Introduction.- The t-statistic under dynamic misspecification.- The model.- Properties of the estimators.- The distribution of the quasi t-statistic.- Invariance results.- Monte Carlo experimentation.- Consumer theory and the Rotterdam model.- Commodity space and budget set.- Preferences, direct utility function and Marshallian demand.- Cost function and Hicksian demand.- The Rotterdam model.- The Rotterdam model in matrix form.- Robust estimation.- Quasi-maximum likelihood estimation.- Estimation of the covariance matrix of the quasi-maximum likelihood estimator.- Testing for homogeneity.- Anderson`s U test if the errors are time-independent (LRU).- Functional equivalence between the LRU and Laitinen`s statistic.- Likelihood ratio test if the errors are VAR(p).- The asymptotic distribution of the LRU statistic under dynamic misspecification.- The robust Wald test.- Summary.- Monte Carlo experimentation.- Data.- The data-generating process.- Experiments.- Simulation results.- Conclusions.
This book deals with the omitted variable tests for a multivariate time-series regression model. What are the consequences of testing for the omission of a variable when the model is dynamically misspecified? What is the small sample bias of the omitted variable test when the model dynamics is correctly specificfied? The answers to these questions are proposed in this book. As an empirical illustration, the analysis is applied to the homogeneity test of a demand system. I particularly thank Professor Dr. Philippe J. Deschamps who draw my attention on this subject and who made very helpful comments and sugges tions. Additionally, I would like to thank Professor Dr. Reiner Wolff for his comments especially on the chapter dealing with consumer theory. Special thanks go to Maria Jose Redondo, who read this book several times and for the inspiring discussions with her. I would also like to thank Dr. Ali Vak ili (always ready to answer any questions in mathematics), Prof. Dr. Hans Wolf gang Brachinger, Curzio De Gottardi, Peter Mantsch, Dr. Paul-Andre Monney, Dr. Uwe Steinhauser, Leon Stroeks and Dr. Peter Windlin. Frances Angell improved the English of this work. The research for this book had been financially suppürted by the Univer site de Fribourg (Switzerland). Finally, I appreciated the support from Springer-Verlag and I thank Dr.
1. Introduction.- 2. The t-statistic under dynamic misspecification.- 2.1 The model.- 2.2 Properties of the estimators ?.- 2.3 The distribution of the quasi t-statistic.- 2.4 Invariance results.- 2.5 Monte Carlo experimentation.- 3. Consumer theory and the Rotterdam model.- 3.1 Commodity space and budget set.- 3.2 Preferences, direct utility function and Marshallian demand.- 3.3 Cost function and Hicksian demand.- 3.4 The Rotterdam model.- 3.5 The Rotterdam model in matrix form.- 4. Robust estimation.- 4.1 Quasi-maximum likelihood estimation.- 4.2 Estimation of the covariance matrix of the quasi-maximum likelihood estimator.- 5. Testing for homogeneity.- 5.1 Anderson's U test if the errors are time-independent (LRU).- 5.2 Functional equivalence between the LRU and Laitinen's statistic.- 5.3 Likelihood ratio test if the errors are VAR(p).- 5.4 The distribution of the LRU statistic under dynamic misspecification.- 5.5 The robust Wald test.- 5.6 Summary.- 6. Monte Carlo experimentation.- 6.1 Data.- 6.2 The data-generating process.- 6.3 Experiments.- 6.4 Simulation results.- 7. Conclusions.- A. Proof of proposition 5.4.- B. Data.- C. Values of the population parameters.- D. Algorithm for the MA(1) parameters.- List of Figures.- List of Tables.
Inhaltsverzeichnis
Introduction.- The t-statistic under dynamic misspecification.- The model.- Properties of the estimators.- The distribution of the quasi t-statistic.- Invariance results.- Monte Carlo experimentation.- Consumer theory and the Rotterdam model.- Commodity space and budget set.- Preferences, direct utility function and Marshallian demand.- Cost function and Hicksian demand.- The Rotterdam model.- The Rotterdam model in matrix form.- Robust estimation.- Quasi-maximum likelihood estimation.- Estimation of the covariance matrix of the quasi-maximum likelihood estimator.- Testing for homogeneity.- Anderson`s U test if the errors are time-independent (LRU).- Functional equivalence between the LRU and Laitinen`s statistic.- Likelihood ratio test if the errors are VAR(p).- The asymptotic distribution of the LRU statistic under dynamic misspecification.- The robust Wald test.- Summary.- Monte Carlo experimentation.- Data.- The data-generating process.- Experiments.- Simulation results.- Conclusions.
Klappentext
This book deals with the omitted variable tests for a multivariate time-series regression model. What are the consequences of testing for the omission of a variable when the model is dynamically misspecified? What is the small sample bias of the omitted variable test when the model dynamics is correctly specificfied? The answers to these questions are proposed in this book. As an empirical illustration, the analysis is applied to the homogeneity test of a demand system. I particularly thank Professor Dr. Philippe J. Deschamps who draw my attention on this subject and who made very helpful comments and sugges tions. Additionally, I would like to thank Professor Dr. Reiner Wolff for his comments especially on the chapter dealing with consumer theory. Special thanks go to Maria Jose Redondo, who read this book several times and for the inspiring discussions with her. I would also like to thank Dr. Ali Vak ili (always ready to answer any questions in mathematics), Prof. Dr. Hans Wolf gang Brachinger, Curzio De Gottardi, Peter Mantsch, Dr. Paul-Andre Monney, Dr. Uwe Steinhauser, Leon Stroeks and Dr. Peter Windlin. Frances Angell improved the English of this work. The research for this book had been financially suppürted by the Univer site de Fribourg (Switzerland). Finally, I appreciated the support from Springer-Verlag and I thank Dr.
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