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Does current-quarter information improve quarterly forecasts for the U.S. economy?

  • Tom Stark
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    This paper presents new evidence on the benefits of conditioning quarterly model forecasts on monthly current-quarter data. On the basis of a quarterly Bayesian vector error corrections model, the findings indicate that such conditioning produces economically relevant and statistically significant improvement. The improvement, which begins as early as the end of the first week of the second month of the quarter, is largest in the current quarter, but in some cases, extends beyond the current quarter. Forecast improvement is particularly large during periods of recessions but generally extends to other periods as well. Overall, the findings suggest that it is rational to update one's quarterly forecast in response to incoming monthly data.

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    File URL: http://www.philadelphiafed.org/research-and-data/publications/working-papers/2000/wp00-2.pdf
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    Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 00-2.

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    Date of creation: 2000
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    Handle: RePEc:fip:fedpwp:00-2
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    1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
    2. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    3. Christoffersen, Peter F & Diebold, Francis X, 1998. "Cointegration and Long-Horizon Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 450-58, October.
    4. Rudebusch, Glenn D, 1998. "Do Measures of Monetary Policy in a VAR Make Sense?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 907-31, November.
    5. Terry J. Fitzgerald & Preston J. Miller, 1989. "A simple way to estimate current-quarter GNP," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 27-31.
    6. Bharat Trehan, 1989. "Forecasting growth in current quarter real GNP," Economic Review, Federal Reserve Bank of San Francisco, issue Win, pages 39-52.
    7. Duy, Timothy A. & Thoma, Mark A., 1998. "Modeling and Forecasting Cointegrated Variables: Some Practical Experience," Journal of Economics and Business, Elsevier, vol. 50(3), pages 291-307, May.
    8. Evan F. Koenig & Sheila Dolmas, 1997. "Real-time GDP Growth Forecasts," Working Papers 9710, Federal Reserve Bank of Dallas.
    9. Carol Corrado & Jane Haltmaier, 1988. "The use of high-frequency data in model-based forecasting at the Federal Reserve Board," Finance and Economics Discussion Series 24, Board of Governors of the Federal Reserve System (U.S.).
    10. Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
    11. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, vol. 55(2), pages 251-76, March.
    12. Franses, Philip Hans & Kleibergen, Frank, 1996. "Unit roots in the Nelson-Plosser data: Do they matter for forecasting?," International Journal of Forecasting, Elsevier, vol. 12(2), pages 283-288, June.
    13. Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
    14. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    15. Preston J. Miller & Daniel M. Chin, 1996. "Using monthly data to improve quarterly model forecasts," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr, pages 16-33.
    16. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    17. Carol A. Corrado & Mark Greene, 1984. "Reducing uncertainty in short-term projections: linkage of monthly and quarterly models," Special Studies Papers 207, Board of Governors of the Federal Reserve System (U.S.).
    18. Dean Croushore & Tom Stark, 1999. "Does data vintage matter for forecasting?," Working Papers 99-15, Federal Reserve Bank of Philadelphia.
    19. Francis X. Diebold & Peter F. Christoffersen, 1997. "Cointegration and Long-Horizon Forecasting," IMF Working Papers 97/61, International Monetary Fund.
    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. Tom Stark, 1998. "A Bayesian vector error corrections model of the U.S. economy," Working Papers 98-12, Federal Reserve Bank of Philadelphia.
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