Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-22T22:09:14.273Z Has data issue: false hasContentIssue false

Distribution of the ML Estimator of an MA(1) and a local level model

Published online by Cambridge University Press:  11 February 2009

Neil Shephard
Affiliation:
Nuffield College, Oxford

Abstract

Although considerable attention has recently been paid to the behavior of the maximum likelihood estimator of simple moving average models, little progress has been made in finding a good approximation to its distribution in cases where the process is close to being noninvertible. In this paper a method is produced that gives an excellent approximation to the distribution function, even in the case where the process is strictly noninvertible. Also studied is the related problem of the distribution of the maximum likelihood estimator of the signalto-noise ratio in the local level model.

Type
Articles
Copyright
Copyright © Cambridge University Press 1993

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Anderson, T.W.The Statistical Analysis of Time Series. New York: Wiley, 1971.Google Scholar
2.Anderson, T.W. & Takemura, A.. Why do noninvertible estimated moving averages occur? Journal of Time Series Analysis 7 (1986): 235254.CrossRefGoogle Scholar
3.Ansley, C.F. & Newbold, P.. Finite sample properties of estimators for autoregressive moving average models. Journal of Econometrics 13 (1980): 159183.CrossRefGoogle Scholar
4.Barndorff-Nielson, O.E. & Cox, D.R.. Asymptotic Techniques for Use in Statistics. London: Chapman and Hall, 1989.CrossRefGoogle Scholar
5.Cooper, D.M. & Thompson, R.. A note on the estimation of the parameters of the autoregressive-moving average process. Biometrika 64 (1977): 625628.Google Scholar
6.Cryer, J.D. & Ledolter, J.. Small-sample properties of the maximum likelihood estimator in the first-order moving average model. Biometrika 68 (1981): 691694.Google Scholar
7.Daniels, H.E.Saddlepoint approximations for estimating equations. Biometrika 70 (1983): 8996.CrossRefGoogle Scholar
8.Davidson, J. Small sample properties of estimators of the moving average process. In Charatsis, E.G. (ed.), Proceedings of the Econometric Society Meeting 1979: Selected Econometric Papers in Memory of Stephan Valananis. Amsterdam: North-Holland, 1979.Google Scholar
9.Davidson, J.Problems with the estimation of moving average processes. Journal of Econometrics 16 (1981): 295310.CrossRefGoogle Scholar
10.Davies, R.B.Numerical inversion of a characteristic function. Biometrika 60 (1973): 415417.CrossRefGoogle Scholar
11.Davies, R.B.AS 155: The distribution of a linear combination of χ2 random variables. Applied Statistics 29 (1980): 323333.CrossRefGoogle Scholar
12.Dent, W. & Min, A-S.. A Monte Carlo study of autoregressive integrated moving average processes. Journal of Econometrics 7 (1978): 2355.CrossRefGoogle Scholar
13.Farebrother, R.W.AS 153: Pan's procedure for the tail probabilities of the Durbin-Watson statistic. Applied Statistics 29 (1980): 224227.CrossRefGoogle Scholar
14.Field, C.A. & Hampel, F.R.. Small-sample asymptotic distributions of M-estimators of location. Biometrika 69 (1982): 2946.CrossRefGoogle Scholar
15.Grenander, U. & Szego, G.. Toeplitz Forms and Their Applications. Berkeley, CA: University of California Press, 1958.CrossRefGoogle Scholar
16.Hannan, E.J.Multiple Time Series. New York: Wiley, 1970.CrossRefGoogle Scholar
17.Harvey, A.C.Time Series Models. Oxford: Philip Allen Publishers Limited, 1981.Google Scholar
18.Harvey, A.C., Henry, B., Peters, S., & Wren-Lewis, S.. Stochastic trends in dynamic regression models: An application to the employment-output equation. Economic Journal 96 (1986): 975985.CrossRefGoogle Scholar
19.Harvey, A.C. & Stock, J.H.. Continuous time autoregressive models with common stochastic trends. Journal of Economic Dynamics and Control 12 (1988): 365384.CrossRefGoogle Scholar
20.Harvey, A.C.Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
21.Harville, D.A.Bayesian inference for variance components using only error contrasts. Biometrika 61 (1974): 383385.CrossRefGoogle Scholar
22.Huber, P.J.Robust estimation of a location parameter. Annals of Mathematical Statistics 35 (1964): 73101.CrossRefGoogle Scholar
23.Huzurbazar, V.S.The likelihood equation, consistency and the maxima of the likelihood function. Annals of Eugenics 14 (1948): 185200.CrossRefGoogle ScholarPubMed
24.Kalbfleisch, J.D. & Sprott, D.A.. Applications of likelihood methods to models involving large numbers of parameters. Journal of the Royal Statistical Society, Series B 32 (1970): 175194.Google Scholar
25.Kang, K.M. A Comparison of Estimators of Moving Average Processes, unpublished, from the Australian Bureau of the Census.Google Scholar
26.McCullagh, P. & Nelder, J.A.. Generalized Linear Models, 2nd ed.London: Chapman and Hall, 1989.CrossRefGoogle Scholar
27.Muth, J.F.Optimal properties of exponentially weighted forecasts. Journal of the American Statistical Association 55 (1960): 299305.CrossRefGoogle Scholar
28.Pesaran, M.H.A note on the maximum likelihood estimation of regression models with first order moving average errors with roots on the unit circle. Australian Journal of Statistics 25 (1983): 442448.CrossRefGoogle Scholar
29.Pötscher, B.M.Noninvertibility and pseudo maximum likelihood estimation of misspecified ARMA models. Econometric Theory 7 (1991): 435449.CrossRefGoogle Scholar
30.Robinson, D.L.Estimation and use of variance components. The Statistician 36 (1987): 314.CrossRefGoogle Scholar
31.Robinson, P.M. Robust nonparametric autoregression. In Frenke, J., Hardie, W., & Martin, D. (eds.), Robust and Nonlinear Time Series Analysis, Lecture Notes in Statistics, Vol. 26, pp. 247255. New York: Springer-Verlag, 1984.CrossRefGoogle Scholar
32.Sargan, J.E. & Bhargava, A.. Maximum likelihood estimation of regression models with first order moving average errors when the root lies on the unit circle. Econometrica 51 (1983): 799820.CrossRefGoogle Scholar
33.Shephard, N.Maximum likelihood estimation of regression models with stochastic trend components. Journal of the American Statistical Association 88 (1993): forthcoming.CrossRefGoogle Scholar
34.Shephard, N. & Harvey, A.C.. On the probability of estimating a deterministic component in the local level model. Journal of Time Series Analysis 11 (1990): 339347.CrossRefGoogle Scholar
35.Tanaka, K. & Satchell, S.E.. Asymptotic properties of the maximum likelihood and nonlienar least squares estimator for noninvertible moving average models. Econometric Theory 5 (1989): 333353.CrossRefGoogle Scholar
36.West, M. & Harrison, P.J.. Bayesian Forecasting and Dynamic Models. New York: Springer-Verlag, 1989.CrossRefGoogle Scholar
37.White, J.S.The limiting distribution of the serial correlation coefficient in the explosive case. Annals of Mathematical Statistics 29 (1958): 11881197.CrossRefGoogle Scholar