Forecasting Macroeconomic Time Series: LASSO-Based Approaches and Their Forecast Combinations with Dynamic Factor Models

43 Pages Posted: 19 Mar 2014

See all articles by Jiahan Li

Jiahan Li

University of Notre Dame

Weiye Chen

University of Notre Dame

Date Written: March 7, 2014

Abstract

In a data-rich environment, forecasting economic variables amounts to extracting and organizing useful information out of a large number of predictors. So far dynamic factor model and its variants have been the most successful models for such exercises. In this paper, we investigate a category of LASSO-based approaches and evaluate their predictive abilities in forecasting twenty important macroeconomic variables. These alternative models could handle hundreds of data series simultaneously, and extract useful information for forecasting. We also show analytically and empirically that combing forecasts from LASSO-based models and those from dynamic factor models could further reduce the mean square forecast error (MSFE). Our three main findings can be summarized as follows. First, for most of the variables under investigation, all LASSO-based models outperform dynamic factor models in the out-of-sample forecast evaluations. Second, by extracting information and formulating predictors at the economically meaningful block levels, new methods greatly enhance model interpretabilities. Third, once forecasts from a LASSO-based approach and those from a dynamic factor model are combined by forecasts combination techniques, the combined forecasts are significantly better than dynamic factor model forecasts and the naïve random walk benchmark.

Keywords: High-dimensional time series; Model selection; Dynamic factor model; Combining forecasts

JEL Classification: C22, C53, E17

Suggested Citation

Li, Jiahan and Chen, Weiye, Forecasting Macroeconomic Time Series: LASSO-Based Approaches and Their Forecast Combinations with Dynamic Factor Models (March 7, 2014). Available at SSRN: https://ssrn.com/abstract=2410214 or http://dx.doi.org/10.2139/ssrn.2410214

Jiahan Li (Contact Author)

University of Notre Dame ( email )

156 Hurley Hall
Notre Dame, IN 46556-5646
United States

Weiye Chen

University of Notre Dame ( email )

153 Hurley Hall
Notre Dame, IN 46556-5646
United States

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