Econometrics:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2021]
|
| Ausgabe: | Sixth edition |
| Schriftenreihe: | Classroom companion: economics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxi, 485 Seiten Illustrationen, Diagramme |
| ISBN: | 9783030801489 |
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| adam_text | Contents Parti 1 What Is Econometrics?............................................................................. 1.1 Introduction..................................................................................... 1.2 A Brief History.................................................................................. 1.3 Critiques of Econometrics............................................................... 1.4 Looking Ahead.................................................................................. Notes............................................................................................................... References...................................................................................................... 3 3 6 8 9 H 11 2 Basic Statistical Concepts.......................................................................... 2.1 Introduction..................................................................................... 2.2 Methods of Estimation.................................................................... 2.3 Properties of Estimators................................................................. 2.4 Hypothesis Testing.......................................................................... 2.5 Confidence Intervals....................................................................... 2.6 Descriptive Statistics....................................................................... Notes............................................................................................................... Problems
....................................................................................................... Appendix....................................................................................................... References...................................................................................................... 13 13 13 17 24 34 36 41 42 48 56 3 Simple Linear Regression.......................................................................... 3.1 Introduction..................................................................................... 3.2 Least Squares Estimation andthe Classical Assumptions............ 3.3 Statistical Properties of LeastSquares............................................ 3.4 Estimation of σ2 ............................................................................. 3.5 Maximum Likelihood Estimation.................................................. 3.6 A Measure of Fit............................................................................. 3.7 Prediction......................................................................................... 3.8 Residual Analysis............................................................................ 3.9 Numerical Example......................................................................... 57 57 59 64 66 67 69 71 72 74 XI
xii Contents 3.10 Empirical Example............................................................................ Problems ......................................................................................................... Appendix: Centered and Uncentered R2...................................................... References....................................................................................................... 76 79 84 85 Multiple Regression Analysis..................................................................... 4Л Introduction....................................................................................... 4.2 Least Squares Estimation................................................................. 4.3 Residual Interpretation of Multiple Regression Estimates........... 4.4 Overspecification and Underspecification of the Regression Equation............................................................................................ 4.5 R-Squared Versus R-Bar-Squared................................................... 4.6 Testing Linear Restrictions.............................................................. 4.7 Dummy Variables............................................................................. Notes................................................................................................................ Problems ........................................................................................................ Appendix: Residual Interpretation of Multiple Regression Estimates ....
References...................................................................................................... 87 87 87 89 91 93 94 97 102 102 108 Ill 5 Violations of the Classical Assumptions................................................... 5.1 Introduction...................................................................................... 5.2 Normality of the Disturbances....................................................... 5.3 Heteroskedasticity........................................................................... 5.4 Autocorrelation................................................................................ Notes............................................................................................................... Problems ........................................................................................................ References...................................................................................................... 113 113 115 116 129 141 141 149 6 Distributed Lags and Dynamic Models................................................... 6.1 Introduction...................................................................................... 6.2 Infinite Distributed Lag................................................................... 6.2.1 Adaptive Expectations Model (AEM).......................... 6.2.2 Partial Adjustment Model (PAM).................................. 6.3 Estimation and Testing of Dynamic Models with Serial Correlation....................................................................................... 6.3.1 A
