Hostname: page-component-84b7d79bbc-g7rbq Total loading time: 0 Render date: 2024-07-26T13:22:40.109Z Has data issue: false hasContentIssue false

A Note on Autoregressive Modeling

Published online by Cambridge University Press:  11 February 2009

D.S. Poskitt
Affiliation:
Australian National University

Abstract

This paper addresses the problem of estimating vector autoregressive models. An approach to handling nonstationary (integrated) time series is briefly discussed, but the main emphasis is upon the estimation of autoregressive approximations to stationary processes. Three alternative estimators are considered–the Yule-Walker, least-squares, and Burg-type estimates–and a complete analysis of their asymptotic properties in the stationary case is given. The results obtained, when placed together with those found elsewhere in the literature, lead to the direct recommendation that the less familiar Burg-type estimator should be used in practice when modeling stationary series. This is particularly so when the underlying objective of the analysis is to investigate the interrelationships between variables of interest via impulse response functions and dynamic multipliers.

Type
Articles
Copyright
Copyright © Cambridge University Press 1994

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

1Akaike, H.Fitting autoregressive models for prediction. Annals of the Institute of Statistical Mathematics 21 (1969): 243247.CrossRefGoogle Scholar
2An, H.-Z.Chen, Z.-G. & Hannan, E.J.. Autocorrelation, autoregression and autoregressive approximation. Annals of Statistics 10 (1982): 926936.CrossRefGoogle Scholar
3Birkhoff, G. & MacLane, S.. A Survey of Modern Algebra, 4th ed.New York: Macmillan, 1977.Google Scholar
4Burg, J.P. A New Analysis Technique for Time Series Data. Presented at the Advanced Study Institute on Signal Processing, NATO, Enschede, The Netherlands, 1968.Google Scholar
5Dégerine, S.Canonical partial autocorrelation function of a multivariate time series. Annals of Statistics 18 (1990): 961971.CrossRefGoogle Scholar
6Hannan, E.J. & Deistler, M.. The Statistical Theory of Linear Systems. New York: Wiley, 1988.Google Scholar
7Hannan, E.J. & Kavalieris, L.. Regression, autoregression models. Journal of Time Series Analysis 7 (1986): 2749.CrossRefGoogle Scholar
8Hannan, E.J. & Poskitt, D.S.. Unit canonical correlations between future and past. Annals of Statistics 16 (1988): 784790.CrossRefGoogle Scholar
9Levinson, N.The Wiener RMS (root mean square) error criterion in filter design and prediction. Journal of Mathematical Physics 25 (1947): 261278.CrossRefGoogle Scholar
10Lewis, R. & Reinsel, G.C.. Prediction of multivariate time series by autoregressive model fitting. Journal of Multivariate Analysis 16 (1985): 393411.CrossRefGoogle Scholar
11Lütkepohl, H.Asymptotic distribution of the moving average coefficients of an estimated vector autoregressive process. Econometric Theory 4 (1988): 7785.CrossRefGoogle Scholar
12Lütkepohl, H.A note on the asymptotic distribution of impulse response functions of estimated VAR models with orthogonal residuals. Journal of Econometrics 42 (1989): 371376.CrossRefGoogle Scholar
13Lütkepohl, H.Asymptotic distributions of impulse response functions and forecast error variance decompositions of vector autoregressive models. Review of Economics and Statistics 72 (1990): 116125.CrossRefGoogle Scholar
14Lütkepohl, H. & Poskitt, D.S.. Estimating orthogonal impulse responses via vector autoregressive models. Econometric Theory 7 (1991): 487496.CrossRefGoogle Scholar
15Lysne, D. & Tjøstheim, D.. Loss of spectral peaks in autoregressive spectral estimation. Biometrika 74 (1987): 200206.CrossRefGoogle Scholar
16Morf, M., Vieira, A. & Kailath, T.. Covariance characterisation by partial autocorrelation matrices. Annals of Statistics 6 (1978): 643648.CrossRefGoogle Scholar
17Morf, M., Vieira, A., Lee, D. & Kailath, T.. Recursive multichannel maximum entropy spectral estimation. IEEE Transactions on Geoscience Electronics GE-16 (1978): 8594.CrossRefGoogle Scholar
18Mudholkar, G.S. & Rao, P.S.R.S.. Some sharp multivariate Tchebycheff inequalities. Annals of Mathematical Statistics 38 (1967): 393401.CrossRefGoogle Scholar
19Parzen, E. Multiple time series modeling. In Krishnaiah, P.R. (ed.), Multivariate Analysis II, pp. 389409. New York: Academic Press, 1969.Google Scholar
20Paulsen, J.Order determination of multivariate autoregressive time series with unit roots. Journal of Time Series Analysis 5 (1984): 115127.CrossRefGoogle Scholar
21Paulsen, J. & Tjøstheim, D.. On the estimation of residual variance and order in autoregressive time series. Journal of the Royal Statistical Society B-47 (1985): 216228.Google Scholar
22Phillips, P.C.B. & Durlauf, S.N.. Multiple time series regression with integrated processes. Review of Economic Studies 53 (1986): 473495.CrossRefGoogle Scholar
23Poskitt, D.S.Autoregressive approximations and testing for spectral zeroes (invertibility). Symposium: New Directions in Time Series Analysis. Internationales Wissenschaftsforum, Heidelberg, 1992.Google Scholar
24Rozanov, Yu. A.Stationary Random Processes. San Francisco: Holden-Day 1967.Google Scholar
25Sims, C.A.Macroeconomics and reality. Econometrica 48 (1980): 148.CrossRefGoogle Scholar
26Tanaka, K.Testing for a moving average unit root. Econometric Theory 6 (1990): 433444.CrossRefGoogle Scholar
27Tjøstheim, D. & Paulsen, J.. Empirical identification of multiple time series. Journal of Time Series Analysis 3 (1982): 265282.CrossRefGoogle Scholar
28Tjøstheim, D. & Paulsen, J.. Bias of some commonly-used time series estimators. Biometrika 70 (1983): 389400.CrossRefGoogle Scholar
29Tsay, R.S. & Tiao, G.C.. Asymptotic properties of multivariate nonstationary processes with applications to autoregressions. Annals of Statistics 18 (1990): 220250.CrossRefGoogle Scholar
30Whittle, P.On the fitting of multivariate autoregression and the approximate canonical factorisation of a spectral density matrix. Biometrika 50 (1963): 129134.CrossRefGoogle Scholar
31Wiggins, R.A. & Robinson, E.A.. Recursive solution to the multichannel filtering problem. Journal of Geophysical Research 70 (1965): 18851891.CrossRefGoogle Scholar
32Yamamoto, T. & Kunitomo, N.. Asymptotic bias of the least squares estimator for multivariate autoregressive models. Annals of the Institute of Statistical Mathematics A36 (1984): 419430.CrossRefGoogle Scholar