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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.

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Bibliographic Info

Article provided by Federal Reserve Bank of San Francisco in its journal Economic Review.

Volume (Year): (1996)
Issue (Month): ()
Pages: 3-11

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Handle: RePEc:fip:fedfer:y:1996:p:3-11:n:3

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Keywords: Forecasting ; Gross domestic product ; Employment (Economic theory) ; Industrial productivity ; Economic indicators ; Econometric models;

References

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  1. 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.
  2. 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.
  3. Richard M. Todd, 1984. "Improving economic forecasting with Bayesian vector autoregression," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  4. James H. Stock & Mark W. Watson, 1994. "Evidence on Structural Instability in Macroeconomic Time Series Relations," NBER Technical Working Papers 0164, National Bureau of Economic Research, Inc.
  5. Bharat Trehan, 1992. "Predicting contemporaneous output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
  6. Robert B. Litterman, 1984. "Above-average national growth in 1985 and 1986," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall.
  7. 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 549-63, September.
  8. 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.
  9. Brian Motley, 1992. "Index numbers and the measurement of real GDP," Economic Review, Federal Reserve Bank of San Francisco, pages 3-13.
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Citations

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Cited by:
  1. Evan F. Koenig & Sheila Dolmas & Jeremy Piger, 2000. "The use and abuse of "real-time" data in economic forecasting," International Finance Discussion Papers 684, Board of Governors of the Federal Reserve System (U.S.).
  2. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, issue Q1, pages 4-18.
  3. Rünstler, Gerhard & Sédillot, Franck, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 0276, European Central Bank.
  4. Kitchen, John & Monaco, Ralph, 2003. "Real-Time Forecasting in Practice: The U.S. Treasury Staff's Real-Time GDP Forecast System," MPRA Paper 21068, University Library of Munich, Germany, revised Oct 2003.
  5. Dennis L. Hoffman & Robert H. Rasche, 1997. "STLS/US-VECM6.1: a vector error-correction forecasting model of the U. S. economy," Working Papers 1997-008, Federal Reserve Bank of St. Louis.
  6. Konstantins Benkovskis, 2008. "Short-Term Forecasts of Latvia's Real Gross Domestic Product Growth Using Monthly Indicators," Working Papers 2008/05, Latvijas Banka.
  7. Tom Stark, 2000. "Does current-quarter information improve quarterly forecasts for the U.S. economy?," Working Papers 00-2, Federal Reserve Bank of Philadelphia.
  8. Bennett T. McCallum & Edward Nelson, 1998. "Performance of Operational Policy Rules in an Estimated Semi-Classical Structural Model," NBER Working Papers 6599, National Bureau of Economic Research, Inc.
  9. Fabio Ghironi, 2000. "Alternative Monetary Rules for a Small Open Economy: The Case of Canada," Boston College Working Papers in Economics 466, Boston College Department of Economics, revised 30 Oct 2000.
  10. Elisa Keller, 2007. "Classical and Bayesian Methods for the VAR Analysis: International Comparisons," Rivista di Politica Economica, SIPI Spa, vol. 97(6), pages 149-202, November-.
  11. Klaus Wohlrabe, 2009. "Makroökonomische Prognosen mit gemischten Frequenzen," Ifo Schnelldienst, Ifo Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
  12. Antipa, P. & Barhoumi, K. & Brunhes-Lesage, V. & Darné, O., 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Working papers 401, Banque de France.
  13. Barhoumi, K. & Brunhes-Lesage, V. & Darné, O. & Ferrara, L. & Pluyaud, B. & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Working papers 222, Banque de France.

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