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Improving the forecast accuracy of provisional data: an application of the Kalman filter to retail sales estimates

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  • B. Dianne Pauls

Abstract

If forecasts of economic activity are to rely on preliminary data, the predictable component of the data revisions should be taken into account. This paper applies the Kalman filter to improve the forecast accuracy of published preliminary estimates of retail sales. Successive estimates of retail sales are modeled jointly as a vector autoregressive process, incorporating panel rotation and calendar effects. Estimates of retail sales based on this model are then combined with the raw Census estimates via the Kalman filter. This technique, which may be applied to other bodies of data, yields a significant improvement in the efficiency of the raw Census data, reducing the mean-squared error by about 1/3.

Suggested Citation

  • B. Dianne Pauls, 1987. "Improving the forecast accuracy of provisional data: an application of the Kalman filter to retail sales estimates," International Finance Discussion Papers 318, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:318
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    References listed on IDEAS

    as
    1. William E. Conrad & Carol Corrado, 1978. "Applications of the Kalman filter to revisions in monthly retail sales estimates," Special Studies Papers 125, Board of Governors of the Federal Reserve System (U.S.).
    2. Howrey, E Philip, 1978. "The Use of Preliminary Data in Econometric Forecasting," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 193-200, May.
    3. Rosanne Cole, 1969. "Introduction to "Errors in Provisional Estimates of Gross National Product"," NBER Chapters, in: Errors in Provisional Estimates of Gross National Product, pages 3-6, National Bureau of Economic Research, Inc.
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    Keywords

    Forecasting; Retail trade;

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