A1661
Title: Nonlinear forecasting with many predictors using mixed data sampling kernel ridge regression models
Authors: Kristofer Mansson - JU JIBS (Sweden)
Farrukh Javed - Lund University (Sweden) [presenting]
Deliang Dai - Linnaeus university (Sweden)
Peter Karlsson - Linneaus University (Sweden)
Abstract: Policy institutes such as central banks need accurate forecasts of key measures of economic activity to design stabilization policies that reduce the severity of economic fluctuations. Therefore, the recommendation is kernel ridge regression in a mixed data sampling framework. Kernel ridge regression can handle many predictors with a nonlinear relationship to the target variable. Consequently, it can potentially improve the currently used principal component-based methods when the economic data follow a nonlinear factor structure. In a Monte Carlo study, it is shown that the kernel ridge regression approach is superior in terms of mean square error and is more robust than principal component-based methods for different nonlinear data-generating processes. By using a dataset consisting of 24 economic indicators, Swedish gross domestic production is forecasted. The results confirm the superiority of the kernel ridge regression approach, especially during the economic crisis caused by the COVID-19 pandemic. Therefore, it is suggested that policy institutes consider the use of kernel-based approaches when forecasting key measures of economic activity.
