A guide to modern econometrics:
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| Format: | Book |
| Language: | English |
| Published: |
Chichester
WILEY
2012
|
| Edition: | 4th edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. [459] - 475. - Hier auch später erschienene, unveränd. Nachdrucke |
| Physical Description: | XV, 497 Seiten Diagramme |
| ISBN: | 9781119951674 |
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| 100 | 1 | |a Verbeek, Marno |d 1965- |0 (DE-588)170802655 |4 aut | |
| 245 | 1 | 0 | |a A guide to modern econometrics |c Marno Verbeek |
| 250 | |a 4th edition | ||
| 264 | 1 | |a Chichester |b WILEY |c 2012 | |
| 300 | |a XV, 497 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| adam_text | Titel: A guide to modern econometrics
Autor: Verbeek, Marno
Jahr: 2012
Contents
Preface xiii
1 Introduction 1
1.1 About Econometrics 1
1.2 The Structure of this Book 3
1.3 Illustrations and Exercises 4
2 An Introduction to Linear Regression 6
2.1 Ordinary Least Squares as an Algebraic Tool 7
2.1.1 Ordinary Least Squares 7
2.1.2 Simple Linear Regression 9
2.1.3 Example: Individual Wages 10
2.1.4 Matrix Notation 11
2.2 The Linear Regression Model 12
2.3 Small Sample Properties of the OLS Estimator 15
2.3.1 The Gauss-Markov Assumptions 15
2.3.2 Properties of the OLS Estimator 16
2.3.3 Example: Individual Wages (Continued) 20
2.4 Goodness-of-fit 20
2.5 Hypothesis Testing 22
2.5.1 A Simple /-Test 23
2.5.2 Example: Individual Wages (Continued) 25
2.5.3 Testing One Linear Restriction 25
2.5.4 A Joint Test of Significance
of Regression Coefficients 26
2.5.5 Example: Individual Wages (Continued) 28
2.5.6 The General Case 29
2.5.7 Size, Power and p-Values 31
CONTENTS
2.6 Asymptotic Properties of the OLS Estimator 32
2.6.1 Consistency 32
2.6.2 Asymptotic Normality 34
2.6.3 Small Samples and Asymptotic Theory 36
2.7 Illustration: The Capital Asset Pricing Model 38
2.7.1 The CAPM as a Regression Model 39
2.7.2 Estimating and Testing the CAPM 40
2.7.3 The World s Largest Hedge Fund 42
2.8 Multicollinearity 43
2.8.1 Example: Individual Wages (Continued) 46
2.9 Missing Data, Outliers and Influential Observations 47
2.9.1 Outliers and Influential Observations 47
2.9.2 Robust Estimation Methods 49
2.9.3 Missing Observations 50
2.10 Prediction 52
Wrap-up 53
Exercises 54
Interpreting and Comparing Regression Models 58
3.1 Interpreting the Linear Model 58
3.2 Selecting the Set of Regressors 62
3.2.1 Misspecifying the Set of Regressors 62
3.2.2 Selecting Regressors 63
3.2.3 Comparing Non-nested Models 67
3.3 Misspecifying the Functional Form 70
3.3.1 Nonlinear Models 70
3.3.2 Testing the Functional Form 71
3.3.3 Testing for a Structural Break 71
3.4 Illustration: Explaining House Prices 72
3.5 Illustration: Predicting Stock Index Returns 76
3.5.1 Model Selection 76
3.5.2 Forecast Evaluation 79
3.6 Illustration: Explaining Individual Wages 81
3.6.1 Linear Models 81
3.6.2 Loglinear Models 84
3.6.3 The Effects of Gender 87
3.6.4 Some Words of Warning 89
Wrap-up 90
Exercises 90
Heteroskedasticity and Autocorrelation 94
4.1 Consequences for the OLS Estimator 94
4.2 Deriving an Alternative Estimator 96
4.3 Heteroskedasticity 97
4.3.1 Introduction 97
4.3.2 Estimator Properties and Hypothesis Testing 100
4.3.3 When the Variances are Unknown 101
CONTENTS
A3 A Heteroskedasticity-consistent Standard Errors
for OLS 102
