation under Weaker Assumptions 604
15.8 Binary Response Models for Panel Data 608
15.8.1 Pooled Probit and Logit 609
15.8.2 Unobserved Effects Probit Models under Strict Exogeneity 610
15.8.3 Unobserved Effects Logit Models under Strict Exogeneity 619
15.8.4 Dynamic Unobserved Effects Models 625
15.8.5 Probit Models with Heterogeneity and Endogenous
Explanatory Variables 630
15.8.6 Semiparametric Approaches 632
Problems 635
16 Multinomial and Ordered Response Models 643
16.1 Introduction 643
16.2 Multinomial Response Models 643
16.2.1 Multinomial Logit 643
16.2.2 Probabilistic Choice Models 646
16.2.3 Endogenous Explanatory Variables 651
16.2.4 Panel Data Methods 653
16.3 Ordered Response Models 655
16.3.1 Ordered Logit and Ordered Probit 655
16.3.2 Specification Issues in Ordered Models 658
16.3.3 Endogenous Explanatory Variables 660
16.3.4 Panel Data Methods 662
Problems 663
17 Corner Solution Responses 667
17.1 Motivation and Examples 667
17.2 Useful Expressions for Type I Tobit 671
Contents xv
17.3 Estimation and Inference with the Type I Tobit Model 676
17.4 Reporting the Results 677
17.5 Specification Issues in Tobit Models 680
17.5.1 Neglected Heterogeneity 680
17.5.2 Endogenous Explanatory Models 681
17.5.3 Heteroskedasticity and Nonnormality in the Latent
Variable Model 685
17.5.4 Estimating Parameters with Weaker Assumptions 687
17.6 Two-Part Models and Type II Tobit for Corner Solutions 690
17.6.1 Truncated Normal Hurdle Model 692
17.6.2 Lognormal Hurdle Model and Exponential Conditional
Mean 694
17.6.3 Exponential Type II Tobit Model 697
17.7 Two-Limit Tobit Model 703
17.8 Panel Data Methods 705
17.8.1 Pooled Methods 705
17.8.2 Unobserved Effects Models under Strict Exogeneity 707
17.8.3 Dynamic Unobserved Effects Tobit Models 713
Problems 715
18 Count, Fractional, and Other Nonnegative Responses 723
18.1 Introduction 723
18.2 Poisson Regression 724
18.2.1 Assumptions Used for Poisson Regression and Quantities
of Interest 724
18.2.2 Consistency of the Poisson QMLE 727
18.2.3 Asymptotic Normality of the Poisson QMLE 728
18.2.4 Hypothesis Testing 732
18.2.5 Specification Testing 734
18.3 Other Count Data Regression Models 736
18.3.1 Negative Binomial Regression Models 736
18.3.2 Binomial Regression Models 739
18.4 Gamma (Exponential) Regression Model 740
18.5 Endogeneity with an Exponential Regression Function 742
18.6 Fractional Responses 748
xvi Contents
18.6.1 Exogenous Explanatory Variables 748
18.6.2 Endogenous Explanatory Variables 753
18.7 Panel Data Methods 755
18.7.1 Pooled QMLE 756
18.7.2 Specifying Models of Conditional Expectations with
Unobserved Effects 758
18.7.3 Random Effects Methods 759
18.7.4 Fixed Effects Poisson Estimation 762
18.7.5 Relaxing the Strict Exogeneity Assumption 764
18.7.6 Fractional Response Models for Panel Data 766
Problems 769
19 Censored Data, Sample Selection, and Attrition 777
19.1 Introduction 777
19.2 Data Censoring 778
19.2.1 Binary Censoring 780
19.2.2 Interval Coding 783
19.2.3 Censoring from Above and Below 785
19.3 Overview of Sample Selection 790
19.4 When Can Sample Selection Be Ignored? 792
19.4.1 Linear Models: Estimation by OLS and 2SLS 792
19.4.2 Nonlinear Models 798
19.5 Selection on the Basis of the Response Variable: Truncated
Regression 799
19.6 Incidental Truncation: A Probit Selection Equation 802
19.6.1 Exogenous Explanatory Variables 802
19.6.2 Endogenous Explanatory Variables 809
19.6.3 Binary Response Model with Sample Selection 813
19.6.4 An Exponential Response Function 814
19.7 Incidental Truncation: A Tobit Selection Equation 815
19.7.1 Exogenous Explanatory Variables 815
19.7.2 Endogenous Explanatory Variables 817
19.7.3 Estimating Structural Tobit Equations with Sample
Selection 819
19.8 Inverse Probability Weighting for Missing Data 821
Contents xvii
19.9 Sample Selection and Attrition in Linear Panel Data Models 827
19.9.1 Fixed and Random Effects Estimation with Unbalanced
Panels 828
19.9.2 Testing and Correcting for Sample Selection Bias 832
19.9.3 Attrition 837
Problems 845
20 Stratified Sampling and Cluster Sampling 853
20.1 Introduction 853
20.2 Stratified Sampling 854
20.2.1 Standard Stratified Sampling and Variable Probability
Sampling 854
20.2.2 Weighted Estimators to Account for Stratification 856
20.2.3 Stratification Based on Exogenous Variables 861
20.3 Cluster Sampling 863
20.3.1 Inference with a Large Number of Clusters and Small
Cluster Sizes 864
20.3.2 Cluster Samples with Unit-Specific Panel Data 876
20.3.3 Should We Apply Cluster-Robust Inference with Large
Group Sizes? 883
20.3.4 Inference When the Number of Clusters Is Small 884
20.4 Complex Survey Sampling 894
Problems 899
21 Estimating Average Treatment Effects 903
21.1 Introduction 903
21.2 A Counterfactual Setting and the Self-Selection Problem 904
