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Using Large Data Sets to Forecast Sectoral Employment

Author

Listed:
  • Rangan Gupta

    (Department of Economics, University of Pretoria)

  • Alain Kabundi

    (Department of Economics and Econometrics, University of Johannesburg)

  • Stephen M. Miller

    (Department of Economics, University of Nevada, Las Vegas)

  • Josine Uwilingiye

    (Department of Economics and Econometrics, University of Johannesburg)

Abstract

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.

Suggested Citation

  • Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working Papers 1106, University of Nevada, Las Vegas , Department of Economics.
  • Handle: RePEc:nlv:wpaper:1106
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    More about this item

    Keywords

    Sectoral Employment; Forecasting; Factor Augmented Models; Large-Scale BVAR models;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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