Verfügbarkeit: Introduction to econometrics

Introduction to econometrics:

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1. Verfasser:	Maddala, Gangadharrao S. 1933- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Chichester [u.a.] Wiley 2006
Ausgabe:	3. ed., repr.
Schlagworte:	Ökonometrie Einführung Lehrbuch
Online-Zugang:	Publisher description Inhaltsverzeichnis
Beschreibung:	XXVII, 636 S. graf. Darst.
ISBN:	0471497282 9780471497288

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Datensatz im Suchindex

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adam_text	Contents Foreword xvii Preface to the Second Edition xix Preface to the Third Edition xxiii Obituary xxv PART I INTRODUCTION AND THE LINEAR REGRESSION MODEL 1 1 What is Econometrics? 3 What is in this Chapter? 3 1.1 What is Econometrics? 3 1.2 Economic and Econometric Models 4 1.3 The Aims and Methodology of Econometrics 6 1.4 What Constitutes a Test of an Economic Theory? 9 Summary and an Outline of the Book 9 2 Statistical Background and Matrix Algebra 11 What is in this Chapter? 11 2.1 Introduction 12 2.2 Probability 12 Addition Rules of Probability 13 Conditional Probability and the Multiplication Rule 14 Bayes Theorem 15 Summation and Product Operations 15 2.3 Random Variables and Probability Distributions 17 Joint, Marginal, and Conditional Distributions 18 Illustrative Example 18 2.4 The Normal Probability Distribution and Related Distributions 19 The Normal Distribution 19 Related Distributions 20 v¡ CONTENTS 2.5 Classical Statistical Inference 21 Point Estimation 22 2.6 Properties of Estimators 23 Unbiasedness 23 Efficiency 24 Consistency 24 Other Asymptotic Properties 25 2.7 Sampling Distributions for Samples from a Normal Population 26 2.8 Interval Estimation 27 2.9 Testing of Hypotheses 28 2.10 Relationship Between Confidence Interval Procedures and Tests of Hypotheses 32 2.11 Combining Independent Tests 33 Summary 33 Exercises 34 Appendix to Chapter 2 41 Matrix Algebra 41 Exercises on Matrix Algebra 56 3 Simple Regression 59 What is in this Chapter? 59 3.1 Introduction 59 3.2 Specification of the Relationships 61 3.3 The Method of Moments 65 Illustrative Example 66 3.4 The Method of Least Squares 68 Reverse Regression 71 Illustrative Example 72 3.5 Statistical Inference in the Linear Regression Model 75 Illustrative Example 77 Confidence Intervals for α, β, and σ2 78 Testing of Hypotheses 79 Example of Comparing Test Scores from the GRE and GMAT Tests 81 Regression with No Constant Term 82 3.6 Analysis of Variance for the Simple Regression Model 83 3.7 Prediction with the Simple Regression Model 84 Prediction of Expected Values 86 Illustrative Example 87 3.8 Outliers 88 Some Illustrative Examples 89 3.9 Alternative Functional Forms for Regression Equations 94 Illustrative Example 97 3.10 Inverse Prediction in the Least Squares Regression Model 99 3.11 Stochastic Regressors 101 CONTENTS vii 3.12 The Regression Fallacy 102 The Bivariate Normal Distribution 102 Galton s Result and the Regression Fallacy 104 A Note on the Term: Regression 104 Summary 105 Exercises 106 Appendix to Chapter 3 112 4 Multiple Regression 127 What is in this Chapter? 127 4.1 Introduction 127 4.2 A Model with Two Explanatory Variables 129 The Least Squares Method 130 Illustrative Example 132 4.3 Statistical Inference in the Multiple Regression Model 134 Illustrative Example 135 Formulas for the General Case of к Explanatory Variables 139 Some Illustrative Examples 140 4.4 Interpretation of the Regression Coefficients 143 Illustrative Example 145 4.5 Partial Correlations and Multiple Correlation 146 4.6 Relationships Among Simple, Partial, and Multiple Correlation Coefficients 147 Two Illustrative Examples 148 4.7 Prediction in the Multiple Regression Model 153 Illustrative Example 153 4.8 Analysis of Variance and Tests of Hypotheses 154 Nested and Nonnested Hypotheses 156 Tests for Linear Functions of Parameters 157 Illustrative Example 158 4.9 Omission of Relevant Variables and Inclusion of Irrelevant Variables 159 Omission of Relevant Variables 160 Example 1: Demand for Food in the United States 161 Example 2: Production Functions and Management Bias 162 Inclusion of Irrelevant Variables 163 4.10 Degrees of Freedom and R 164 4.11 Tests for Stability 168 The Analysis of Variance Test 168 Example 1: Stability of the Demand for Food Function 169 Example 2: Stability of Production Functions 170 Predictive Tests for Stability 173 Illustrative Example 173 4.12 The LR, W, and LM Tests 176 Illustrative Example 176 viiî CONTENTS Summary 1 Exercises 1^9 Appendix to Chapter 4 185 The Multiple Regression Model in Matrix Notation 185 Data Sets 192 PART Π VIOLATION OF THE ASSUMPTIONS OF THE BASIC MODEL 197 5 Heteroskedasticity 199 What is in this Chapter? 