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

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  • Tom Stark
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    Abstract

    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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    Bibliographic Info

    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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    Keywords: Economic conditions - United States ; Forecasting;

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    1. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 13(3), pages 253-63, July.
    2. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, Elsevier, vol. 105(1), pages 111-130, November.
    3. Terry J. Fitzgerald & Preston J. Miller, 1989. "A simple way to estimate current-quarter GNP," Quarterly Review, Federal Reserve Bank of Minneapolis, Federal Reserve Bank of Minneapolis, issue Fall, pages 27-31.
    4. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 4(1), pages 25-38, January.
    5. Rudebusch, G.D., 1996. "Do Measures of Monetary Policy in a VAR Make Sense?," Papers, Banca Italia - Servizio di Studi 269, Banca Italia - Servizio di Studi.
    6. 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, American Statistical Association, vol. 8(3), pages 293-304, July.
    7. Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, Federal Reserve Bank of San Francisco, pages 3-11.
    8. Bharat Trehan, 1989. "Forecasting growth in current quarter real GNP," Economic Review, Federal Reserve Bank of San Francisco, Federal Reserve Bank of San Francisco, issue Win, pages 39-52.
    9. Duy, Timothy A. & Thoma, Mark A., 1998. "Modeling and Forecasting Cointegrated Variables: Some Practical Experience," Journal of Economics and Business, Elsevier, Elsevier, vol. 50(3), pages 291-307, May.
    10. Tom Stark, 1998. "A Bayesian vector error corrections model of the U.S. economy," Working Papers, Federal Reserve Bank of Philadelphia 98-12, Federal Reserve Bank of Philadelphia.
    11. Dean Croushore & Tom Stark, 1999. "Does data vintage matter for forecasting?," Working Papers, Federal Reserve Bank of Philadelphia 99-15, Federal Reserve Bank of Philadelphia.
    12. Francis X. Diebold & Peter F. Christoffersen, 1997. "Cointegration and Long-Horizon Forecasting," IMF Working Papers, International Monetary Fund 97/61, International Monetary Fund.
    13. Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, Econometric Society, vol. 55(2), pages 251-76, March.
    14. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
    15. 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, Board of Governors of the Federal Reserve System (U.S.) 24, Board of Governors of the Federal Reserve System (U.S.).
    16. Carol A. Corrado & Mark Greene, 1984. "Reducing uncertainty in short-term projections: linkage of monthly and quarterly models," Special Studies Papers, Board of Governors of the Federal Reserve System (U.S.) 207, Board of Governors of the Federal Reserve System (U.S.).
    17. Preston J. Miller & Daniel M. Chin, 1996. "Using monthly data to improve quarterly model forecasts," Quarterly Review, Federal Reserve Bank of Minneapolis, Federal Reserve Bank of Minneapolis, issue Spr, pages 16-33.
    18. 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, MIT Press, vol. 85(3), pages 605-617, August.
    19. Evan F. Koenig & Sheila Dolmas, 1997. "Real-time GDP Growth Forecasts," Working Papers, Federal Reserve Bank of Dallas 9710, Federal Reserve Bank of Dallas.
    20. Franses, Philip Hans & Kleibergen, Frank, 1996. "Unit roots in the Nelson-Plosser data: Do they matter for forecasting?," International Journal of Forecasting, Elsevier, Elsevier, vol. 12(2), pages 283-288, June.
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    Cited by:
    1. Croushore, Dean, 2004. "Do Consumer Confidence Indexes Help Forecast Consumer Spending in Real Time?," Discussion Paper Series 1: Economic Studies, Deutsche Bundesbank, Research Centre 2004,27, Deutsche Bundesbank, Research Centre.
    2. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(3), pages 368-385.

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