21.3 Methods Assuming Ignorability (or Unconfoundedness) of
Treatment 908
21.3.1 Identification 911
21.3.2 Regression Adjustment 915
21.3.3 Propensity Score Methods 920
21.3.4 Combining Regression Adjustment and Propensity Score
Weighting 930
21.3.5 Matching Methods 934
xviii Contents
21.4 Instrumental Variables Methods 937
21.4.1 Estimating the Average Treatment Effect Using IV 937
21.4.2 Correction and Control Function Approaches 945
21.4.3 Estimating the Local Average Treatment Effect by IV 951
21.5 Regression Discontinuity Designs 954
21.5.1 The Sharp Regression Discontinuity Design 954
21.5.2 The Fuzzy Regression Discontinuity Design 957
21.5.3 Unconfoundedness versus the Fuzzy Regression
Discontinuity 959
21.6 Further Issues 960
21.6.1 Special Considerations for Responses with Discreteness or
Limited Range 960
21.6.2 Multivalued Treatments 961
21.6.3 Multiple Treatments 964
21.6.4 Panel Data 968
Problems 975
22 Duration Analysis 983
22.1 Introduction 983
22.2 Hazard Functions 984
22.2.1 Hazard Functions without Covariates 984
22.2.2 Hazard Functions Conditional on Time-Invariant
Covariates 988
22.2.3 Hazard Functions Conditional on Time-Varying
Covariates 989
22.3 Analysis of Single-Spell Data with Time-Invariant Covariates 991
22.3.1 Flow Sampling 992
22.3.2 Maximum Likelihood Estimation with Censored Flow
Data 993
22.3.3 Stock Sampling 1000
22.3.4 Unobserved Heterogeneity 1003
22.4 Analysis of Grouped Duration Data 1010
22.4.1 Time-Invariant Covariates 1011
22.4.2 Time-Varying Covariates 1015
22.4.3 Unobserved Heterogeneity 1017
Contents xix
22.5
Further Issues 1018
22.5.1 Cox s Partial Likelihood Method for the Proportional
Hazard Model 1018
22.5.2 Multiple-Spell Data 1018
22.5.3 Competing Risks Models 1019
Problems 1019
References 1025
Index 1045
|
| any_adam_object | 1 |
| author | Wooldridge, Jeffrey M. 1960- |
| author_GND | (DE-588)131680463 |
| author_facet | Wooldridge, Jeffrey M. 1960- |
| author_role | aut |
| author_sort | Wooldridge, Jeffrey M. 1960- |
| author_variant | j m w jm jmw |
| building | Verbundindex |
| bvnumber | BV036499501 |
| callnumber-first | H - Social Science |
| callnumber-label | HB139 |
| callnumber-raw | HB139.W663 2002 |
| callnumber-search | HB139.W663 2002 |
| callnumber-sort | HB 3139 W663 42002 |
| callnumber-subject | HB - Economic Theory and Demography |
| classification_rvk | QH 300 QH 320 QH 230 QH 310 QH 244 SK 830 SK 980 |
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| dewey-full | 330/.01/519521 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330/.01/5195 21 |
| dewey-search | 330/.01/5195 21 |
| dewey-sort | 3330 11 45195 221 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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| id | DE-604.BV036499501 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T14:35:46Z |
| institution | BVB |
| isbn | 9780262232586 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020421953 |
| oclc_num | 705585553 |
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| physical | xxvii, 1064 Seiten |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | The MIT Press |
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| spellingShingle | Wooldridge, Jeffrey M. 1960- Econometric analysis of cross section and panel data Econometrics -- Asymptotic theory Mikroökonomie (DE-588)4039225-9 gnd Ökonometrie (DE-588)4132280-0 gnd Methode (DE-588)4038971-6 gnd Asymptotische Statistik (DE-588)4203167-9 gnd |
| subject_GND | (DE-588)4039225-9 (DE-588)4132280-0 (DE-588)4038971-6 (DE-588)4203167-9 (DE-588)4143389-0 |
| title | Econometric analysis of cross section and panel data |
| title_auth | Econometric analysis of cross section and panel data |
| title_exact_search | Econometric analysis of cross section and panel data |
| title_full | Econometric analysis of cross section and panel data Jeffrey M. Wooldridge |
| title_fullStr | Econometric analysis of cross section and panel data Jeffrey M. Wooldridge |
| title_full_unstemmed | Econometric analysis of cross section and panel data Jeffrey M. Wooldridge |
| title_short | Econometric analysis of cross section and panel data |
| title_sort | econometric analysis of cross section and panel data |
| topic | Econometrics -- Asymptotic theory Mikroökonomie (DE-588)4039225-9 gnd Ökonometrie (DE-588)4132280-0 gnd Methode (DE-588)4038971-6 gnd Asymptotische Statistik (DE-588)4203167-9 gnd |
| topic_facet | Econometrics -- Asymptotic theory Mikroökonomie Ökonometrie Methode Asymptotische Statistik Aufgabensammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020421953&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT wooldridgejeffreym econometricanalysisofcrosssectionandpaneldata |
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