Abstract

http://ssrn.com/abstract=1868703
 
 

References (59)



 
 

Citations (3)



 


 



The Three-Pass Regression Filter: A New Approach to Forecasting Using Many Predictors


Bryan T. Kelly


University of Chicago - Booth School of Business; National Bureau of Economic Research (NBER)

Seth Pruitt


Federal Reserve Board

June 1, 2012

Fama-Miller Working Paper
Chicago Booth Research Paper No. 11-19

Abstract:     
We forecast a single time series using many predictor variables with a new estimator called the three-pass regression filter (3PRF). It is calculated in closed form and conveniently represented as a set of ordinary least squares regressions. 3PRF forecasts converge to the infeasible best forecast when both the time dimension and cross section dimension become large. This requires only specifying the number of relevant factors driving the forecast target, regardless of the total number of common (and potentially irrelevant) factors driving the cross section of predictors. We derive inferential theory in the form of limiting distributions for estimated relevant factors, predictive coefficients and forecasts, and provide consistent standard error estimators. We explore two empirical applications that exemplify the many predictor problem: Forecasting macroeconomic aggregates with a large panel of economic indices, and forecasting stock market aggregates with many individual assets' price-dividend ratios. These, combined with a range of Monte Carlo experiments, demonstrate the 3PRF's forecasting power.

Number of Pages in PDF File: 56

Keywords: forecast, many predictors, factor model, Kalman filter, constrained least squares, principal components, partial least squares

working papers series


Download This Paper

Date posted: June 22, 2011 ; Last revised: June 13, 2012

Suggested Citation

Kelly, Bryan T. and Pruitt, Seth, The Three-Pass Regression Filter: A New Approach to Forecasting Using Many Predictors (June 1, 2012). Fama-Miller Working Paper; Chicago Booth Research Paper No. 11-19. Available at SSRN: http://ssrn.com/abstract=1868703 or http://dx.doi.org/10.2139/ssrn.1868703

Contact Information

Bryan T. Kelly (Contact Author)
University of Chicago - Booth School of Business ( email )
5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-8359 (Phone)
National Bureau of Economic Research (NBER) ( email )
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Seth Pruitt
Federal Reserve Board ( email )
20th Street and Constitution Avenue NW
Washington, DC 20551
United States
Feedback to SSRN


Paper statistics
Abstract Views: 3,557
Downloads: 1,085
Download Rank: 9,374
References:  59
Citations:  3

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo4 in 0.500 seconds