199 5.1 Introduction 199 Illustrative Example 200 5.2 Detection of Heteroskedasticity 202 Illustrative Example 202 Some Other Tests 203 Illustrative Example 205 An Intuitive Justification for the Breusch-Pagan Test 206 5.3 Consequences of Heteroskedasticity 207 Estimation of the Variance of the OLS Estimator Under Heteroskedasticity 209 5.4 Solutions to the Heteroskedasticity Problem 209 Illustrative Example 211 5.5 Heteroskedasticity and the Use of Deflators 212 Illustrative Example: The Density Gradient Model 215 5.6 Testing the Linear Versus Log-Linear Functional Form 217 The Box -Сох Test 217 The BM Test 219 The PE Test 219 Summary 220 Exercises 221 Appendix to Chapter 5 224 Generalized Least Squares 224 6 Autocorrelation 227 What is in this Chapter? 227 6.1 Introduction 227 6.2 Durbin- Watson Test 228 Illustrative Example 229 6.3 Estimation in Levels Versus First Differences 230 Some Illustrative Examples 232 6.4 Estimation Procedures with Autocorrelated Errors 234 Iterative Procedures 236 Grid-Search Procedures 237 Illustrative Example 238 6.5 Effect of AR(1) Errors on OLS Estimates 238 CONTENTS їх 6.6 Some Further Comments on the DW Test 242 The von Neumann Ratio 243 The Berenblut-Webb Test 243 6.7 Tests for Serial Correlation in Models with Lagged Dependent Variables 245 Durbin s /г -Test 246 Durbin s Alternative Test 246 Illustrative Example 247 6.8 A General Test for Higher-Order Serial Correlation: The LM Test 248 6.9 Strategies When the DW Test Statistic is Significant 249 Errors Not ARCI) 249 Autocorrelation Caused by Omitted Variables 250 Serial Correlation Due to Misspecified Dynamics 252 The Wald Test 253 Illustrative Example 254 6.10 Trends and Random Walks 255 Spurious Trends 257 Differencing and Long-Run Effects: The Concept of Cointegration 258 6.11 ARCH Models and Serial Correlation 260 6.12 Some Comments on the DW Test and Durbin s й -Test and i-Test 262 Summary 262 Exercises 264 7 Multicollinearity 267 What is in this Chapter? 267 7.1 Introduction 268 7.2 Some Illustrative Examples 268 7.3 Some Measures of Multicollinearity 272 7.4 Problems with Measuring Multicollinearity 274 7.5 Solutions to the Multicollinearity Problem: Ridge Regression 278 7.6 Principal Component Regression 281 7.7 Dropping Variables 286 7.8 Miscellaneous Other Solutions 289 Using Ratios or First Differences 289 Using Extraneous Estimates 289 Getting More Data 291 Summary 291 Exercises 291 Appendix to Chapter 7 293 Linearly Dependent Explanatory Variables 293 8 Dummy Variables and Truncated Variables 301 What is in mis Chapter? 301 8.1 Introduction 301 CONTENTS 8.2 Dummy Variables for Changes in the Intercept Term 302 Illustrative Example 305 Two More ïïlustrative Examples 306 8.3 Dummy Variables for Changes in Slope Coefficients 307 8.4 Dummy Variables for Cross-Equation Constraints 310 8.5 Dummy Variables for Testing Stability of Regression Coefficients 313 8.6 Dummy Variables Under Heteroskedasticity and Autocorrelation 316 8.7 Dummy Dependent Variables 317 8.8 The Linear Probability Model and the Linear Discriminant Function 318 The Linear Probability Model 318 The Linear Discriminant Function 320 8.9 The Probit and Logit Models 322 Illustrative Example 324 The Problem of Disproportionate Sampling 325 Prediction of Effects of Changes in the Explanatory Variables 327 Measuring Goodness of Fit 327 8.10 Illustrative Example 329 8.11 Truncated Variables: The Tobit Model 333 Some Examples 333 Method of Estimation 334 Limitations of the Tobit Model 335 The Truncated Regression Model 336 Summary 338 Exercises 339 Simultaneous Equations Models 343 What is in this Chapter? 