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Using monthly data to predict quarterly output

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  • Robert Ingenito
  • Bharat Trehan

Abstract

Some time ago, the Commerce Department changed the way it calculates real gross domestic product. In response to that change, this paper presents an update of a simple model that is used to predict the growth rate of current quarter real output based on available monthly data. After searching over a set containing more than 30 different variables, we find that a model that utilized monthly data on consumption and nonfarm payroll employment to predict contemporaneous real GDP does best.

Suggested Citation

  • Robert Ingenito & Bharat Trehan, 1996. "Using monthly data to predict quarterly output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
  • Handle: RePEc:fip:fedfer:y:1996:p:3-11:n:3
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    File URL: http://www.frbsf.org/econrsrch/econrev/96-3/ingenito.pdf
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    References listed on IDEAS

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    1. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 8(Fall).
    2. Hendry, David F & Mizon, Grayham E, 1978. "Serial Correlation as a Convenient Simplification, not a Nuisance: A Comment on a Study of the Demand for Money by the Bank of England," Economic Journal, Royal Economic Society, vol. 88(351), pages 549-563, September.
    3. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    4. Robert B. Litterman, 1984. "Above-average national growth in 1985 and 1986," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 8(Fall).
    5. Stock, James H & Watson, Mark W, 1996. "Evidence on Structural Instability in Macroeconomic Time Series Relations," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 11-30, January.
    6. Brian Motley, 1992. "Index numbers and the measurement of real GDP," Economic Review, Federal Reserve Bank of San Francisco, pages 3-13.
    7. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    8. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
    9. Godfrey, Leslie G, 1978. "Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables," Econometrica, Econometric Society, vol. 46(6), pages 1293-1301, November.
    10. Bharat Trehan, 1992. "Predicting contemporaneous output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
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