The Structural Econometric Time Series Analysis Approach

Herausgeber: Palm, Franz C.; Zellner, Arnold

• Broschiertes Buch

Produktbeschreibung

This text offers key texts in the theory and application of the Structural Econometric Time Series Analysis (SEMTSA) approach.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 736
• Erscheinungstermin: 25. November 2010
• Englisch
• Abmessung: 229mm x 152mm x 39mm
• Gewicht: 1046g
• ISBN-13: 9780521187435
• ISBN-10: 0521187435
• Artikelnr.: 32734624

Inhaltsangabe

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.