Bringing together a collection of previously published work, this book provides a timely discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes and be useful for policy-making. Analytical relations between dynamic econometric structural models and empirical time series MVARMA, VAR, transfer function, and univariate ARIMA models are established with important application for model-checking and model construction. 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 also presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision and the new Marshallian Macroeconomic Model that features demand, supply and entry equations for major sectors of economies is analysed and described. This volume will prove invaluable to professionals, academics and students alike.
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David A. Belsley Cabanas
págs. 79-81
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págs. 91-95
Structural econometric modeling and time series analysis: an integrated approach
págs. 96-165
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págs. 169-171
págs. 172-174
Time series analysis, forecasting, and econometric modeling: the structural econometric modeling, time series analysis (SEMTSA) approach
págs. 175-200
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Time series versus structural models: a case study of Canadian manufacturing inventory behavior
Pravin K. Trivedi
págs. 288-314
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Macroeconomic forecasting using pooled international data
Antonio García Ferrer, Richard A. Highfield, Franz C. Palm, Arnold Zellner
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Pooling in dynamic panel data models: an application to forecasting GDP growth rates
págs. 590-611
Forecasting turning points in countries' output growth rates: a response to Milton Friedman
págs. 612-616
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