Discussion paper

DP14469 A Similarity-based Approach for Macroeconomic Forecasting

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.

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Citation

Dendramis, Y, G Kapetanios and M Marcellino (2020), ‘DP14469 A Similarity-based Approach for Macroeconomic Forecasting‘, CEPR Discussion Paper No. 14469. CEPR Press, Paris & London. https://cepr.org/publications/dp14469