This book brings together presentations of some of the fundamental new research in dynamic econometric modeling.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Editors' introduction; List of contributors; Part 1. Dynamic Structural Modeling: 1. Efficient instrumental variables estimation of systems of implicit heterogeneous nonlinear dynamic equations with nonspherical errors Charles Bates and Halbert White; 2. Envelope consistent functional separability Ernst R. Berndt; 3. Flexible functional forms for profit functions and global curvature conditions W. Erwin Diewert and Lawrence Ostensoe; 4. Likelihood inference in the nonlinear regression model with explosive linear dynamics Ian Domowitz and Lars Muus; 5. Exact inference in models with autoregressive conditional heteroscedasticity John Geweke; 6. Control of a linear regression process with unknown parameters Nicholas M. Kiefer and Yaw Nyarko; 7. Some tests of nonparametric regression models Adonis John Yatchew; Part II. Linear Time Series Modeling: 8. A central-limit result for instrumental variables estimators of linear time series models Lars Peter Hansen; 9. Exact and approximate distribution of the t ratio test statistic in an AR(1) model Alberto Holly and Georg Michael Rockinger; 10. The use of ARIMA models in unobserved-components estimation: an application to Spanish monetary control Agustin Maravall; Part III. Chaotic Attractor Modeling: 11. The aggregation-theoretic monetary aggregates are chaotic and have strange attractors: an econometric application of mathematical chaos William A. Barnett and Ping Chen; 12. Theorems on distinguishing deterministic from random systems W. A. Brock and W. D. Dechert; Part IV. Applications: 13. Investment and sales: some empirical evidence Andrew B. Abel and Olivier J. Blanchard; 14. Me and my shadow: estimating the size of the U.S. hidden economy from time series data Dennis J. Aigner, Friedrich Schneider, and Damayanti Ghosh; 15. Estimating structural models of unemployment and job duration Dale T. Mortensen and George R. Neumann; 16. Comparison of dynamic factor demand models Peter E. Rossi.
Editors' introduction; List of contributors; Part 1. Dynamic Structural Modeling: 1. Efficient instrumental variables estimation of systems of implicit heterogeneous nonlinear dynamic equations with nonspherical errors Charles Bates and Halbert White; 2. Envelope consistent functional separability Ernst R. Berndt; 3. Flexible functional forms for profit functions and global curvature conditions W. Erwin Diewert and Lawrence Ostensoe; 4. Likelihood inference in the nonlinear regression model with explosive linear dynamics Ian Domowitz and Lars Muus; 5. Exact inference in models with autoregressive conditional heteroscedasticity John Geweke; 6. Control of a linear regression process with unknown parameters Nicholas M. Kiefer and Yaw Nyarko; 7. Some tests of nonparametric regression models Adonis John Yatchew; Part II. Linear Time Series Modeling: 8. A central-limit result for instrumental variables estimators of linear time series models Lars Peter Hansen; 9. Exact and approximate distribution of the t ratio test statistic in an AR(1) model Alberto Holly and Georg Michael Rockinger; 10. The use of ARIMA models in unobserved-components estimation: an application to Spanish monetary control Agustin Maravall; Part III. Chaotic Attractor Modeling: 11. The aggregation-theoretic monetary aggregates are chaotic and have strange attractors: an econometric application of mathematical chaos William A. Barnett and Ping Chen; 12. Theorems on distinguishing deterministic from random systems W. A. Brock and W. D. Dechert; Part IV. Applications: 13. Investment and sales: some empirical evidence Andrew B. Abel and Olivier J. Blanchard; 14. Me and my shadow: estimating the size of the U.S. hidden economy from time series data Dennis J. Aigner, Friedrich Schneider, and Damayanti Ghosh; 15. Estimating structural models of unemployment and job duration Dale T. Mortensen and George R. Neumann; 16. Comparison of dynamic factor demand models Peter E. Rossi.
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