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Statistical Inference in Regressions with Integrated Processes: Part 2

Published online by Cambridge University Press:  18 October 2010

Joon Y. Park  and
Peter C.B. Phillips
Joon Y. Park
Affiliation:
Cowles Foundation for Research in Economics, Yale University
Peter C.B. Phillips
Affiliation:
Cowles Foundation for Research in Economics, Yale University
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Abstract

This paper continues the theoretical investigation of Park and Phillips. We develop an asymptotic theory of regression for multivariate linear models that accommodates integrated processes of different orders, nonzero means, drifts, time trends, and cointegrated regressors. The framework of analysis is general but has a common architecture that helps to simplify and codify what would otherwise be a myriad of isolated results. A good deal of earlier research by the authors and by others comes within the new framework. Special models of some importance are considered in detail, such as VAR systems with multiple lags and cointegrated variates.

Type
Articles
Information
Econometric Theory , Volume 5 , Issue 1 , April 1989 , pp. 95 - 131
Copyright
Copyright © Cambridge University Press 1989

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References

REFERENCES

1.Engle, R.F. & Granger, C.W.J.. Cointegration and error correction: Representation, estimation and testing. Econometica 55 (1987): 251–276.CrossRefGoogle Scholar
2.Fuller, W.A., Hasza, D.P., & Goebel, J.J.. Estimation of parameters of stochastic difference equations. Annals of Statistics 9 (1981): 531–543.CrossRefGoogle Scholar
3.Hannan, E.J.Multiple Time Series. New York: John Wiley, 1970.CrossRefGoogle Scholar
4.Hasza, D.P. & Fuller, W.A.. Estimation of autoregressive processes with unit roots. Annals of Statistics 7 (1979): 1106–1120.CrossRefGoogle Scholar
5.McLeish, D.L.A maximal inequality and dependent strong laws. Annals of Probability 3 (1975): 829–839.CrossRefGoogle Scholar
6.Nankervis, J.C. & Savin, N.E.. Finite sample distributions of t and F statistics in an AR(1) model with an exogenous variable. Mimeographed, University of Iowa, 1986.Google Scholar
7.Park, J.Y. & Phillips, P.C.B.. Statistical inference in regressions with integrated processes: Part 1. Cowles Foundation Discussion Paper No. 811, Yale University, 1986.Google Scholar
8.Phillips, P.C.B.Understanding spurious regressions in econometrics. Journal of Econometrics 33 (1986): 311–340.CrossRefGoogle Scholar
9.Phillips, P.C.B. & Durlauf, S.N.. Multiple time series regression with integrated processes. Review of Economic Studies 53 (1986): 473–496.CrossRefGoogle Scholar
10.Phillips, P.C.B. & Ouliaris, S.. Testing for cointegration using principal components methods. Cowles Foundation Discussion Paper No. 809R, Yale University, 1986/1987.Google Scholar
11.Sims, C.A.Least squares estimation of autoregressions with some unit roots. CERDE Discussion Paper No. 78–95, University of Minnesota, 1978.Google Scholar
12.Sims, C.A., Stock, J.H., & Watson, M.W.. Inference in linear time series models with some unit roots. Mimeographed, Minnesota University, 1986.Google Scholar
13.White, H.Asymptotic Theory for Econometricians. New York: Academic Press, 1984.Google Scholar