Machine Learning and Forecast Combination in Incomplete Panels

55 Pages Posted: 5 Dec 2013

See all articles by Kajal Lahiri

Kajal Lahiri

State University of New York (SUNY) at Albany, College of Arts and Sciences, Economics

Huaming Peng

State University of New York (SUNY) at Albany - Department of Economics

Yongchen Zhao

Towson University - Department of Economics

Date Written: December 2, 2013

Abstract

This paper focuses on a number of newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012). We first establish certain asymptotic properties of these algorithms and compare them with the Bates and Granger (1969) method. We then show that when implemented on unbalanced panels, different combination algorithms implicitly impute missing data differently, so that the performance of the resulting combined forecasts are not comparable. Using forecasts of several important macroeconomic variables from the U.S. Survey of Professional Forecasters, we evaluate the performance of the combination methods, after explicitly accounting for the missing data. We find that even though equally weighted average is hard to beat, the new algorithms deliver superior performance especially during periods of volatility clustering and structural breaks.

Keywords: On-line learning, Recursive algorithms, Unbalanced panel, SPF forecasts

JEL Classification: C22; C53; C14

Suggested Citation

Lahiri, Kajal and Peng, Huaming and Zhao, Yongchen, Machine Learning and Forecast Combination in Incomplete Panels (December 2, 2013). Available at SSRN: https://ssrn.com/abstract=2359523 or http://dx.doi.org/10.2139/ssrn.2359523

Kajal Lahiri

State University of New York (SUNY) at Albany, College of Arts and Sciences, Economics ( email )

Department of Economics
1400 Washington Avenue
Albany, NY 12222
United States
518-442 4758 (Phone)
518-442 4736 (Fax)

HOME PAGE: http://www.albany.edu/~klahiri

Huaming Peng

State University of New York (SUNY) at Albany - Department of Economics ( email )

1400 Washington Avenue
Building, Room 109
Albany, NY 12222
United States

Yongchen Zhao (Contact Author)

Towson University - Department of Economics ( email )

Towson, MD 21204
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
89
Abstract Views
889
rank
300,181
PlumX Metrics