Seasonality in Regression: An Application of Smoothness Priors
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.
|Date of creation:||1977|
|Publication status:||Published in Journal of the American Statistical Association, 73, 1978|
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- 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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