Using Large Data Sets to Forecast Sectoral Employment
Rangan Gupta (),
Alain Kabundi (),
Stephen Miller () and
No 201101, Working Papers from University of Pretoria, Department of Economics
We implement several Bayesian and classical models to forecast employment for eight sectors of the US economy. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also 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 approaches – extracting common factors (principle components) in a factor-augmented vector autoregressive or vector error-correction, Bayesian factor-augmented vector autoregressive or vector error-correction models, or Bayesian shrinkage in a large-scale Bayesian vector autoregressive models. Using the period of January 1972 to December 1999 as the in-sample period and January 2000 to March 2009 as the out-of-sample horizon, we compare the forecast performance of the alternative models. Finally, we forecast out-of sample from April 2009 through March 2010, using the best forecasting model for each employment series. 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.
Keywords: Sectoral Employment; Forecasting; Factor Augmented Models; Large-Scale BVAR models (search for similar items in EconPapers)
JEL-codes: C32 R31 (search for similar items in EconPapers)
Pages: 58 pages
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Journal Article: Using large data sets to forecast sectoral employment (2014)
Working Paper: Using Large Data Sets to Forecast Sectoral Employment (2012)
Working Paper: Using Large Data Sets to Forecast Sectoral Employment (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:201101
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