343 9.1 Introduction 343 9.2 Endogenous and Exogenous Variables 345 9.3 The Identification Problem: Identification through Reduced Form 346 Illustrative Example 348 9.4 Necessary and Sufficient Conditions for Identification 351 Illustrative Example 353 9.5 Methods of Estimation: The Instrumental Variable Method 354 Measuring R2 356 Illustrative Example3 357 9.6 Methods of Estimation: The Two-Stage Least Squares Method 360 Computing Standard Errors 361 Illustrative Example 363 9.7 The Question of Normalization 366 9.8 The Limited-Information Maximum Likelihood Method 367 Illustrative Example 368 CONTENTS xi 9.9 On the Use of OLS in the Estimation of Simultaneous Equations Models 369 Working s Concept of Identification 371 Recursive Systems 373 Estimation of Cobb-Douglas Production Functions 373 9. 10 Exogeneity and Causality 375 Weak Exogeneity 378 Superexogeneity 378 Strong Exogeneity 378 Granger Causality 379 Granger Causality and Exogeneity 380 Tests for Exogeneity 380 9.11 Some Problems with Instrumental Variable Methods 381 Summary 382 Exercises 383 Appendix to Chapter 9 386 10 Nonlinear Regressions, Models of Expectations, and Nonnormality 391 What is in this Chapter? 391 10.1 Introduction 392 10.2 The Newton-Raphson Method 392 10.3 Nonlinear Least Squares 393 The Gauss-Newton Method 393 10.4 Models of Expectations 394 10.5 Naive Models of Expectations 395 10.6 The Adaptive Expectations Model 397 10.7 Estimation with the Adaptive Expectations Model 399 Estimation in the Autoregressive Form 399 Estimation in the Distributed Lag Form 400 10.8 Two Blustrative Examples 401 10.9 Expectational Variables and Adjustment Lags 405 10.10 Partial Adjustment with Adaptive Expectations 409 10.11 Alternative Distributed Lag Models: Polynomial Lags 411 Finite Lags: The Polynomial Lag 412 Illustrative Example 415 Choosing the Degree of the Polynomial 416 10.12 Rational Lags 417 10.13 Rational Expectations 419 10.14 Tests for Rationality 422 10.15 Estimation of a Demand and Supply Model Under Rational Expectations 424 Case 1 424 Case 2 425 Illustrative Example 428 10.16 The Serial Correlation Problem in Rational Expectations Models 431 x¡¡ CONTENTS 10.17 Nonnormality of Errors 431 Tests for Normality 432 10.18 Data Transformations 433 Summary 433 Exercises 435 11 Errors in Variables 437 What is in this Chapter? 437 11.1 Introduction 437 11.2 The Classical Solution for a Single-Equation Model with One Explanatory Variable 438 11.3 The Single-Equation Model with Two Explanatory Variables 441 Two Explanatory Variables: One Measured with Error 441 Illustrative Example 444 Two Explanatory Variables: Both Measured with Error 446 11.4 Reverse Regression 449 11.5 Instrumental Variable Methods 451 11.6 Proxy Variables 454 Coefficient of the Proxy Variable 456 11.7 Some Other Problems 457 The Case of Multiple Equations 458 Correlated Errors 459 Summary 459 Exercises 461 PART Ш SPECIAL TOPICS 463 12 Diagnostic Checking, Model Selection, and Specification Testing 465 What is in this Chapter? 465 12.1 Introduction 465 12.2 Diagnostic Tests Based on Least Squares Residuals 466 Tests for Omitted Variables 467 Tests for ARCH Effects 468 12.3 Problems with Least Squares Residuals 469 12.4 Some Other Types of Residuals 470 Predicted Residuals and Studentized Residuals 470 Dummy Variable Method for Studentized Residuals 471 BLUS Residuals 472 Recursive Residuals 472 Illuslrative Example 474 12.5 DFHTS and Bounded Influence Estimation 476 Illustrative Example 478 12.6 Model Selection 479 Hypothesis-Testing Search 480 Interpretive Search 4g 1 CONTENTS хШ Simplification Search 481 Proxy Variable Search 481 Data Selection Search 482 Post-Data Model Construction 482 Hendry s Approach to Model Selection 483 12.7 Selection of Regressors 484 Theiľs R2 Criterion 486 Criteria Based on Minimizing the Mean-Squared Error of Prediction 486 Akaiké s Information Criterion 488 12.8 Implied F-Ratios for the Various Criteria 488 Bayes Theorem and Posterior Odds for Model Selection 491 12.9 Cross-Validation 492 12.10 Hausman s Specification Error Test 494 An Application: Testing for Errors in Variables or Exogeneity 496 Some Illustrative Examples 497 An Omitted Variable Interpretation of the Hausman Test 498 12.11 The Plosser-Schwert-White Differencing Test 501 12.12 Tests for Nonnested Hypotheses 502 The Davidson and MacKinnon Test 502 The Encompassing Test 505 A Basic Problem in Testing Nonnested Hypotheses 506 Hypothesis Testing Versus Model Selection as a Research Strategy 506 Summary 506 Exercises 508 Appendix to Chapter 12 510 13 Introduction to Time-Series Analysis 513 What is in this Chapter? 