The Structural Econometric Time Series Analysis Approach

, by Edited by Arnold Zellner , Franz C. Palm

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ISBN: 9780521187435 | 0521187435
Cover: Paperback
Copyright: 2/17/2011

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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.

Introduction
The SEMTSA Approach:
Time series analysis and simultaneous equation econometric models
Statistical analysis of econometric models
Structural econometric modeling and time series analysis: an integrated approach
Time series analysis, forecasting and econometric modeling: the structural econometric modeling, times series analysis (SEMTSA) approach
Large sample estimation and testing procedures for dynamic equation systems
Selected Applications:
Time series and structural analysis of monetary models of the US economy
Time series versus structural models: a case study of Canadian manufacturing inventory behavior
Time series analysis of the German hyperinflation
A time series analysis of seasonality in econometric models
The behavior of speculative prices and the consistency of economic models
A comparison of the stochastic processes of structural and time series exchange rate models
Encompassing univariate models in multivariate times series: a case study
Macroeconomic Forecasting and Modeling:
Macroeconomic forecasting using pooled international data A. Garcia-Ferrer
Forecasting international growth rates using Bayesian shrinkage and other procedures
Turning points in economic time series, loss structures and Bayesian forecasting A. Zellner
Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter and pooling techniques
Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates
Pooling in dynamic panel data models: an application to forecasting GDP growth rates
Forecasting turning points in countries' output growth rates: a response to Milton Friedman
Using Bayesian techniques for data pooling in regional payroll forecasting
Forecasting turning points in metropolitan employment growth rates using Bayesian techniques
A note on aggregation, disaggregation and forecasting performance
The Marshallian macroeconomic model
Bayesian modeling of economies and data requirements
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The Structural Econometric Time Series Analysis Approach

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