Lagged Dependent Variable Model with AR( 1 ) Disturbances........................................................ 6.3.2 A Lagged Dependent Variable Model with MA(1) Disturbances....................................................... 6.4 Autoregressive Distributed Lag..................................................... Notes............................................................................................................... Problems ....................................................................................................... References...................................................................................................... 153 153 160 161 162 4 163 164 166 167 168 169 171
Contents xiii Part II 7 The General Linear Model: The Basics.................................................. 7.1 Introduction..................................................................................... 7.2 Least Squares Estimation............................................................... 7.3 Partitioned Regression and the Frisch-Waugh-Lovell Theorem........................................................................................... 7.4 Maximum Likelihood Estimation.................................................. 7.5 Prediction......................................................................................... 7.6 Confidence Intervals and Test of Hypotheses .............................. 7.7 Joint Confidence Intervals and Test of Hypotheses..................... 7.8 Restricted MLE and Restricted Least Squares ............................. 7.9 Likelihood Ratio, Wald and Lagrange Multiplier Tests............... 7.9.1 Chow’s (1960) Test for Regression Stability............... 7.9.2 The W, LR, and LM Inequality .................................... Notes.............................................................................................................. Problems ....................................................................................................... Appendix: Some Useful Matrix Properties................................................ References..................................................................................................... 175 175 175 180 182 185 186 187 188 189 192 193 196 196 202 208 8 Regression
Diagnostics andSpecification Tests...................................... 8.1 Influential Observations................................................................. 8.2 Recursive Residuals......................................................................... 8.3 Specification Tests.......................................................................... 8.4 Nonlinear Least Squares and the Gauss-Newton Regression .... 8.4.1 Diagnostic Tests for Linear Regression Models............ 8.5 Testing Linear Versus Log-Linear Functional Form.................... Notes.............................................................................................................. Problems ....................................................................................................... References..................................................................................................... 209 209 220 228 241 246 248 249 249 254 9 Generalized Least Squares....................................................................... 9.1 Introduction..................................................................................... 9.2 Generalized Least Squares ............................................................ 9.2.1 Necessary and Sufficient Conditions for OLS to Be Equivalent to GLS .................................................... 9.3 Special Forms of Ω......................................................................... 9.4 Maximum Likelihood Estimation.................................................. 9.5 Test of
Hypotheses.......................................................................... 9.6 Prediction......................................................................................... 9.7 Unknown Ω..................................................................................... 9.8 The W, LR, and LM Statistics Revisited....................................... 257 257 257 259 260 261 262 262 263 264
xiv 10 11 12 Contents 9.9 Spatial Error Correlation ................................................................. Notes................................................................................................................ Problems ........................................................................................................ References....................................................................................................... 266 269 269 275 Seemingly Unrelated Regressions.............................................................. 10.1 Introduction...................................................................................... 10.2 Feasible GLS Estimation................................................................. 10.2.1 Relative Efficiency of OLS in the Case of Simple Regressions....................................................................... 10.2.2 Relative Efficiency of OLS in the Case of Multiple Regressions...................................................... 10.3 Testing Diagonality of the Variance-Covariance Matrix.............. 10.4 Seemingly Unrelated Regressions with Unequal Observations... 10.5 Empirical Examples ........................................................................ Problems ........................................................................................................ References...................................................................................................... 279 279 283 Simultaneous Equations