4.3.5 Multiplicative Heteroskedasticity 103
4.3.6 Weighted Least Squares with Arbitrary Weights 104
4.4 Testing for Heteroskedasticity 105
4.4.1 Testing for Multiplicative Heteroskedasticity 105
4.4.2 The Breusch-Pagan Test 106
4.4.3 The White Test 106
4.4.4 Which Test? 107
4.5 Illustration: Explaining Labour Demand 107
4.6 Autocorrelation 112
4.6.1 First-order Autocorrelation 113
4.6.2 Unknown p 115
4.7 Testing for First-order Autocorrelation 116
4.7.1 Asymptotic Tests 116
4.7.2 The Durbin-Watson Test 117
4.8 Illustration: The Demand for Ice Cream 119
4.9 Alternative Autocorrelation Patterns 122
4.9.1 Higher-order Autocorrelation 122
4.9.2 Moving Average Errors 122
4.10 What to do When you Find Autocorrelation? 123
4.10.1 Misspecification 124
4.10.2 Heteroskedasticity-and-autocorrelation-consistent
Standard Errors for OLS 125
4.11 Illustration: Risk Premia in Foreign Exchange Markets 127
4.11.1 Notation 127
4.11.2 Tests for Risk Premia in the 1 Month Market 128
4.11.3 Tests for Risk Premia Using Overlapping
Samples 132
Wrap-up 134
Exercises 134
Endogenous Regressors, Instrumental Variables and GMM 137
5.1 A Review of the Properties of the OLS Estimator 138
5.2 Cases Where the OLS Estimator Cannot be Saved 141
5.2.1 Autocorrelation with a Lagged Dependent
Variable 141
5.2.2 Measurement Error in an Explanatory Variable 142
5.2.3 Endogeneity and Omitted Variable Bias 144
5.2.4 Simultaneity and Reverse Causality 146
5.3 The Instrumental Variables Estimator 148
5.3.1 Estimation with a Single Endogenous Regressor
and a Single Instrument 148
5.3.2 Back to the Keynesian model 152
5.3.3 Back to the Measurement Error Problem 153
5.3.4 Multiple Endogenous Regressors 153
vffi CONTENTS
5.4 Illustration: Estimating the Returns to Schooling 154
5.5 The Generalized Instrumental Variables Estimator 158
5.5.1 Multiple Endogenous Regressors with an Arbitrary
Number of Instruments 159
5.5.2 Two-stage Least Squares and the Keynesian
Model Again 162
5.5.3 Specification Tests 163
5.5.4 Weak Instruments 164
5.6 The Generalized Method of Moments 166
5.6.1 Example 166
5.6.2 The Generalized Method of Moments 167
5.6.3 Some Simple Examples 170
5.6.4 Weak Identification 171
5.7 Illustration: Estimating Intertemporal Asset Pricing
Models 171
Wrap-up 175
Exercises 176
6 Maximum Likelihood Estimation and Specification Tests 179
6.1 An Introduction to Maximum Likelihood 180
6.1.1 Some Examples 180
6.1.2 General Properties 183
6.1.3 An Example (Continued) 186
6.1.4 The Normal Linear Regression Model 187
6.2 Specification Tests 189
6.2.1 Three Test Principles 189
6.2.2 Lagrange Multiplier Tests 191
6.2.3 An Example (Continued) 194
6.3 Tests in the Normal Linear Regression Model 195
6.3.1 Testing for Omitted Variables 196
6.3.2 Testing for Heteroskedasticity 197
6.3.3 Testing for Autocorrelation 198
6.4 Quasi-maximum Likelihood and Moment
Conditions Tests 199
6.4.1 Quasi-maximum Likelihood 199
6.4.2 Conditional Moment Tests 201
6.4.3 Testing for Normality 202
Wrap-up 203
Exercises 203
7 Models with Limited Dependent Variables 206
7.1 Binary Choice Models 207
7.1.1 Using Linear Regression? 207
7.1.2 Introducing Binary Choice Models 207
7.1.3 An Underlying Latent Model 210
7.1.4 Estimation 211
CONTENTS
7.1.5 Goodness-of-fìt 212
7.1.6 Illustration: The Impact of Unemployment Benefits
on Recipiency 215
7.1.7 Specification Tests in Binary Choice Models 217
