DP14469 A Similarity-based Approach for Macroeconomic Forecasting
|Author(s):||Yiannis Dendramis, george kapetanios, Massimiliano Marcellino|
|Publication Date:||March 2020|
|Keyword(s):||empirical similarity, Forecast comparison, Kernel estimation , Macroeconomic forecasting, parameter time variation|
|Programme Areas:||Monetary Economics and Fluctuations|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=14469|
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.