Model................................................................ 11.1 Introduction...................................................................................... 11.1.1 Simultaneous Bias.......................................................... 11.1.2 The Identification Problem............................................ 11.2 Single Equation Estimation: Two-Stage Least Squares................ 11.2.1 Spatial Lag Dependence................................................. 11.3 System Estimation: Three-Stage Least Squares............................ 11.4 Test for Over-Identification Restrictions........................................ 11.5 Hausman’s Specification Test.......................................................... 11.6 Empirical Examples ........................................................................ Notes............................................................................................................... Problems ....................................................................................................... Appendix: The Identification Problem Revisited: The Rank Condition of Identification ............................................................. References...................................................................................................... 299 299 299 303 307 316 318 319 322 325 335 335 Pooling Time-Series of Cross-Section Data............................................ 12.1 Introduction..................................................................................... 12.2 The Error Components
Model........................................................ 12.2.1 The Fixed Effects Model............................................... 12.2.2 The Random Effects Model........................................... 12.2.3 Maximum Likelihood Estimation................................. 12.3 Prediction......................................................................................... 12.4 Empirical Example.......................................................................... 12.5 Testing in a Pooled Model.............................................................. 12.6 Dynamic Panel Data Models.......................................................... 12.6.1 Empirical Illustration....................................................... 357 357 358 359 363 367 368 369 372 378 382 284 284 286 287 289 291 297 347 353
Contents XV 12.7 Program Evaluation and Difference-in֊Differences Estimator ... 12.7.1 The Difference-in-Differences Estimator..................... Problems ....................................................................................................... References..................................................................................................... 384 385 385 389 13 Limited Dependent Variables.................................................................. 13.1 Introduction.................................................................................... 13.2 The Linear Probability Model........................................................ 13.3 Functional Form: Logit and Probit................................................ 13.4 Grouped Data................................................................................. 13.5 Individual Data: Probit and Logit.................................................. 13.6 The Binary Response Model Regression...................................... 13.7 Asymptotic Variances for Predictions and Marginal Effects....... 13.8 Goodness of Fit Measures.............................................................. 13.9 Empirical Examples....................................................................... 13.10 Multinomial Choice Models........................................................... 13.10.1 Ordered Response Models............................................. 13.10.2 Unordered Response Models......................................... 13.11 The Censored Regression
Model................................................... 13.12 The Truncated Regression Model.................................................. 13.13 Sample Selectivity.......................................................................... 13.14 Limited Dependent Variables Panel Data...................................... Notes.............................................................................................................. Problems ....................................................................................................... Appendix...................................................................................................... References..................................................................................................... 393 393 393 396 397 401 404 406 407 408 415 415 418 420 424 427 430 433 433 440 443 14 Time-Series Analysis................................................................................. 14.1 Introduction.................................................................................... 14.2 Stationarity...................................................................................... 14.3 The Box and Jenkins Method........................................................ 14.4 Vector Autoregression.................................................................... 14.5 Unit Roots............................................................. 14.6 Trend Stationary Versus Difference Stationary............................. 14.7
Cointegration................................................................................... 14.8 A Cointegration Example............................................................... 14.9 Autoregressive Conditional Heteroskedasticity........................... Notes.............................................................................................................. Problems ...................................................................................................... References..................................................................................................... 447 447 447 449 454 455 459 460 461 464 467 468 472 Appendix.............................................................................................................. 475 Index...................................................................................................................... 481
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| adam_txt |
Contents Parti 1 What Is Econometrics?. 1.1 Introduction. 1.2 A Brief History. 1.3 Critiques of Econometrics. 1.4 Looking Ahead. Notes. References. 3 3 6 8 9 H 11 2 Basic Statistical Concepts. 2.1 Introduction. 2.2 Methods of Estimation. 2.3 Properties of Estimators. 2.4 Hypothesis Testing. 2.5 Confidence Intervals. 2.6 Descriptive Statistics. Notes. Problems
. Appendix. References. 13 13 13 17 24 34 36 41 42 48 56 3 Simple Linear Regression. 3.1 Introduction. 3.2 Least Squares Estimation andthe Classical Assumptions. 3.3 Statistical Properties of LeastSquares. 3.4 Estimation of σ2 . 3.5 Maximum Likelihood Estimation. 3.6 A Measure of Fit. 3.7 Prediction. 3.8 Residual Analysis. 3.9 Numerical Example. 57 57 59 64 66 67 69 71 72 74 XI
