Bootstrap Tests for Regression Models [Paperback]

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.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 Bootsl‡