This book contains an accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample performance of well-known asymptotic tests for regression models. This book uses the linear regression model as a framework for introducing simulation-based tests to help perform econometric analyses.
This book contains an accessible discussion examining computationally-intensive techniques and bootstrap methods, providing ways to improve the finite-sample performance of well-known asymptotic tests for regression models. This book uses the linear regression model as a framework for introducing simulation-based tests to help perform econometric analyses.
LESLIE GODFREY is Professor of Econometrics at the University of York, UK and a Fellow of the Journal of Econometrics. He has served on the editorial boards of Econometric Theory and Econometric Reviews. His articles have been published in leading journals, including Econometrica, Journal of Econometrics and Review of Economics and Statistics.
Inhaltsangabe
Preface PART I: TESTS FOR LINEAR REGRESSION MODELS Introduction Tests for the Classical Linear Regression Model Tests for Linear Regression Models Under Weaker Assumptions: Random Regressors and Non-Normal IID Errors Tests for Generalized Linear Regression Models Finite-Sample Properties of Asymptotic Tests Non-Standard Tests for Linear Regression Models Summary and Concluding Remarks PART II: SIMULATION-BASED TESTS: BASIC IDEAS Introduction Some Simple Examples of Tests for IID Variables and Key Concepts Simulation-Based Tests for Regression Models Asymptotic Properties of Bootstrap Tests The Double Bootstrap Summary and Concluding Remarks PART III: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH IID ERRORS: SOME STANDARD CASES Introduction A Monte Carlo Test of the Assumption of Normality Simulation-Based Tests for Heteroskedasticity Bootstrapping F Tests of Linear Coefficient Restrictions Bootstrapping LM Tests for Serial Correlation in Dynamic Regression Models Summary and Concluding Remarks PART IV: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH IID ERRORS: SOME NON-STANDARD CASES Introduction Bootstrapping Predictive Tests Using Bootstrap Methods with a Battery of OLS Diagnostic Tests Bootstrapping Tests for Structural Breaks Summary and Conclusions PART V: BOOTSTRAP METHODS FOR REGRESSION MODELS WITH NON-IID ERRORS Introduction Bootstrap Methods for Independent Heteroskedastic Errors Bootstrap Methods for Homoskedastic Autocorrelated Errors Bootstrap Methods for Heteroskedastic Autocorrelated Errors Summary and Concluding Remarks PART VI: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH NON-IID ERRORS Introduction Bootstrapping Heteroskedasticity-Robust Regression Specification Error Tests Bootstrapping Heteroskedasticity-Robust Autocorrelation Tests for Dynamic Models Bootstrapping Heteroskedasticity-Robust Structural Break Tests with an Unknown Breakpoint Bootstrapping Autocorrelation-Robust Hausman Tests Summary and Conclusions PART VII:Simulation-Based Tests for Non-Nested Regression Models Introduction Asymptotic Tests for Models with Non-Nested Regressors Bootstrapping Tests for Models with Non-Nested Regressors Bootstrapping the LLR Statistic with Non-Nested Models Summary and Concluding Remarks PART VIII: EPILOGUE Bibliography Author Index Subject Index
Preface PART I: TESTS FOR LINEAR REGRESSION MODELS Introduction Tests for the Classical Linear Regression Model Tests for Linear Regression Models Under Weaker Assumptions: Random Regressors and Non-Normal IID Errors Tests for Generalized Linear Regression Models Finite-Sample Properties of Asymptotic Tests Non-Standard Tests for Linear Regression Models Summary and Concluding Remarks PART II: SIMULATION-BASED TESTS: BASIC IDEAS Introduction Some Simple Examples of Tests for IID Variables and Key Concepts Simulation-Based Tests for Regression Models Asymptotic Properties of Bootstrap Tests The Double Bootstrap Summary and Concluding Remarks PART III: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH IID ERRORS: SOME STANDARD CASES Introduction A Monte Carlo Test of the Assumption of Normality Simulation-Based Tests for Heteroskedasticity Bootstrapping F Tests of Linear Coefficient Restrictions Bootstrapping LM Tests for Serial Correlation in Dynamic Regression Models Summary and Concluding Remarks PART IV: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH IID ERRORS: SOME NON-STANDARD CASES Introduction Bootstrapping Predictive Tests Using Bootstrap Methods with a Battery of OLS Diagnostic Tests Bootstrapping Tests for Structural Breaks Summary and Conclusions PART V: BOOTSTRAP METHODS FOR REGRESSION MODELS WITH NON-IID ERRORS Introduction Bootstrap Methods for Independent Heteroskedastic Errors Bootstrap Methods for Homoskedastic Autocorrelated Errors Bootstrap Methods for Heteroskedastic Autocorrelated Errors Summary and Concluding Remarks PART VI: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH NON-IID ERRORS Introduction Bootstrapping Heteroskedasticity-Robust Regression Specification Error Tests Bootstrapping Heteroskedasticity-Robust Autocorrelation Tests for Dynamic Models Bootstrapping Heteroskedasticity-Robust Structural Break Tests with an Unknown Breakpoint Bootstrapping Autocorrelation-Robust Hausman Tests Summary and Conclusions PART VII:Simulation-Based Tests for Non-Nested Regression Models Introduction Asymptotic Tests for Models with Non-Nested Regressors Bootstrapping Tests for Models with Non-Nested Regressors Bootstrapping the LLR Statistic with Non-Nested Models Summary and Concluding Remarks PART VIII: EPILOGUE Bibliography Author Index Subject Index
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