xii Contents 3.10 Empirical Example. Problems . Appendix: Centered and Uncentered R2. References. 76 79 84 85 Multiple Regression Analysis. 4Л Introduction. 4.2 Least Squares Estimation. 4.3 Residual Interpretation of Multiple Regression Estimates. 4.4 Overspecification and Underspecification of the Regression Equation. 4.5 R-Squared Versus R-Bar-Squared. 4.6 Testing Linear Restrictions. 4.7 Dummy Variables. Notes. Problems . Appendix: Residual Interpretation of Multiple Regression Estimates .
References. 87 87 87 89 91 93 94 97 102 102 108 Ill 5 Violations of the Classical Assumptions. 5.1 Introduction. 5.2 Normality of the Disturbances. 5.3 Heteroskedasticity. 5.4 Autocorrelation. Notes. Problems . References. 113 113 115 116 129 141 141 149 6 Distributed Lags and Dynamic Models. 6.1 Introduction. 6.2 Infinite Distributed Lag. 6.2.1 Adaptive Expectations Model (AEM). 6.2.2 Partial Adjustment Model (PAM). 6.3 Estimation and Testing of Dynamic Models with Serial Correlation. 6.3.1 A
Lagged Dependent Variable Model with AR( 1 ) Disturbances. 6.3.2 A Lagged Dependent Variable Model with MA(1) Disturbances. 6.4 Autoregressive Distributed Lag. Notes. Problems . References. 153 153 160 161 162 4 163 164 166 167 168 169 171
Contents xiii Part II 7 The General Linear Model: The Basics. 7.1 Introduction. 7.2 Least Squares Estimation. 7.3 Partitioned Regression and the Frisch-Waugh-Lovell Theorem. 7.4 Maximum Likelihood Estimation. 7.5 Prediction. 7.6 Confidence Intervals and Test of Hypotheses . 7.7 Joint Confidence Intervals and Test of Hypotheses. 7.8 Restricted MLE and Restricted Least Squares . 7.9 Likelihood Ratio, Wald and Lagrange Multiplier Tests. 7.9.1 Chow’s (1960) Test for Regression Stability. 7.9.2 The W, LR, and LM Inequality . Notes. Problems . Appendix: Some Useful Matrix Properties. References. 175 175 175 180 182 185 186 187 188 189 192 193 196 196 202 208 8 Regression
Diagnostics andSpecification Tests. 8.1 Influential Observations. 8.2 Recursive Residuals. 8.3 Specification Tests. 8.4 Nonlinear Least Squares and the Gauss-Newton Regression . 8.4.1 Diagnostic Tests for Linear Regression Models. 8.5 Testing Linear Versus Log-Linear Functional Form. Notes. Problems . References. 209 209 220 228 241 246 248 249 249 254 9 Generalized Least Squares. 9.1 Introduction. 9.2 Generalized Least Squares . 9.2.1 Necessary and Sufficient Conditions for OLS to Be Equivalent to GLS . 9.3 Special Forms of Ω. 9.4 Maximum Likelihood Estimation. 9.5 Test of
Hypotheses. 9.6 Prediction. 9.7 Unknown Ω. 9.8 The W, LR, and LM Statistics Revisited. 257 257 257 259 260 261 262 262 263 264
xiv 10 11 12 Contents 9.9 Spatial Error Correlation . Notes. Problems . References. 266 269 269 275 Seemingly Unrelated Regressions. 10.1 Introduction. 10.2 Feasible GLS Estimation. 10.2.1 Relative Efficiency of OLS in the Case of Simple Regressions. 10.2.2 Relative Efficiency of OLS in the Case of Multiple Regressions. 10.3 Testing Diagonality of the Variance-Covariance Matrix. 10.4 Seemingly Unrelated Regressions with Unequal Observations. 10.5 Empirical Examples . Problems . References. 279 279 283 Simultaneous Equations
Model. 11.1 Introduction. 11.1.1 Simultaneous Bias. 11.1.2 The Identification Problem. 11.2 Single Equation Estimation: Two-Stage Least Squares. 11.2.1 Spatial Lag Dependence. 11.3 System Estimation: Three-Stage Least Squares. 11.4 Test for Over-Identification Restrictions. 11.5 Hausman’s Specification Test. 11.6 Empirical Examples . Notes. Problems . Appendix: The Identification Problem Revisited: The Rank Condition of Identification . References. 299 299 299 303 307 316 318 319 322 325 335 335 Pooling Time-Series of Cross-Section Data. 12.1 Introduction. 12.2 The Error Components
Model. 12.2.1 The Fixed Effects Model. 12.2.2 The Random Effects Model. 12.2.3 Maximum Likelihood Estimation. 12.3 Prediction. 12.4 Empirical Example. 12.5 Testing in a Pooled Model. 12.6 Dynamic Panel Data Models. 12.6.1 Empirical Illustration. 357 357 358 359 363 367 368 369 372 378 382 284 284 286 287 289 291 297 347 353
Contents XV 12.7 Program Evaluation and Difference-in֊Differences Estimator . 12.7.1 The Difference-in-Differences Estimator. Problems . References. 384 385 385 389 13 Limited Dependent Variables. 13.1 Introduction. 13.2 The Linear Probability Model. 13.3 Functional Form: Logit and Probit. 13.4 Grouped Data. 13.5 Individual Data: Probit and Logit. 13.6 The Binary Response Model Regression. 13.7 Asymptotic Variances for Predictions and Marginal Effects. 13.8 Goodness of Fit Measures. 13.9 Empirical Examples. 13.10 Multinomial Choice Models. 13.10.1 Ordered Response Models. 13.10.2 Unordered Response Models. 13.11 The Censored Regression
Model. 13.12 The Truncated Regression Model. 13.13 Sample Selectivity. 13.14 Limited Dependent Variables Panel Data. Notes. Problems . Appendix. References. 393 393 393 396 397 401 404 406 407 408 415 415 418 420 424 427 430 433 433 440 443 14 Time-Series Analysis. 14.1 Introduction. 14.2 Stationarity. 14.3 The Box and Jenkins Method. 14.4 Vector Autoregression. 14.5 Unit Roots. 14.6 Trend Stationary Versus Difference Stationary. 14.7
Cointegration. 14.8 A Cointegration Example. 14.9 Autoregressive Conditional Heteroskedasticity. Notes. Problems . References. 447 447 447 449 454 455 459 460 461 464 467 468 472 Appendix. 475 Index. 481 |
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| index_date | 2024-07-03T19:11:14Z |
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| isbn | 9783030801489 |
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| spelling | Baltagi, Badi H. 1954- Verfasser (DE-588)128356812 aut Econometrics Badi H. Baltagi Sixth edition Cham Springer [2021] © 2021 xxi, 485 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Classroom companion: economics Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s DE-604 Erscheint auch als Online-Ausgabe 978-3-030-80149-6 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033222088&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Econometrics |
| title_auth | Econometrics |
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| title_full | Econometrics Badi H. Baltagi |
| title_fullStr | Econometrics Badi H. Baltagi |
| title_full_unstemmed | Econometrics Badi H. Baltagi |
| title_short | Econometrics |
| title_sort | econometrics |
| topic | Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Ökonometrie Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033222088&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT baltagibadih econometrics |