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Real-Time or Current Vintage: Does the Type of Data Matter for Forecasting and Model Selection?

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Author Info
Hui Feng () (Department of Economics, University of Victoria)

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Abstract

In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real-time monetary aggregate M3 (1977-2000), and residential mortgage credit (1975-1998). The forecasting method we use is multi-step-ahead non-adaptive forecasting.

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File URL: http://web.uvic.ca/econ/research/papers/ewp0515.pdf
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Publisher Info
Paper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0515.

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Length: 19 pages
Date of creation: 24 Aug 2005
Date of revision:
Handle: RePEc:vic:vicewp:0515

Note: ISSN 1485-6441
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Postal: PO Box 1700, STN CSC, Victoria, BC, Canada, V8W 2Y2
Phone: (250)721-8540
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Web page: http://web.uvic.ca/econ
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Related research
Keywords: Vintage Data; Real-time Data; Model Selection; SETAR Model; ARMA model; Forecasting;

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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References listed on IDEAS
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  1. Norman Swanson, 1996. "Forecasting Using First-Available Versus Fully Revised Economic Time-Series Data," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 1(1). [Downloadable!]
  2. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November. [Downloadable!] (restricted)
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  3. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
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  4. Dean Croushore & Tom Stark, 1999. "Does data vintage matter for forecasting?," Working Papers 99-15, Federal Reserve Bank of Philadelphia. [Downloadable!]
  5. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
    Other versions:
  6. Gallegati, Mauro & Mignacca, Domenico, 1995. "Nonlinearities in Business Cycle: SETAR Models and G7 Industrial Production Data," Applied Economics Letters, Taylor and Francis Journals, vol. 2(11), pages 422-27, November. [Downloadable!] (restricted)
  7. Sowell, Fallaw, 1992. "Modeling long-run behavior with the fractional ARIMA model," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 277-302, April. [Downloadable!] (restricted)
  8. Swanson, N.R., 1996. "Forecasting Using First Available Versus Fully Revised Economic Time Series data," Papers 4-96-7, Pennsylvania State - Department of Economics.
  9. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November. [Downloadable!] (restricted)
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  10. Trivellato, Ugo & Rettore, Enrico, 1986. "Preliminary Data Errors and Their Impact on the Forecast Error of Simultaneous-Equations Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 445-53, October.
  11. Lawrence J. Christiano & Martin Eichenbaum, 1990. "Current real business cycle theories and aggregate labor market fluctuations," Discussion Paper / Institute for Empirical Macroeconomics 24, Federal Reserve Bank of Minneapolis. [Downloadable!]
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