513 13.1 Introduction 513 13.2 Two Methods of Time-Series Analysis: Frequency Domain and Time Domain 514 13.3 Stationary and Nonstatkmary Time Series 514 Strict Stationarity 515 Weak Stationarity 516 Properties of Autocorrelation Function 517 Nonstationarity 517 13.4 Some Useful Models for Time Series 517 Purely Random Process 517 Random Walk 518 Moving Average Process 519 Autoregressive Process 520 Aatoregressive Moving Average Process 522 Autoregressive Integrated Moving Average Process 524 xiv CONTENTS 13.5 Estimation of AR, MA, and ARMA Models 524 Estimation of MA Models 524 Estimation of ARMA Models 525 Residuals from the ARMA Models 526 Testing Goodness of Fit 527 13.6 The Box-Jenkins Approach 529 Forecasting from Box-Jenkins Models 531 Illustrative Example 532 Trend Elimination: The Traditional Method 534 A Summary Assessment 535 Seasonality in the Box-Jenkins Modeling 535 13.7 Rz Measures in Time-Series Models 536 Summary 540 Exercises 540 Data Sets 541 14 Vector Autoregressions, Unit Roots, and Cointegration 543 What is in this Chapter? 543 14.1 Introduction 543 14.2 Vector Autoregressions 544 14.3 Problems with VAR Models in Practice 546 14.4 Unit Roots 547 14.5 Unit Root Tests 548 Dickey-Fuller Test 548 The Serial Correlation Problem 549 The Low Power of Unit Root Tests 550 The DF-GLS Test 550 What are the Null and Alternative Hypotheses in Unit Root Tests? 550 Tests with Stationarity as Null 552 Confirmatory Analysis 553 Panel Data Unit Root Tests 554 Structural Change and Unit Roots 555 14.6 Cointegration 556 14.7 The Cointegrating Regression 557 14.8 Vector Autoregressions and Cointegration 560 14.9 Cointegration and Error Correction Models 564 14.10 Tests for Cointegration 565 14.11 Cointegration and Testing of the REH and МЕН 566 14.12 A Summary Assessment of Cointegration 568 Summary 569 Exercises 570 15 Panel Data Analysis What is in this Chapter? 15.1 Introduction 15.2 The LSDV or Fixed Effects Model 15.3 The Random Effects Model 15.4 Fixed Effects Versus Random Effects Hausman Test Breusch and Pagan Test 15.5 The SUR Model 15.6 Dynamic Panel Data Models 15.7 The Random Coefficient Model CONTENTS xv 573 573 573 574 575 578 578 579 579 580 581 Summary 583 16 Large-Sample Theory 585 What is in this Chapter? 585 16.1 The Maximum Likelihood Method 585 16.2 Methods of Solving the Likelihood Equations 586 16.3 The Cramer-Rao Lower Bound 588 16.4 Large-Sample Tests Based on ML 588 16.5 GIVE and GMM 589 Summary 591 17 Small-Sample Inference: Resampling Methods 593 What is in this Chapter? 593 17.1 Introduction 593 17.2 Monte Carlo Methods 594 More Efficient Monte Carlo Methods 595 Response Surfaces 595 17.3 Resampling Methods: Jackknife and Bootstrap 595 Some Illustrative Examples 597 Other Issues Relating to Bootstrap 598 17.4 Bootstrap Confidence Intervals 599 17.5 Hypothesis Testing with the Bootstrap 599 17.6 Bootstrapping Residuals Versus Bootstrapping the Data 600 17.7 NonllD Errors and Nonstationary Models 601 Heteroskedasticity and Autocorrelation 601 Unit Root Tests Based on the Bootstrap 601 Cointegration Tests 601 17.8 Miscellaneous Other Applications 602 xvi CONTENTS Summary 602 Appendices 605 Appendix A: Data Sets 605 Appendix B: Data Sets on the Web 613 Appendix C: Computer Programs 615 Index 617
