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The Structural Econometric Time Series Analysis Approach

$108.00 (Z)

A. Zellner, F. C. Palm, P. K. Trivedi, P. Evans, C. I. Plosser, R. I. Webb, F. W. Ahking, S. M. Miller, A. Maravall, A. Mathis, A. Garcia-Ferrer, R. A. Highfield, C. Hong, G. M. Gulati, C. Min, A. J. Hoogstrate, G. A. Pfann, J. P. LeSage, M. Magura, J. Tobias, B. Chen
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  • Date Published: February 2011
  • availability: Manufactured on demand: supplied direct from the printer
  • format: Paperback
  • isbn: 9780521187435

$108.00 (Z)
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About the Authors
  • 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.

    • Includes basic material on how to construct econometric models, with applied examples, including the Marshallian Macroeconomic Model
    • Provides an overview of model checking and construction
    • Brings together key texts in one volume
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    Product details

    • Date Published: February 2011
    • format: Paperback
    • isbn: 9780521187435
    • length: 736 pages
    • dimensions: 229 x 152 x 37 mm
    • weight: 0.97kg
    • availability: Manufactured on demand: supplied direct from the printer
  • Table of Contents

    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.

  • Editors

    Arnold Zellner, University of Chicago

    Franz C. Palm, Universiteit Maastricht, Netherlands

    Contributors

    A. Zellner, F. C. Palm, P. K. Trivedi, P. Evans, C. I. Plosser, R. I. Webb, F. W. Ahking, S. M. Miller, A. Maravall, A. Mathis, A. Garcia-Ferrer, R. A. Highfield, C. Hong, G. M. Gulati, C. Min, A. J. Hoogstrate, G. A. Pfann, J. P. LeSage, M. Magura, J. Tobias, B. Chen

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