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A state space forecasting model with fiscal and monetary control

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  • Donald S. Allen
  • Meenakshi Pasupathy

Abstract

In this paper we model the U.S. economy parsimoniously in an a theoretic state space representation. We use monthly data for thirteen macroeconomic variables. We treat the federal deficit as a proxy for fiscal policy and the fed funds rate as a proxy for monetary policy and use each of them as control (exogenous) variables, and designate the rest as state variables. The output (measured) variable is the growth rate of quarterly real GDP which we interpolate to obtain a monthly equivalent. We specify a linear relation between state variables and implicitly allow for time variation of the relationship by using a recursive least squares (RLS) with forgetting factor algorithm to estimate the coefficients. The model coefficients are also estimated using ordinary least squares (OLS) and the resulting forecasts (in-sample and out-of-sample) are compared. The RLS algorithm performs better in the out-of-sample forecasts, particularly for those state variables which exhibit the greatest cyclical variations. Variables which had greater stability were forecasted more precisely with OLS estimated parameters.

Suggested Citation

  • Donald S. Allen & Meenakshi Pasupathy, 1997. "A state space forecasting model with fiscal and monetary control," Working Papers 1997-017, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:1997-017
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    References listed on IDEAS

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    1. Thomas C. Chiang & Douglas R. Kahl, 1991. "Forecasting The Treasury Bill Rate: A Time-Varying Coefficient Approach," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 14(4), pages 327-336, December.
    2. Havenner, Arthur & Zhiqiang Leng, 1996. "Improved estimates of the parameters of state space time series models," Journal of Economic Dynamics and Control, Elsevier, vol. 20(5), pages 767-789, May.
    3. Bharat Trehan, 1992. "Predicting contemporaneous output," Economic Review, Federal Reserve Bank of San Francisco, pages 3-11.
    4. Mittnik, Stefan, 1990. "Macroeconomic forecasting experience with balanced state space models," International Journal of Forecasting, Elsevier, vol. 6(3), pages 337-348, October.
    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. Chiang, Thomas C & Kahl, Douglas R, 1991. "Forecasting the Treasury Bill Rate: A Time-Varying Coefficient Approach," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 14(4), pages 327-336, Winter.
    7. Swamy, P. A. V. B. & Kennickell, Arthur B. & von zur Muehlen, Peter, 1990. "Comparing forecasts from fixed and variable coefficient models: The case of money demand," International Journal of Forecasting, Elsevier, vol. 6(4), pages 469-477, December.
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