adam_txt	Contents Foreword xvii Preface to the Second Edition xix Preface to the Third Edition xxiii Obituary xxv PART I INTRODUCTION AND THE LINEAR REGRESSION MODEL 1 1 What is Econometrics? 3 What is in this Chapter? 3 1.1 What is Econometrics? 3 1.2 Economic and Econometric Models 4 1.3 The Aims and Methodology of Econometrics 6 1.4 What Constitutes a Test of an Economic Theory? 9 Summary and an Outline of the Book 9 2 Statistical Background and Matrix Algebra 11 What is in this Chapter? 11 2.1 Introduction 12 2.2 Probability 12 Addition Rules of Probability 13 Conditional Probability and the Multiplication Rule 14 Bayes' Theorem 15 Summation and Product Operations 15 2.3 Random Variables and Probability Distributions 17 Joint, Marginal, and Conditional Distributions 18 Illustrative Example 18 2.4 The Normal Probability Distribution and Related Distributions 19 The Normal Distribution 19 Related Distributions 20 v¡ CONTENTS 2.5 Classical Statistical Inference 21 Point Estimation 22 2.6 Properties of Estimators 23 Unbiasedness 23 Efficiency 24 Consistency 24 Other Asymptotic Properties 25 2.7 Sampling Distributions for Samples from a Normal Population 26 2.8 Interval Estimation 27 2.9 Testing of Hypotheses 28 2.10 Relationship Between Confidence Interval Procedures and Tests of Hypotheses 32 2.11 Combining Independent Tests 33 Summary 33 Exercises 34 Appendix to Chapter 2 41 Matrix Algebra 41 Exercises on Matrix Algebra 56 3 Simple Regression 59 What is in this Chapter? 59 3.1 Introduction 59 3.2 Specification of the Relationships 61 3.3 The Method of Moments 65 Illustrative Example 66 3.4 The Method of Least Squares 68 Reverse Regression 71 Illustrative Example 72 3.5 Statistical Inference in the Linear Regression Model 75 Illustrative Example 77 Confidence Intervals for α, β, and σ2 78 Testing of Hypotheses 79 Example of Comparing Test Scores from the GRE and GMAT Tests 81 Regression with No Constant Term 82 3.6 Analysis of Variance for the Simple Regression Model 83 3.7 Prediction with the Simple Regression Model 84 Prediction of Expected Values 86 Illustrative Example 87 3.8 Outliers 88 Some Illustrative Examples 89 3.9 Alternative Functional Forms for Regression Equations 94 Illustrative Example 97 3.10 Inverse Prediction in the Least Squares Regression Model 99 3.11 Stochastic Regressors 101 CONTENTS vii 3.12 The Regression Fallacy 102 The Bivariate Normal Distribution 102 Galton' s Result and the Regression Fallacy 104 A Note on the Term: "Regression" 104 Summary 105 Exercises 106 Appendix to Chapter 3 112 4 Multiple Regression 127 What is in this Chapter? 127 4.1 Introduction 127 4.2 A Model with Two Explanatory Variables 129 The Least Squares Method 130 Illustrative Example 132 4.3 Statistical Inference in the Multiple Regression Model 134 Illustrative Example 135 Formulas for the General Case of к Explanatory Variables 139 Some Illustrative Examples 140 4.4 Interpretation of the Regression Coefficients 143 Illustrative Example 145 4.5 Partial Correlations and Multiple Correlation 146 4.6 Relationships Among Simple, Partial, and Multiple Correlation Coefficients 147 Two Illustrative Examples 148 4.7 Prediction in the Multiple Regression Model 153 Illustrative Example 153 4.8 Analysis of Variance and Tests of Hypotheses 154 Nested and Nonnested Hypotheses 156 Tests for Linear Functions of Parameters 157 Illustrative Example 158 4.9 Omission of Relevant Variables and Inclusion of Irrelevant Variables 159 Omission of Relevant Variables 160 Example 1: Demand for Food in the United States 161 Example 2: Production Functions and Management Bias 162 Inclusion of Irrelevant Variables ' 163 4.10 Degrees of Freedom and R 164 4.11 Tests for Stability 168 The Analysis of Variance Test 168 Example 1: Stability of the Demand for Food Function 169 Example 2: Stability of Production Functions 170 Predictive Tests for Stability 173 Illustrative Example 173 4.12 The LR, W, and LM Tests 176 Illustrative Example 176 viiî CONTENTS Summary 1" Exercises 1^9 Appendix to Chapter 4 185 The Multiple Regression Model in Matrix Notation 185 Data Sets 192 PART Π VIOLATION OF THE ASSUMPTIONS OF THE BASIC MODEL 197 5 Heteroskedasticity 199 What is in this Chapter? 