Assessing Macroeconomic Forecast Uncertainty: An Application to the Risk of Deflation in Germany
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Assessing Macroeconomic Forecast Uncertainty: An Application to the Risk of Deflation in Germany
Borbély, Dora | Meier, Carsten-Patrick
Credit and Capital Markets – Kredit und Kapital, Vol. 38 (2005), Iss. 3 : pp. 377–399
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Dora Borbely, Kiel
Carsten-Patrick Meier, Kiel
References
-
Baxter, M. and King, R. (1995): Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series. National Bureau of Economic Research Working Paper No. 5022. Cambridge Mass.
Google Scholar -
Belsley, D. A., Kuh, E. and Welsch, R. E. (1980): Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, New York.
Google Scholar -
Berkowitz, J. and Kilian, L. (2000): Recent Developments in Bootstrapping Time Series. Econometric Reviews 19, 1-48.
Google Scholar -
Brownstone, D. and Valetta, R. (2001): The Boostrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests. Journal of Economic Perspectives 15 (4), 129-141.
Google Scholar -
Brüggemann, R. and Lütkepohl, H. (2001): Lag Selection in Subset VAR Models with an Application to a U.S. Monetary System. In: Econometric Studies: A Festschrift for Joachim Frohn, Münster, pp. 107-128.
Google Scholar -
Chen, C. and Lui, L.-M. (1993): Joint Estimation of Model Parameters and Outlier Effects in Time Series. Journal of the American Statistical Association 88, 284-297.
Google Scholar
Abstract
This paper proposes an approach for estimating the uncertainty associated with model-based macroeconomic forecasts. We argue that estimated forecast intervals should account for the uncertainty arising from selecting the specification of an empirical forecasting model from the sample data. To allow this uncertainty to be considered systematically, we formalize a model selection procedure that specifies the lag structure of a model and accounts for aberrant observations. The procedure can be used to bootstrap the complete model selection process when estimating forecast intervals. We apply the procedure to generating forecasts and forecast intervals for the change in the consumer price index in Germany, with special emphasis on assessing the risk of deflationary developments. (JEL C5, E0, E5)