Statistics, Econometrics and Forecasting

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Statistics, Econometrics and Forecasting
Arnold Zellner, Arnold Zellner
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Last edited by raybb
October 10, 2021 | History

Statistics, Econometrics and Forecasting

  • 1 Want to read

Based on two lectures presented as part of The Stone Lectures in Economics series, Arnold Zellner describes the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling. Developed by Zellner and Franz Palm, the SEMTSA approach produces an understanding of the relationship of univariate and multivariate time series forecasting models and dynamic, time series structural econometric models. As scientists and decision-makers in industry and government world-wide adopt the Bayesian approach to scientific inference, decision-making and forecasting, Zellner offers an in-depth analysis and appreciation of this important paradigm shift. Finally Zellner discusses the alternative approaches to model building and looks at how the use and development of the SEMTSA approach has led to the production of a Marshallian Macroeconomic Model that will prove valuable to many. Written by one of the foremost practitioners of econometrics, this book will have wide academic and professional appeal.

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English

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Cover of: Statistics, Econometrics and Forecasting
Statistics, Econometrics and Forecasting
2009, Cambridge University Press
in English
Cover of: STATISTICS, ECONOMETRICS AND FORECASTING.
STATISTICS, ECONOMETRICS AND FORECASTING.
2004, CAMBRIDGE UNIV PRESS
in Undetermined
Cover of: Statistics, Econometrics and Forecasting
Statistics, Econometrics and Forecasting
2004, Cambridge University Press
in English
Cover of: Statistics, Econometrics and Forecasting
Statistics, Econometrics and Forecasting
2004, Cambridge University Press
in English
Cover of: Statistics, Econometrics, and Forecasting
Statistics, Econometrics, and Forecasting
2004, Cambridge University Press
in English
Cover of: Statistics, Econometrics and Forecasting
Statistics, Econometrics and Forecasting
2000, Cambridge University Press
eBook in English

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Edition Identifiers

Open Library
OL40507780M
ISBN 13
9780511187728

Work Identifiers

Work ID
OL25659084W

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