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Seasonality in Regression: An Application of Smoothness Priors

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Author Info

  • Mark Gersovitz
  • James G. MacKinnon

Abstract

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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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_257.pdf
File Function: First version 1977
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Bibliographic Info

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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Related research

Keywords: seasonality; smoothness prior; distributed lag; mixed estimation; soft drinks;

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References

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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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Citations

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Cited by:
  1. Svend Hylleberg, 2006. "Seasonal Adjustment," Economics Working Papers 2006-04, School of Economics and Management, University of Aarhus.
  2. Rodríguez Poo, Juan M. & Ferreira García, María Eva & Orbe Mandaluniz, Susan, 2001. "Nonparametric estimation of time varying parameters under shape restrictions," BILTOKI 2001-02, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
  3. Robert J. Shiller, 1982. "Smoothness Priors and Nonlinear Regression," NBER Technical Working Papers 0025, National Bureau of Economic Research, Inc.
  4. Richard M. Todd, 1989. "Periodic linear-quadratic methods for modeling seasonality," Staff Report 127, Federal Reserve Bank of Minneapolis.

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