199 5.1 Introduction 199 Illustrative Example 200 5.2 Detection of Heteroskedasticity 202 Illustrative Example 202 Some Other Tests 203 Illustrative Example 205 An Intuitive Justification for the Breusch-Pagan Test 206 5.3 Consequences of Heteroskedasticity 207 Estimation of the Variance of the OLS Estimator Under Heteroskedasticity 209 5.4 Solutions to the Heteroskedasticity Problem 209 Illustrative Example 211 5.5 Heteroskedasticity and the Use of Deflators 212 Illustrative Example: The Density Gradient Model 215 5.6 Testing the Linear Versus Log-Linear Functional Form 217 The Box -Сох Test 217 The BM Test 219 The PE Test 219 Summary 220 Exercises 221 Appendix to Chapter 5 224 Generalized Least Squares 224 6 Autocorrelation 227 What is in this Chapter? 227 6.1 Introduction 227 6.2 Durbin- Watson Test 228 Illustrative Example 229 6.3 Estimation in Levels Versus First Differences 230 Some Illustrative Examples 232 6.4 Estimation Procedures with Autocorrelated Errors 234 Iterative Procedures 236 Grid-Search Procedures 237 Illustrative Example 238 6.5 Effect of AR(1) Errors on OLS Estimates 238 CONTENTS їх 6.6 Some Further Comments on the DW Test 242 The von Neumann Ratio 243 The Berenblut-Webb Test 243 6.7 Tests for Serial Correlation in Models with Lagged Dependent Variables 245 Durbin' s /г -Test 246 Durbin' s Alternative Test 246 Illustrative Example 247 6.8 A General Test for Higher-Order Serial Correlation: The LM Test 248 6.9 Strategies When the DW Test Statistic is Significant 249 Errors Not ARCI) 249 Autocorrelation Caused by Omitted Variables 250 Serial Correlation Due to Misspecified Dynamics 252 The Wald Test 253 Illustrative Example 254 6.10 Trends and Random Walks 255 Spurious Trends 257 Differencing and Long-Run Effects: The Concept of Cointegration 258 6.11 ARCH Models and Serial Correlation 260 6.12 Some Comments on the DW Test and Durbin's й -Test and i-Test 262 Summary 262 Exercises 264 7 Multicollinearity 267 What is in this Chapter? 267 7.1 Introduction 268 7.2 Some Illustrative Examples 268 7.3 Some Measures of Multicollinearity 272 7.4 Problems with Measuring Multicollinearity 274 7.5 Solutions to the Multicollinearity Problem: Ridge Regression 278 7.6 Principal Component Regression 281 7.7 Dropping Variables 286 7.8 Miscellaneous Other Solutions 289 Using Ratios or First Differences 289 Using Extraneous Estimates 289 Getting More Data 291 Summary 291 Exercises 291 Appendix to Chapter 7 293 Linearly Dependent Explanatory Variables 293 8 Dummy Variables and Truncated Variables 301 What is in mis Chapter? 301 8.1 Introduction 301 CONTENTS 8.2 Dummy Variables for Changes in the Intercept Term 302 Illustrative Example 305 Two More ïïlustrative Examples 306 8.3 Dummy Variables for Changes in Slope Coefficients 307 8.4 Dummy Variables for Cross-Equation Constraints 310 8.5 Dummy Variables for Testing Stability of Regression Coefficients 313 8.6 Dummy Variables Under Heteroskedasticity and Autocorrelation 316 8.7 Dummy Dependent Variables 317 8.8 The Linear Probability Model and the Linear Discriminant Function 318 The Linear Probability Model 318 The Linear Discriminant Function 320 8.9 The Probit and Logit Models 322 Illustrative Example 324 The Problem of Disproportionate Sampling 325 Prediction of Effects of Changes in the Explanatory Variables 327 Measuring Goodness of Fit 327 8.10 Illustrative Example 329 8.11 Truncated Variables: The Tobit Model 333 Some Examples 333 Method of Estimation 334 Limitations of the Tobit Model 335 The Truncated Regression Model 336 Summary 338 Exercises 339 Simultaneous Equations Models 343 What is in this Chapter? 343 9.1 Introduction 343 9.2 Endogenous and Exogenous Variables 345 9.3 The Identification Problem: Identification through Reduced Form 346 Illustrative Example 348 9.4 Necessary and Sufficient Conditions for Identification 351 Illustrative Example 353 9.5 Methods of Estimation: The Instrumental Variable Method 354 Measuring R2 356 Illustrative Example3 357 9.6 Methods of Estimation: The Two-Stage Least Squares Method 360 Computing Standard Errors 361 Illustrative Example 363 9.7 The Question of Normalization 366 9.8 The Limited-Information Maximum Likelihood Method 367 Illustrative Example 368 CONTENTS xi 9.9 On the Use of OLS in the Estimation of Simultaneous Equations Models 369 Working's Concept of Identification 371 Recursive Systems 373 Estimation of Cobb-Douglas Production Functions 373 9. 