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Seasonality in Regression: An Application of Smoothness Priors

  • Mark Gersovitz
  • James G. MacKinnon

This article argues that conventional approaches to the treatment of seasonality in econometric investigation are often inappropriate. A more appropriate technique is to allow all regression coefficients to vary with the season, but to constrain them to do so in a smooth fashion. A Bayesian method of estimating smoothly varying seasonal coefficients is developed, based on Shiller's (1973) approach to estimating distributed lags. In a sampling experiment, this technique outperforms ordinary least squares by a substantial margin. An application of this technique to the estimation of the demand for soft drinks is also presented.

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Paper provided by Queen's University, Department of Economics in its series Working Papers with number 257.

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Length: 20
Date of creation: 1977
Date of revision:
Publication status: Published in Journal of the American Statistical Association, 73, 1978
Handle: RePEc:qed:wpaper:257
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  1. Michael C. Lovell, 1963. "Seasonal Adjustment of Economic Time Series and Multiple Regression," Cowles Foundation Discussion Papers 151, Cowles Foundation for Research in Economics, Yale University.
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