The Structural Econometric Time Series Analysis Approach

ISBN-13: 9780521814072 / Angielski / Twarda / 2004 / 736 str.

Franz Palm; Arnold Zellner

Opis

This book assembles key texts in the theory and applications of the Structural Econometric Time Series Analysis (SEMTSA) approach. The theory and applications of these procedures to a variety of econometric modeling and forecasting problems as well as Bayesian and non-Bayesian testing, shrinkage estimation and forecasting procedures are presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision.

Spis treści

Introduction; Part I. The SEMTSA Approach: 1. Time series analysis and simultaneous equation econometric models A. Zellner and F. C. Palm; 2. Statistical analysis of econometric models A. Zellner; 3. Structural econometric modeling and time series analysis: an integrated approach F. C. Palm; 4. Time series analysis, forecasting and econometric modeling: the structural econometric modeling, times series analysis (SEMTSA) approach A. Zellner; 5. Large sample estimation and testing procedures for dynamic equation systems F. Palm and A. Zellner; Part II. Selected Applications: 6. Time series and structural analysis of monetary models of the US economy A. Zellner and F. Palm; 7. Time series versus structural models: a case study of Canadian manufacturing inventory behavior P. K. Trivedi; 8. Time series analysis of the German hyperinflation P. Evans; 9. A time series analysis of seasonality in econometric models C. I. Plosser; 10. The behavior of speculative prices and the consistency of economic models R. I. Webb; 11. A comparison of the stochastic processes of structural and time series exchange rate models F. W. Ahking and S. M. Miller; 12. Encompassing univariate models in multivariate times series: a case study A. Maravall and A. Mathis; Part III. Macroeconomic Forecasting and Modeling: 13. Macroeconomic forecasting using pooled international data A. Garcia-Ferrer, R. A. Highfield, F. Palm and A. Zellner; 14. Forecasting international growth rates using Bayesian shrinkage and other procedures A. Zellner and C. Hong; 15. Turning points in economic time series, loss structures and Bayesian forecasting A. Zellner, C. Hong and G. M. Gulati; 16. Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter and pooling techniques A. Zellner, C. Hong and C. Min; 17. Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates C. Min and A. Zellner; 18. Pooling in dynamic panel data models: an application to forecasting GDP growth rates A. J. Hoogstrate, F. C. Palm and G. A. Pfann; 19. Forecasting turning points in countries' output growth rates: a response to Milton Friedman A. Zellner and C. Min; 20. Using Bayesian techniques for data pooling in regional payroll forecasting J. P. LeSage and M. Magura; 21. Forecasting turning points in metropolitan employment growth rates using Bayesian techniques J. P. LeSage; 22. A note on aggregation, disaggregation and forecasting performance A. Zellner and J. Tobias; 23. The Marshallian macroeconomic model A. Zellner; 24. Bayesian modeling of economies and data requirements A. Zellner and B. Chen.