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Estimation of short‐run predictive factor for US growth using state employment data

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  • Arabinda Basistha

Abstract

We estimate a predictive single factor model targeted to unobserved common growth in gross domestic product and gross domestic income (GDI) using a state‐space framework with select state employment data. We use likelihood‐based comparison to select the states to estimate the dynamic factor. The results show improved in‐sample and out‐of‐sample performance than threshold principal component factors and financial spreads. Out‐of‐sample evaluations indicate larger gains for GDI growth with 14% to 20% lower mean squared forecast errors than other alternatives. Sectoral employment factors based on selected sectors using the state‐space framework also show forecasting gains. An expanded model using both sectoral and state employment data shows that their common component is the primary predictive factor.

Suggested Citation

  • Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
  • Handle: RePEc:wly:jforec:v:42:y:2023:i:1:p:34-50
    DOI: 10.1002/for.2896
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    1. Aruoba, S. Borağan & Diebold, Francis X. & Nalewaik, Jeremy & Schorfheide, Frank & Song, Dongho, 2016. "Improving GDP measurement: A measurement-error perspective," Journal of Econometrics, Elsevier, vol. 191(2), pages 384-397.
    2. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    3. Jeremy J. Nalewaik, 2010. "The Income- and Expenditure-Side Estimates of U.S. Output Growth," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 41(1 (Spring), pages 71-127.
    4. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2015. "Forecasting National Recessions Using State‐Level Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(5), pages 847-866, August.
    5. David López-Salido & Jeremy C. Stein & Egon Zakrajšek, 2017. "Credit-Market Sentiment and the Business Cycle," The Quarterly Journal of Economics, Oxford University Press, vol. 132(3), pages 1373-1426.
    6. Gonçalves, Sílvia & McCracken, Michael W. & Perron, Benoit, 2017. "Tests of equal accuracy for nested models with estimated factors," Journal of Econometrics, Elsevier, vol. 198(2), pages 231-252.
    7. James H. Stock & Mark W. Watson, 2016. "Core Inflation and Trend Inflation," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 770-784, October.
    8. Jeremy J. Nalewaik, 2012. "Estimating Probabilities of Recession in Real Time Using GDP and GDI," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 235-253, February.
    9. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    10. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    13. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
    14. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    15. Neville Francis & Laura E. Jackson & Michael T. Owyang, 2018. "Countercyclical Policy and the Speed of Recovery after Recessions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(4), pages 675-704, June.
    16. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2005. "Business Cycle Phases in U.S. States," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 604-616, November.
    17. James D. Hamilton & Michael T. Owyang, 2012. "The Propagation of Regional Recessions," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 935-947, November.
    18. Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
    19. Clark, Todd E, 1998. "Employment Fluctuations in U.S. Regions and Industries: The Roles of National, Region-Specific, and Industry-Specific Shocks," Journal of Labor Economics, University of Chicago Press, vol. 16(1), pages 202-229, January.
    20. Braun, Steven N, 1990. "Estimation of Current-Quarter Gross National Product by Pooling Preliminary Labor-Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(3), pages 293-304, July.
    21. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.
    22. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    23. Arabinda Basistha & Richard Startz, 2008. "Measuring the NAIRU with Reduced Uncertainty: A Multiple-Indicator Common-Cycle Approach," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 805-811, November.
    24. Hamilton, James D., 1986. "A standard error for the estimated state vector of a state-space model," Journal of Econometrics, Elsevier, vol. 33(3), pages 387-397, December.
    25. Jeremy J. Nalewaik, 2012. "Estimating Probabilities of Recession in Real Time Using GDP and GDI," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 235-253, February.
    26. Hernandez-Murillo, Ruben & Owyang, Michael T., 2006. "The information content of regional employment data for forecasting aggregate conditions," Economics Letters, Elsevier, vol. 90(3), pages 335-339, March.
    27. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    28. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    29. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
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