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Bayes Models and Forecasts of Australian Macroeconomic Time Series

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Abstract

This paper provides an empirical implementation of some recent work by the author and Werner Ploberger on the development of "Bayes models" for time series. The methods offer a new data-based approach to model selection, to hypothesis testing and to forecast evaluation in the analysis of time series. A particular advantage of the approach is that modelling issues such as lag order, parameter constancy, and the presence of deterministic and stochastic trends all come within the compass of the same statistical methodology, as do the evaluation of forecasts from competing models. The paper shows how to build parsimonious empirical "Bayes models" using the new approach and applies the methodology to some Australian macroeconomic data. "Bayes models" are constructed for 13 quarterly Australian macroeconomic time series over the period 1959(3)-1987(4). These models are compared with certain fixed format models (like an AR(4) + linear trend) in terms of their forecasting performance over the period 1988(1)-1991(4). The "Bayes models" are found to be superior in these forecasting exercises for 10 of the 13 series, while at the same time being more parsimonious in form.

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File URL: http://cowles.econ.yale.edu/P/cd/d10a/d1024.pdf
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Bibliographic Info

Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1024.

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Length: 33 pages
Date of creation: Aug 1992
Date of revision:
Publication status: Published in Colin P. Hargreaves, ed., Nonstationary Time Series Analysis and Cointegration, 1994, pp. 53-86
Handle: RePEc:cwl:cwldpp:1024

Note: CFP 897.
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

Related research

Keywords: Bayes model; Bayes measure; deterministic trend; forecast-encompass; model selection; one-period ahead forecasts; PIC criterion; unit root;

References

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  1. Peter C.B. Phillips, 1991. "Bayesian Routes and Unit Roots: de rebus prioribus semper est disputandum," Cowles Foundation Discussion Papers 986, Cowles Foundation for Research in Economics, Yale University.
  2. Chong, Yock Y & Hendry, David F, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Wiley Blackwell, vol. 53(4), pages 671-90, August.
  3. Phillips, Peter C.B. & Ploberger, Werner, 1994. "Posterior Odds Testing for a Unit Root with Data-Based Model Selection," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 774-808, August.
  4. Peter C.B. Phillips & Werner Ploberger, 1991. "Time Series Modelling with a Bayesian Frame of Reference: 1. Concepts and Illustrations," Cowles Foundation Discussion Papers 980, Cowles Foundation for Research in Economics, Yale University.
  5. Peter C.B. Phillips, 1992. "Bayesian Model Selection and Prediction with Empirical Applications," Cowles Foundation Discussion Papers 1023, Cowles Foundation for Research in Economics, Yale University.
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Cited by:
  1. Peter C.B. Phillips, 1995. "Impulse Response and Forecast Error Variance Asymptotics in Nonstationary VAR's," Cowles Foundation Discussion Papers 1102, Cowles Foundation for Research in Economics, Yale University.
  2. Peter C.B. Phillips, 1995. "Automated Forecasts of Asia-Pacific Economic Activity," Cowles Foundation Discussion Papers 1103, Cowles Foundation for Research in Economics, Yale University.
  3. Aaron F. Schiff & Peter C.B. Phillips, 2000. "Forecasting New Zealand's Real GDP," Cowles Foundation Discussion Papers 1278, Cowles Foundation for Research in Economics, Yale University.
  4. Peter C.B. Phillips, 1992. "Bayes Methods for Trending Multiple Time Series with an Empirical Application to the US Economy," Cowles Foundation Discussion Papers 1025, Cowles Foundation for Research in Economics, Yale University.

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