7.1.8 Relaxing Some Assumptions in Binary Choice
Models 219
7.2 Multiresponse Models 220
7.2.1 Ordered Response Models 221
7.2.2 About Normalization 222
7.2.3 Illustration: Explaining Firms Credit Ratings 223
7.2.4 Illustration: Willingness to Pay for Natural Areas 225
7.2.5 Multinomial Models 228
7.3 Models for Count Data 231
7.3.1 The Poisson and Negative Binomial Models 231
7.3.2 Illustration: Patents and R D Expenditures 235
7.4 Tobit Models 238
7.4.1 The Standard Tobit Model 238
7.4.2 Estimation 241
7.4.3 Illustration: Expenditures on Alcohol and
Tobacco (Part 1) 242
7.4.4 Specification Tests in the Tobit Model 245
7.5 Extensions of Tobit Models 247
7.5.1 The Tobit II Model 248
7.5.2 Estimation 250
7.5.3 Further Extensions 253
7.5.4 Illustration: Expenditures on Alcohol and
Tobacco (Part 2) 253
7.6 Sample Selection Bias 257
7.6.1 The Nature of the Selection Problem 257
7.6.2 Semi-parametric Estimation of the Sample-Selection
Model 260
7.7 Estimating Treatment Effects 260
7.7.1 Regression-based Estimators 262
7.7.2 Alternative Approaches 266
7.8 Duration Models 268
7.8.1 Hazard Rates and Survival Functions 268
7.8.2 Samples and Model Estimation 270
7.8.3 Illustration: Duration of Bank Relationships 273
Wrap-up 274
Exercises 274
Univariate Time Series Models 278
8.1 Introduction 279
8.1.1 Some Examples 279
8.1.2 Stationarity and the Autocorrelation Function 281
8.2 General ARMA Processes 284
CONTENTS
8.2.1 Formulating ARMA Processes 284
8.2.2 Invertibility of Lag Polynomials 287
8.2.3 Common Roots 288
8.3 Stationarity and Unit Roots 289
8.4 Testing for Unit Roots 291
8.4.1 Testing for Unit Roots in a First-order
Autoregressive Model 291
8.4.2 Testing for Unit Roots in Higher-order
Autoregressive Models 294
8.4.3 Extensions 296
8.4.4 Illustration: Stock Prices and Earnings 297
8.5 Illustration: Long-run Purchasing Power Parity (Part 1) 300
8.6 Estimation of ARMA Models 304
8.6.1 Least Squares 304
8.6.2 Maximum Likelihood 305
8.7 Choosing a Model 306
8.7.1 The Autocorrelation Function 306
8.7.2 The Partial Autocorrelation Function 308
8.7.3 Diagnostic Checking 309
8.7.4 Criteria for Model Selection 310
8.8 Illustration: The Persistence of Inflation 311
8.9 Predicting with ARMA Models 314
8.9.1 The Optimal Predictor 315
8.9.2 Prediction Accuracy 317
8.9.3 Evaluating predictions 319
8.10 Illustration: The Expectations Theory of the Term
Structure 320
8.11 Autoregressive Conditional Heteroskedasticity 325
8.11.1 ARCH and GARCH Models 325
8.11.2 Estimation and Prediction 329
8.11.3 Illustration: Volatility in Daily Exchange Rates 331
8.12 What about Multivariate Models? 333
Wrap-up 333
Exercises 334
9 Multivariate Time Series Models 338
9.1 Dynamic Models with Stationary Variables 339
9.2 Models with Nonstationary Variables 342
9.2.1 Spurious Regressions 342
9.2.2 Cointegration 343
9.2.3 Cointegration and Error-correction Mechanisms 346
9.3 Illustration: Long-run Purchasing Power Parity (Part 2) 348
9.4 Vector Autoregressive Models 350
9.5 Cointegration: the Multivariate Case 354
9.5.1 Cointegration in a VAR 354
9.5.2 Example: Cointegration in a Bivariate VAR 356
9.5.3 Testing for Cointegration 358
CONTENTS x¡
9.5.4 Illustration: Long-run Purchasing Power
Parity (Part 3) 360
9.6 Illustration: Money Demand and Inflation 362
Wrap-up 368
Exercises 369
10 Models Based on Panel Data 372
10.1 Introduction to Panel Data Modelling 373
10.1.1 Efficiency of Parameter Estimators 374
