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A Models Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks

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Author Info

  • Swanson, N.R.
  • White, H.

Abstract

We take a model selection approach to the question of whether a class of adaptive prediction models (artificial neural networks) is useful for predicting future values of nine macroeconomic variables. We use a variety of out-of-sample forecast-based model selection criteria, including forecast error measures and forecast direction accuracy. Ex ante or real-time forecasting results based on rolling window prediction methods indicate that multivariate adaptive linear vector autoregression models often outperform a variety of (1) adaptive and nonadaptive univariate models, (2) nonadaptive multivariate models, (3) adaptive nonlinear models, and (4) professionally available survey predictions. Further, model selection based on the in-sample Schwarz information criterion apparently fails to offer a convenient shortcut to true out-of-sample performance measures. © 1997 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology

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Bibliographic Info

Paper provided by Pennsylvania State - Department of Economics in its series Papers with number 04-95-12.

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Length: 39 pages
Date of creation: 1995
Date of revision:
Handle: RePEc:fth:pensta:04-95-12

Contact details of provider:
Postal: PENNSYLVANIA STATE UNIVERSITY, DEPARTMENT OF ECONOMICS, UNIVERSITY PARK PENNSYLVANIA 16802 U.S.A.
Phone: (814)865-1456
Fax: (814)863-4775
Web page: http://econ.la.psu.edu/
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Related research

Keywords: MACROECONOMICS; ECONOMIC FORECASTING;

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References

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  1. Sawa, Takamitsu, 1978. "Information Criteria for Discriminating among Alternative Regression Models," Econometrica, Econometric Society, vol. 46(6), pages 1273-91, November.
  2. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-90, June.
  3. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-75, July.
  4. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-33, October.
  5. 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.
  6. Granger, Clive W J, 1993. "Strategies for Modelling Nonlinear Time-Series Relationships," The Economic Record, The Economic Society of Australia, vol. 69(206), pages 233-38, September.
  7. Victor Zarnowitz & Phillip Braun, 1992. "Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance," NBER Working Papers 3965, National Bureau of Economic Research, Inc.
  8. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
  9. Stekler, H. O., 1991. "Macroeconomic forecast evaluation techniques," International Journal of Forecasting, Elsevier, vol. 7(3), pages 375-384, November.
  10. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June.
  11. Keane, Michael & Runkle, David E, 1995. "Testing the Rationality of Price Forecasts: Reply," American Economic Review, American Economic Association, vol. 85(1), pages 290, March.
  12. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  13. Meese, Richard A & Rogoff, Kenneth, 1988. " Was It Real? The Exchange Rate-Interest Differential Relation over the Modern Floating-Rate Period," Journal of Finance, American Finance Association, vol. 43(4), pages 933-48, September.
  14. Bruce Mizrach, 1996. "Forecast Comparison in L2," Departmental Working Papers 199524, Rutgers University, Department of Economics.
  15. Francis X. Diebold & Glenn D. Rudebusch, 1989. "Forecasting output with the composite leading index: an ex ante analysis," Finance and Economics Discussion Series 90, Board of Governors of the Federal Reserve System (U.S.).
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