Measuring Predictability: Theory And Macroeconomic Applications

Francis Diebold and Lutz Kilian

No 2424, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We propose a measure of predictability based on the ratio of the expected loss of a short-run forecast to the expected loss of a long-run forecast. This predictability measure can be tailored to the forecast horizons of interest, and it allows for general loss functions, univariate or multivariate information sets, and covariance stationary or difference stationary processes. We propose a simple estimator, and we suggest resampling methods for inference. We then provide several macroeconomic applications. First, we illustrate the implementation of predictability measures based on fitted parametric models for several US macroeconomic time series. Second, we analyse the internal propagation mechanism of a standard dynamic macroeconomic model by comparing the predictability of model inputs and model outputs. Third, we use predictability as a metric for assessing the similarity of data simulated from the model and actual data. Finally, we outline several nonparametric extensions of our approach.

Keywords: Forecasting; Model evaluation; Propagation mechanism (search for similar items in EconPapers)
JEL-codes: C22 C52 E32 (search for similar items in EconPapers)
Date: 2000-04
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Related works:
Journal Article: Measuring predictability: theory and macroeconomic applications (2001)
Working Paper: Measuring Predictability: Theory and Macroeconomic Applications (1998)
Working Paper: Measuring predictability: theory and macroeconomic applications (1997)
Working Paper: Measuring Predictability: Theory and Macroeconomic Applications (1997)
Working Paper: Measuring Predictability: Theory and Macroeconomic Applications
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