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Working Paper

Forecasting and Seasonal Adjustment

There have been many studies and papers written about the effects of seasonal adjustment on the relationships among variables. However, there has been a dearth of studies about the effects of seasonal adjustment on the problem of forecasting. Since the development of time serles models often has forecasting as a major product, it is essential to study the effects of seasonal adjustment on forecasting in these models. In this paper, we presentan application of multivariate time series forecasting applied to five economic time series, in which we compare forecasts developed from seasonally adjusted data with forecasts from seasonally not-adjusted data. The results of this exercise are mixed. For some forecasting situations, using not-seasonally adjusted data provides better forecasts for most of the variables In this study. However, in other instances, using seasonally adjusted data provides better forecasts for most of the variables in this study. The results appear to depend on the length of the forecast period. Also, it appears that the best solution in some instances might be to develop models for both seasonally adjusted data and not-seasonally adjusted data.

Working Papers of the Federal Reserve Bank of Cleveland are preliminary materials circulated to stimulate discussion and critical comment on research in progress. They may not have been subject to the formal editorial review accorded official Federal Reserve Bank of Cleveland publications. The views expressed in this paper are those of the authors and do not represent the views of the Federal Reserve Bank of Cleveland or the Federal Reserve System.


Suggested Citation

Bagshaw, Michael L. 1985. “Forecasting and Seasonal Adjustment.” Federal Reserve Bank of Cleveland, Working Paper No. 85-07.