Economics at your fingertips  

Using large data sets to forecast sectoral employment

Rangan Gupta (), Alain Kabundi (), Stephen Miller () and Josine Uwilingiye

Statistical Methods & Applications, 2014, vol. 23, issue 2, 229-264

Abstract: We use several models using classical and Bayesian methods to forecast employment for eight sectors of the US economy. In addition to using standard vector-autoregressive and Bayesian vector autoregressive models, we also augment these models to include the information content of 143 additional monthly series in some models. Several approaches exist for incorporating information from a large number of series. We consider two multivariate approaches—extracting common factors (principal components) and Bayesian shrinkage. After extracting the common factors, we use Bayesian factor-augmented vector autoregressive and vector error-correction models, as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive models. For an in-sample period of January 1972 to December 1989 and an out-of-sample period of January 1990 to March 2010, we compare the forecast performance of the alternative models. More specifically, we perform ex-post and ex-ante out-of-sample forecasts from January 1990 through March 2009 and from April 2009 through March 2010, respectively. We find that factor augmented models, especially error-correction versions, generally prove the best in out-of-sample forecast performance, implying that in addition to macroeconomic variables, incorporating long-run relationships along with short-run dynamics play an important role in forecasting employment. Forecast combination models, however, based on the simple average forecasts of the various models used, outperform the best performing individual models for six of the eight sectoral employment series. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Sectoral employment; Forecasting; Factor augmented models; Large-scale BVAR models; C32; R31 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link) (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Using Large Data Sets to Forecast Sectoral Employment (2012) Downloads
Working Paper: Using Large Data Sets to Forecast Sectoral Employment (2011) Downloads
Working Paper: Using Large Data Sets to Forecast Sectoral Employment (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla ().

Page updated 2019-11-12
Handle: RePEc:spr:stmapp:v:23:y:2014:i:2:p:229-264