10 Exogeneity and Causality 375 Weak Exogeneity 378 Superexogeneity 378 Strong Exogeneity 378 Granger Causality 379 Granger Causality and Exogeneity 380 Tests for Exogeneity 380 9.11 Some Problems with Instrumental Variable Methods 381 Summary 382 Exercises 383 Appendix to Chapter 9 386 10 Nonlinear Regressions, Models of Expectations, and Nonnormality 391 What is in this Chapter? 391 10.1 Introduction 392 10.2 The Newton-Raphson Method 392 10.3 Nonlinear Least Squares 393 The Gauss-Newton Method 393 10.4 Models of Expectations 394 10.5 Naive Models of Expectations 395 10.6 The Adaptive Expectations Model 397 10.7 Estimation with the Adaptive Expectations Model 399 Estimation in the Autoregressive Form 399 Estimation in the Distributed Lag Form 400 10.8 Two Blustrative Examples 401 10.9 Expectational Variables and Adjustment Lags 405 10.10 Partial Adjustment with Adaptive Expectations 409 10.11 Alternative Distributed Lag Models: Polynomial Lags 411 Finite Lags: The Polynomial Lag 412 Illustrative Example 415 Choosing the Degree of the Polynomial 416 10.12 Rational Lags 417 10.13 Rational Expectations 419 10.14 Tests for Rationality 422 10.15 Estimation of a Demand and Supply Model Under Rational Expectations 424 Case 1 424 Case 2 425 Illustrative Example 428 10.16 The Serial Correlation Problem in Rational Expectations Models 431 x¡¡ CONTENTS 10.17 Nonnormality of Errors 431 Tests for Normality 432 10.18 Data Transformations 433 Summary 433 Exercises 435 11 Errors in Variables 437 What is in this Chapter? 437 11.1 Introduction 437 11.2 The Classical Solution for a Single-Equation Model with One Explanatory Variable 438 11.3 The Single-Equation Model with Two Explanatory Variables 441 Two Explanatory Variables: One Measured with Error 441 Illustrative Example 444 Two Explanatory Variables: Both Measured with Error 446 11.4 Reverse Regression 449 11.5 Instrumental Variable Methods 451 11.6 Proxy Variables 454 Coefficient of the Proxy Variable 456 11.7 Some Other Problems 457 The Case of Multiple Equations 458 Correlated Errors 459 Summary 459 Exercises 461 PART Ш SPECIAL TOPICS 463 12 Diagnostic Checking, Model Selection, and Specification Testing 465 What is in this Chapter? 465 12.1 Introduction 465 12.2 Diagnostic Tests Based on Least Squares Residuals 466 Tests for Omitted Variables 467 Tests for ARCH Effects 468 12.3 Problems with Least Squares Residuals 469 12.4 Some Other Types of Residuals 470 Predicted Residuals and Studentized Residuals 470 Dummy Variable Method for Studentized Residuals 471 BLUS Residuals 472 Recursive Residuals 472 Illuslrative Example 474 12.5 DFHTS and Bounded Influence Estimation 476 Illustrative Example 478 12.6 Model Selection 479 Hypothesis-Testing Search 480 Interpretive Search 4g 1 CONTENTS хШ Simplification Search 481 Proxy Variable Search 481 Data Selection Search 482 Post-Data Model Construction 482 Hendry's Approach to Model Selection 483 12.7 Selection of Regressors 484 Theiľs R2 Criterion 486 Criteria Based on Minimizing the Mean-Squared Error of Prediction 486 Akaiké' s Information Criterion 488 12.8 Implied F-Ratios for the Various Criteria 488 Bayes' Theorem and Posterior Odds for Model Selection 491 12.9 Cross-Validation 492 12.10 Hausman' s Specification Error Test 494 An Application: Testing for Errors in Variables or Exogeneity 496 Some Illustrative Examples 497 An Omitted Variable Interpretation of the Hausman Test 498 12.11 The Plosser-Schwert-White Differencing Test 501 12.12 Tests for Nonnested Hypotheses 502 The Davidson and MacKinnon Test 502 The Encompassing Test 505 A Basic Problem in Testing Nonnested Hypotheses 506 Hypothesis Testing Versus Model Selection as a Research Strategy 506 Summary 506 Exercises 508 Appendix to Chapter 12 510 13 Introduction to Time-Series Analysis 513 What is in this Chapter? 