In general, procedures for the analysis of interrupted time series are quite sophisticated
and powerful. However, procedures for identifying the intervention component of inter
rupted time-series models remain relatively primitive. In this article we demonstrate how
exponential smoothing can play a function in the identification of the intervention
component of an interrupted time-series model that is analogous to the function that the
sample autocorrelation and partial autocorrelation functions serve in the identification of
the noise portion of such a model.
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References
1.
Box, G.E.P. and G.C. Tiao (1975) "Intervention analysis with application to economic and environmental problems." J. of the Amer. Stat. Assn.70: 70-79.
2.
Brown, R.G. (1963) Smoothing Forecasting and Prediction of Discrete Time Series. Englewood Cliffs, NJ. Prentice-Hall.
3.
———( 1959) Statistical Forecasting for Inventory Control. New York: McGraw-Hill.
4.
Gardner, Jr., E.S. and D.G. Dannenbring (1980) "Forecasting with exponential smoothing: some guidelines for model selection." Decision Sciences11: 370-83.
5.
Glass, G.V., V.L. Wilson and J.M. Gottman (1975) Design and Analysis of Time Series Experiments . Boulder: Colorado Associated Univ. Press.
6.
Hamilton, L.C. (1982) "Response to water conservation campaigns." Evaluation Rev.6: 673-688.
7.
Holtzman, W. (1963) "Statistical models for the study of change in the single case," in C. W. Harris (ed.) Problems in Measuring Change. Madison: Univ. of Wisconsin Press.
8.
Judd, C.M. and D.A. Kenny (1981) Estimating the Effects of Social Interventions . Cambridge: Cambndge Univ. Press .
9.
Makridakis, S., S. Wheelwright, and V. McGee (1983) Forecasting Methods and Applications (2nd Edition). New York: John Wiley.
10.
McCain, L.J. and R. McCleary (1979) "The statistical analysis of the simple interrupted time-series quasi-experiment," in T. D. Cook andD. T. Campbell (eds.) Quasi-Expenmentation: Design and Analysis Issues for Field Settings. Chicago: Rand McNally.
11.
McCleary, R. and R.A. Hay, Jr. (1980) Applied Time Series Analysis for the Social Sciences. Beverly Hills, CA: Sage .
12.
Montgomery, D.C. and L.A. Johnson (1976) Forecasting and Time Series Analysis. New York: McGraw-Hill.
13.
Tukey, John (1977) EDA: Exploratory Data AnalysisReading, MA: Addison-Wesley.
