(), Farid Jimmy Boumediene
() and Pär Österholm
Jan-Erik Antipin: Finnish Tax Administration
Farid Jimmy Boumediene: Confederation of Swedish Enterprise
Pär Österholm: National Institute of Economic Research, Postal: National Institute of Economic Research, P.O. Box 3116, SE-103 62 Stockholm, Sweden
Abstract: In this paper, we assess the usefulness of constant gain least squares (CGLS) when forecasting the unemployment rate. Using quarterly data from 1970 to 2009, we conduct an out-of-sample forecast exercise in which univariate autoregressive models for the unemployment rate in Australia, Sweden, the United Kingdom and the United States are em-ployed. Results show that CGLS very rarely outperforms OLS. At horizons of six to eight quarters, OLS is always associated with higher forecast precision, regardless of model size or gain employed for Australia, Sweden and the United States. Our findings suggest that while CGLS has been shown valuable when forecasting certain mac-roeconomic time series, it has shortcomings when forecasting the unemployment rate. One problematic feature is found to be an increased tendency for the autoregressive model to have explosive dynamics when estimated with CGLS.
28 pages, September 10, 2013
Full text files
Questions (including download problems) about the papers in this series should be directed to Sarah Hegardt Grant ()
Report other problems with accessing this service to Sune Karlsson ().
This page generated on 2018-01-23 23:36:16.