The I(1) strategy can be made robust. This paper has indicated methods by which compromise df can be chosen empirically for reasonable efficiency across a range of characteristics and population parameters. Finally, the I(1) strategy is defensible. It has a motivating statistical model but does not require correctness of that model. Choice of a particular strategy can build on extensive knowledge of related populations. If novel estimation problems are encountered, appropriate estimation techniques can be developed theoretically by going back to the motivating model, and then those techniques could be evaluated with decision-theoretic criteria when the motivating model does not hold.
Finally, it is important to note that although this paper has focused on the class of α-smoothers derived from an I(1) strategy, any other strategies could be evaluated with similar decision-theoretic criteria.
Binder, D.A., and Hidiroglou, M.A. (1988), Sampling in time, in Handbook of statistics, Vol. 6, eds. P.R. Krishnaiah and C.R. Rao, pp. 187–211. Amsterdam: North-Holland.
Brockwell, P.J., and Davis, R.A. (1991). Time series: Theory and methods, 2nd ed. New York: Springer-Verlag.
Durbin, J., and Koopman, S.J. (2001). Time series analysis by state space methods. Oxford, England: Oxford University Press.
Gijbels, I., Pope, A., and Wand, M.P. (1999). Understandingexponential smoothing via kernel regression. Journal of the Royal Statistical Society, Series B, 61, 39– 50.
Harvey, A.C. (1989). Forecasting, structural time series models and the Kalman filter. Cambridge, England: Cambridge University Press.
National Research Council. (2001). The American Community Survey: Summary of a Workshop. Committee on National Statistics. Washington, DC: National Academy Press.
Scott, A.J., and Smith, T.M.F. (1974). Analysis of repeatedsurveys using time series methods. Journal of the American Statistical Association, 69, 674–678.