Testing for a unit root is now an essential part of time series analysis. This volume provides a critical overview and assessment of tests for a unit root in time series, developing the concepts necessary to understand the key theoretical and practical models in unit root testing.
Testing for a unit root is now an essential part of time series analysis. This volume provides a critical overview and assessment of tests for a unit root in time series, developing the concepts necessary to understand the key theoretical and practical models in unit root testing.
KERRY PATTERSON is Professor of Econometrics at the University of Reading. He has established an international reputation in econometrics and has published over 50 articles in leading journals, including the Journal of the Royal Statistical Society, the Review of Economics and Statistics, the Economic Journal and the International Journal of Forecasting. He is author of A Primer for Unit Root Testing and co-editor, with Terence Mills, of the Palgrave Handbook of Econometrics, both published by Palgrave.
Inhaltsangabe
Preface Introduction to Random Walks and Brownian Motion Why Distinguish Between Trend Stationary and Difference Stationary Processes? An Introduction to ARMA Models Bias and Bias Reduction in AR Models Confidence Intervals in AR Models Dickey-Fuller and Related Tests Improving the Power of Unit Root Tests Bootstrap Unit Root Tests Lag Selection and Multiple Tests Testing for Two (or More) Unit Roots Tests with Stationarity As the Hypothesis Combining Tests and Constructing Confidence Intervals Unit Root Tests for Seasonal Data Appendix 1: Random Variables Appendix 2: The Lag Operator and Lag Polynomials References Author Index Subject Index
Preface Introduction to Random Walks and Brownian Motion Why Distinguish Between Trend Stationary and Difference Stationary Processes? An Introduction to ARMA Models Bias and Bias Reduction in AR Models Confidence Intervals in AR Models Dickey-Fuller and Related Tests Improving the Power of Unit Root Tests Bootstrap Unit Root Tests Lag Selection and Multiple Tests Testing for Two (or More) Unit Roots Tests with Stationarity As the Hypothesis Combining Tests and Constructing Confidence Intervals Unit Root Tests for Seasonal Data Appendix 1: Random Variables Appendix 2: The Lag Operator and Lag Polynomials References Author Index Subject Index
Preface Introduction to Random Walks and Brownian Motion Why Distinguish Between Trend Stationary and Difference Stationary Processes? An Introduction to ARMA Models Bias and Bias Reduction in AR Models Confidence Intervals in AR Models Dickey-Fuller and Related Tests Improving the Power of Unit Root Tests Bootstrap Unit Root Tests Lag Selection and Multiple Tests Testing for Two (or More) Unit Roots Tests with Stationarity As the Hypothesis Combining Tests and Constructing Confidence Intervals Unit Root Tests for Seasonal Data Appendix 1: Random Variables Appendix 2: The Lag Operator and Lag Polynomials References Author Index Subject Index
Preface Introduction to Random Walks and Brownian Motion Why Distinguish Between Trend Stationary and Difference Stationary Processes? An Introduction to ARMA Models Bias and Bias Reduction in AR Models Confidence Intervals in AR Models Dickey-Fuller and Related Tests Improving the Power of Unit Root Tests Bootstrap Unit Root Tests Lag Selection and Multiple Tests Testing for Two (or More) Unit Roots Tests with Stationarity As the Hypothesis Combining Tests and Constructing Confidence Intervals Unit Root Tests for Seasonal Data Appendix 1: Random Variables Appendix 2: The Lag Operator and Lag Polynomials References Author Index Subject Index
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