A Similarity-based Approach for Macroeconomic Forecasting

Massimiliano Marcellino, George Kapetanios and Yiannis Dendramis ()

No 14469, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: In the aftermath of the recent financial crisis there has been considerable focus on methods for predicting macroeconomic variables when their behavior is subject to abrupt changes, associated for example with crisis periods. In this paper we propose similarity based approaches as a way to handle parameter instability, and apply them to macroeconomic forecasting. The rationale is that clusters of past data that match the current economic conditions can be more informative for forecasting than the entire past behavior of the variable of interest. We apply our methods to predict both simulated data in a set of Monte Carlo experiments, and a broad set of key US macroeconomic indicators. The forecast evaluation exercises indicate that similarity-based approaches perform well, in general, in comparison with other common time-varying forecasting methods, and particularly well during crisis episodes.

Keywords: Macroeconomic forecasting; Forecast comparison; Empirical similarity; Parameter time variation; Kernel estimation (search for similar items in EconPapers)
Date: 2020-03
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
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Citations: View citations in EconPapers (10)

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Journal Article: A similarity‐based approach for macroeconomic forecasting (2020)
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