10.1.2 Identification of Parameters 375
10.2 The Static Linear Model 376
10.2.1 The Fixed Effects Model 377
10.2.2 The First-difference Estimator 379
10.2.3 The Random Effects Model 381
10.2.4 Fixed Effects or Random Effects? 384
10.2.5 Goodness-of-fit 386
10.2.6 Alternative Instrumental Variables Estimators 387
10.2.7 Robust Inference 389
10.2.8 Testing for Heteroskedasticity and
Autocorrelation 391
10.2.9 The Fama-MacBeth Approach 392
10.3 Illustration: Explaining Individual Wages 394
10.4 Dynamic Linear Models 396
10.4.1 An Autoregressive Panel Data Model 396
10.4.2 Dynamic Models with Exogenous Variables 401
10.4.3 Too Many Instruments 403
10.5 Illustration: Explaining Capital Structure 405
10.6 Panel Time Series 410
10.6.1 Heterogeneity 411
10.6.2 First Generation Panel Unit Root Tests 412
10.6.3 Second Generation Panel Unit Root Tests 415
10.6.4 Panel Cointegration Tests 416
10.7 Models with Limited Dependent Variables 417
10.7.1 Binary Choice Models 418
10.7.2 The Fixed Effects Logit Model 419
10.7.3 The Random Effects Probit Model 420
10.7.4 Tobit Models 422
10.7.5 Dynamics and the Problem
of Initial Conditions 423
10.7.6 Semi-parametric Alternatives 424
10.8 Incomplete Panels and Selection Bias 425
10.8.1 Estimation with Randomly Missing Data 426
10.8.2 Selection Bias and Some Simple Tests 427
10.8.3 Estimation with Nonrandomly Missing Data 429
10.9 Pseudo Panels and Repeated Cross-sections 430
10.9.1 The Fixed Effects Model 431
10.9.2 An Instrumental Variables Interpretation 433
x¡¡ CONTENTS
10.9.3 Dynamic Models 434
Wrap-up 435
Exercises 436
A Vectors and Matrices 441
A.l Terminology 441
A.2 Matrix Manipulations 442
A.3 Properties of Matrices and Vectors 443
A.4 Inverse Matrices 444
A.5 Idempotent Matrices 445
A.6 Eigenvalues and Eigenvectors 445
A.7 Differentiation 446
A.8 Some Least Squares Manipulations 447
B Statistical and Distribution Theory 449
B.l Discrete Random Variables 449
B.2 Continuous Random Variables 450
B.3 Expectations and Moments 451
B.4 Multivariate Distributions 452
B.5 Conditional Distributions 453
B.6 The Normal Distribution 454
B.7 Related Distributions 457
Bibliography 459
Index 477
|
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| publisher | WILEY |
| record_format | marc |
| spellingShingle | Verbeek, Marno 1965- A guide to modern econometrics Includes bibliographical references and index Econometrics Regression analysis Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4123623-3 |
| title | A guide to modern econometrics |
| title_auth | A guide to modern econometrics |
| title_exact_search | A guide to modern econometrics |
| title_full | A guide to modern econometrics Marno Verbeek |
| title_fullStr | A guide to modern econometrics Marno Verbeek |
| title_full_unstemmed | A guide to modern econometrics Marno Verbeek |
| title_short | A guide to modern econometrics |
| title_sort | a guide to modern econometrics |
| topic | Econometrics Regression analysis Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Econometrics Regression analysis Ökonometrie Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024666428&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT verbeekmarno aguidetomoderneconometrics |
Indexes
Sonderstandort Fakultät
| Call Number: |
2000 QH 310 V477(4) |
|---|---|
| Copy 1 | nicht ausleihbar Checked out – Due: 31.12.2099 Recall This |