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Recursive Models for Forecasting Seasonal Processes

Published online by Cambridge University Press:  19 October 2009

James E. Reinmuth  and
Dick R. Wittink
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Extract

Many of the typical problems encountered in forecasting a time series are alleviated when the series follows a seasonal pattern. The seasonal effect is independent of a long-term trend and cyclic effect. Furthermore, since the seasonal effect is recurrent and periodic, it is predictable. Thus, when the time series follows a seasonal pattern, the general shape of the series is known. Questions regarding the growth trend of the series, the expansion and contraction of the seasonal pattern, and random variation affecting the series are, however, left unanswered.

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Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 9 , Issue 4 , September 1974 , pp. 659 - 684
Copyright
Copyright © School of Business Administration, University of Washington 1974

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References

[1]Almon, S. “The Distributed Lag between Capital Appropriations and Expenditures.” Econometrica, vol. 33 (January 1965), pp. 178–196.CrossRefGoogle Scholar
[2]Bear, D.V.T. “Distributed Lags and Economic Theory.” Review of Economic Studies, vol. 33 (July 1966), pp. 235–243.CrossRefGoogle Scholar
[3]Brown, Robert G.Smoothing, Forecasting, and Prediction of Discrete Time Series. Englewood Cliffs, N.J.: Prentice Hall, Inc., 1963.Google Scholar
[4]Coen, P.J.; Gomme, E.D.; and Kendall, M.G.. “Lagged Relationships in Economic Forecasting.” Journal of the Royal Statistical Society, vol. 132, pt. 2 (1969).Google Scholar
[5]Draper, N.R., and Smith, H.. Applied Regression Analysis. New York: John Wiley & Sons, Inc., 1966.Google Scholar
[6]Enrick, Norbert L.Market and Sales Forecasting: A Quantitative Approach. San Francisco: Chandler Publishing Co., 1969.Google Scholar
[7]Goldberger, Arthur S.Econometric Theory. New York: John Wiley & Sons, Inc., 1964.Google Scholar
[8]Gurland, J. “An Example of Autocorrelated Disturbances in Linear Regression.” Econometrica, vol. 22 (April 1954), pp. 218–227.CrossRefGoogle Scholar
[9]Heckman, Dennis. “Higher Order Exponential Smoothing.” Unpublished research paper, College of Business, University of Oregon, 1968.Google Scholar
[10]Hooper, J.W., and Zellner, A.. “The Error of Forecast for Multivariate Models.” Econometrica, vol. 29 (October 1961), pp. 544–555.CrossRefGoogle Scholar
[11]Hurwicz, Leonid. “Variable Parameters in Stochastic Processes: Trend and Seasonality.” In Statistical Inference in Dynamic Economic Models, edited by Koopmans, T.C., Chap. 11. New York: John Wiley & Sons, Inc., 1950.Google Scholar
[12]Jorgenson, D.W. “Rational Distributed Lag Functions.” Econometrica, vol. 34 (January 1966), pp. 135–149.CrossRefGoogle Scholar
[13]Klein, Lawrence R.A Textbook of Econometrics. Evanston, Ill.: Row, Peterson and Co., 1953.Google Scholar
[14]Koyck, L.M.Distributed Lags and Investment Analysis. Amsterdam: North-Holland Publishing Co., 1954.Google Scholar
[15]Ladd, George W. “Regression Analysis of Seasonal Data.” Journal of the American Statistical Association, vol. 59, no. 306 (June 1964), pp. 402–421.CrossRefGoogle Scholar
[16]Lorie, James H. “Two Important Problems in Sales Forecasting.” Journal of Business, vol. 30, no. 3 (1957), pp. 172–179.CrossRefGoogle Scholar
[17]Lovell, M.C. “Seasonal Adjustment of Economic Time Series and Multiple Regression Analysis.” Journal of the American Statistical Association, vol. 58 (December 1963), pp. 993–1010.CrossRefGoogle Scholar
[18]Mendenhall, William, and Reinmuth, James E.. Statistics for Management and Economics. Belmont, Calif.: Duxbury Press, 1971, Chap. 15.Google Scholar
[19]Reinmuth, James E. “Recursive Estimation.” Paper submitted to the Australian Journal of Statistics.Google Scholar
[20]Reinmuth, James E., and Geurts, Michael D.. “A Bayesian Approach to Forecasting Effects of Atypical Situations.” Journal of Marketing Research, vol. 9 (August 1972), pp. 292–297.CrossRefGoogle Scholar
[21]Stekler, H.O. “Forecasting and Analysis with an Econometric Model: Comment.” American Economic Review, vol. 61 (December 1966), pp. 1241–1248.Google Scholar
[22]Suits, Daniel. “Forecasting and Analysis with an Econometric Model.” American Economic Review, vol. 52 (March 1962), pp. 104–132.Google Scholar
[23]Theil, Henri. Economic Forecasts and Policy, 2nd ed.Amsterdam: North-Holland Publishing Co., 1961.Google Scholar
[24]Whiteman, Irvin R. “Improved Forecasting through Feedback.” Journal of Marketing, vol. 30, no. 2 (1966), pp. 45–51.CrossRefGoogle Scholar
[25]Winters, Peter S. “Forecasting Sales by Exponential Weighted Moving Averages.” Management Science, vol. 6, no. 3 (April 1960), pp. 324–342.CrossRefGoogle Scholar