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Using Large Data Sets to Forecast Sectoral Employment

  • 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)

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.

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File URL: http://web.unlv.edu/projects/RePEc/pdf/1106.pdf
File Function: First version, 2011
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Paper provided by University of Nevada, Las Vegas , Department of Economics in its series Working Papers with number 1106.

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Length: 42 pages
Date of creation: Mar 2011
Date of revision:
Handle: RePEc:nlv:wpaper:1106
Contact details of provider: Phone: (702) 895-3776
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Web page: http://business.unlv.edu/econ/

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  1. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
  2. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
  3. Rangan Gupta & Alan Kabundi & Stephen M. Miller, 2010. "Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals," Working Papers 1001, University of Nevada, Las Vegas , Department of Economics.
  4. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
  5. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
  6. Carriero, A. & Kapetanios, G. & Marcellino, M., 2009. "Forecasting exchange rates with a large Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 25(2), pages 400-417.
  7. Jushan Bai & Serena Ng, 2001. "A PANIC Attack on Unit Roots and Cointegration," Boston College Working Papers in Economics 519, Boston College Department of Economics.
  8. Taylor, Carol A., 1982. "Econometric modeling of urban and other substate areas : An analysis of alternative methodologies," Regional Science and Urban Economics, Elsevier, vol. 12(3), pages 425-448, August.
  9. David Rapach & Jack Strauss, 2010. "Bagging or Combining (or Both)? An Analysis Based on Forecasting U.S. Employment Growth," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 511-533.
  10. Ben Bernanke & Jean Boivin & Piotr S. Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, MIT Press, vol. 120(1), pages 387-422, January.
  11. ZELLNER, Arnold & PALM, Franz, . "Time series analysis and simultaneous equation econometric models," CORE Discussion Papers RP -173, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  12. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
  13. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
  14. Christoffel, Kai & Warne, Anders & Coenen, Günter, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
  15. Anindya Banerjee & Massimiliano Marcellino, 2008. "Factor-augmented Error Correction Models," Working Papers 335, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  16. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2010. "Forecasting with Factor-augmented Error Correction," Discussion Papers 09-06r, Department of Economics, University of Birmingham.
  17. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
  18. Rangan Gupta & Stephen M. Miller, 2009. "“Ripple Effects” and Forecasting Home Prices In Los Angeles, Las Vegas, and Phoenix," Working Papers 200901, University of Pretoria, Department of Economics.
  19. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2005. "The generalised dynamic factor model: one sided estimation and forecasting," ULB Institutional Repository 2013/10129, ULB -- Universite Libre de Bruxelles.
  20. Bańbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Large Bayesian VARs," Working Paper Series 0966, European Central Bank.
  21. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
  22. David E. Rapach & Jack K. Strauss, 2008. "Forecasting US employment growth using forecast combining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 75-93.
  23. Sonali Das & Rangan Gupta & Alain Kabundi, 2009. "Could we have predicted the recent downturn in the South African Housing Market?," Working Papers 149, Economic Research Southern Africa.
  24. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
  25. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc.
  26. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
  27. Geetesh Bhardwaj & Norman Swanson, 2004. "An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series," Departmental Working Papers 200422, Rutgers University, Department of Economics.
  28. Glennon, Dennis & Lane, Julia & Johnson, Stanley, 1987. "Regional econometric models that reflect labor market relations," International Journal of Forecasting, Elsevier, vol. 3(2), pages 299-312.
  29. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," Economics Working Papers ECO2009/31, European University Institute.
  30. Rapach, David E. & Strauss, Jack K., 2012. "Forecasting US state-level employment growth: An amalgamation approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 315-327.
  31. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  32. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  33. LeSage, James P, 1990. "A Comparison of the Forecasting Ability of ECM and VAR Models," The Review of Economics and Statistics, MIT Press, vol. 72(4), pages 664-71, November.
  34. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January.
  35. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  36. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  37. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-80, November.
  38. David E. Rapach & Jack K. Strauss, 2005. "Forecasting employment growth in Missouri with many potentially relevant predictors: an analysis of forecast combining methods," Regional Economic Development, Federal Reserve Bank of St. Louis, issue Nov, pages 97-112.
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