Forecasting Australian Macroeconomic Variables Using A Large Dataset
AbstractThis paper investigates the forecasting performance of the diffusion index approach for the Australian economy, and considers the forecasting performance of the diffusion index approach relative to composite forecasts. Weighted and unweighted factor forecasts are benchmarked against composite forecasts, and forecasts derived from individual forecasting models. The results suggest that diffusion index forecasts tend to improve on the benchmark AR forecasts. We also observe that weighted factors tend to produce better forecasts than their unweighted counterparts. We find, however, that the size of the forecasting improvement is less marked than previous research, with the diffusion index forecasts typically producing mean square errors of a similar magnitude to the VAR and BVAR approaches. Copyright 2010 The Authors. Journal compilation 2010 Blackwell Publishing Ltd/University of Adelaide and Flinders University.
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Bibliographic InfoArticle provided by Wiley Blackwell in its journal Australian Economic Papers.
Volume (Year): 49 (2010)
Issue (Month): 1 (03)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0004-900X
Other versions of this item:
- Sarantis Tsiaplias & Chew Lian Chua, 2008. "Forecasting Australian Macroeconomic Variables Using a Large Dataset," Melbourne Institute Working Paper Series wp2008n04, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
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- de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
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