513 13.1 Introduction 513 13.2 Two Methods of Time-Series Analysis: Frequency Domain and Time Domain 514 13.3 Stationary and Nonstatkmary Time Series 514 Strict Stationarity 515 Weak Stationarity 516 Properties of Autocorrelation Function 517 Nonstationarity 517 13.4 Some Useful Models for Time Series 517 Purely Random Process 517 Random Walk 518 Moving Average Process 519 Autoregressive Process 520 Aatoregressive Moving Average Process 522 Autoregressive Integrated Moving Average Process 524 xiv CONTENTS 13.5 Estimation of AR, MA, and ARMA Models 524 Estimation of MA Models 524 Estimation of ARMA Models 525 Residuals from the ARMA Models 526 Testing Goodness of Fit 527 13.6 The Box-Jenkins Approach 529 Forecasting from Box-Jenkins Models 531 Illustrative Example 532 Trend Elimination: The Traditional Method 534 A Summary Assessment 535 Seasonality in the Box-Jenkins Modeling 535 13.7 Rz Measures in Time-Series Models 536 Summary 540 Exercises 540 Data Sets 541 14 Vector Autoregressions, Unit Roots, and Cointegration 543 What is in this Chapter? 543 14.1 Introduction 543 14.2 Vector Autoregressions 544 14.3 Problems with VAR Models in Practice 546 14.4 Unit Roots 547 14.5 Unit Root Tests 548 Dickey-Fuller Test 548 The Serial Correlation Problem 549 The Low Power of Unit Root Tests 550 The DF-GLS Test 550 What are the Null and Alternative Hypotheses in Unit Root Tests? 550 Tests with Stationarity as Null 552 Confirmatory Analysis 553 Panel Data Unit Root Tests 554 Structural Change and Unit Roots 555 14.6 Cointegration 556 14.7 The Cointegrating Regression 557 14.8 Vector Autoregressions and Cointegration 560 14.9 Cointegration and Error Correction Models 564 14.10 Tests for Cointegration 565 14.11 Cointegration and Testing of the REH and МЕН 566 14.12 A Summary Assessment of Cointegration 568 Summary 569 Exercises 570 15 Panel Data Analysis What is in this Chapter? 15.1 Introduction 15.2 The LSDV or Fixed Effects Model 15.3 The Random Effects Model 15.4 Fixed Effects Versus Random Effects Hausman Test Breusch and Pagan Test 15.5 The SUR Model 15.6 Dynamic Panel Data Models 15.7 The Random Coefficient Model CONTENTS xv 573 573 573 574 575 578 578 579 579 580 581 Summary 583 16 Large-Sample Theory 585 What is in this Chapter? 585 16.1 The Maximum Likelihood Method 585 16.2 Methods of Solving the Likelihood Equations 586 16.3 The Cramer-Rao Lower Bound 588 16.4 Large-Sample Tests Based on ML 588 16.5 GIVE and GMM 589 Summary 591 17 Small-Sample Inference: Resampling Methods 593 What is in this Chapter? 593 17.1 Introduction 593 17.2 Monte Carlo Methods 594 More Efficient Monte Carlo Methods 595 Response Surfaces 595 17.3 Resampling Methods: Jackknife and Bootstrap 595 Some Illustrative Examples 597 Other Issues Relating to Bootstrap 598 17.4 Bootstrap Confidence Intervals 599 17.5 Hypothesis Testing with the Bootstrap 599 17.6 Bootstrapping Residuals Versus Bootstrapping the Data 600 17.7 NonllD Errors and Nonstationary Models 601 Heteroskedasticity and Autocorrelation 601 Unit Root Tests Based on the Bootstrap 601 Cointegration Tests 601 17.8 Miscellaneous Other Applications 602 xvi CONTENTS Summary 602 Appendices 605 Appendix A: Data Sets 605 Appendix B: Data Sets on the Web 613 Appendix C: Computer Programs 615 Index 617
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spelling	Maddala, Gangadharrao S. 1933- Verfasser (DE-588)120849844 aut Introduction to econometrics G.S. Maddala 3. ed., repr. Chichester [u.a.] Wiley 2006 XXVII, 636 S. graf. Darst. txt rdacontent n rdamedia nc rdacarrier Ökonometrie Ökonometrie (DE-588)4132280-0 gnd rswk-swf 1\p (DE-588)4151278-9 Einführung gnd-content 2\p (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s DE-604 http://www.loc.gov/catdir/description/wiley035/00068506.html Publisher description Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016329466&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk
spellingShingle	Maddala, Gangadharrao S. 1933- Introduction to econometrics Ökonometrie Ökonometrie (DE-588)4132280-0 gnd
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title	Introduction to econometrics
title_auth	Introduction to econometrics
title_exact_search	Introduction to econometrics
title_exact_search_txtP	Introduction to econometrics
title_full	Introduction to econometrics G.S. Maddala
title_fullStr	Introduction to econometrics G.S. Maddala
title_full_unstemmed	Introduction to econometrics G.S. Maddala
title_short	Introduction to econometrics
title_sort	introduction to econometrics
topic	Ökonometrie Ökonometrie (DE-588)4132280-0 gnd
topic_facet	Ökonometrie Einführung Lehrbuch
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