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UNIFORM ASYMPTOTIC NORMALITY IN STATIONARY AND UNIT ROOT AUTOREGRESSION

Published online by Cambridge University Press:  14 April 2011

Chirok Han ,
Peter C. B. Phillips  and
Donggyu Sul
Chirok Han
Affiliation:
Korea University
Peter C. B. Phillips*
Affiliation:
Yale University, University of Auckland, University of Southampton, and Singapore Management University
Donggyu Sul
Affiliation:
University of Texas at Dallas
*
*Address correspondence to Peter C.B. Phillips, Department of Economics, Yale University Box 208281, New Haven, CT 06520-8281, USA; e-mail: [email protected].
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Abstract

While differencing transformations can eliminate nonstationarity, they typically reduce signal strength and correspondingly reduce rates of convergence in unit root autoregressions. The present paper shows that aggregating moment conditions that are formulated in differences provides an orderly mechanism for preserving information and signal strength in autoregressions with some very desirable properties. In first order autoregression, a partially aggregated estimator based on moment conditions in differences is shown to have a limiting normal distribution that holds uniformly in the autoregressive coefficient ρ, including stationary and unit root cases. The rate of convergence is when and the limit distribution is the same as the Gaussian maximum likelihood estimator (MLE), but when ρ = 1 the rate of convergence to the normal distribution is within a slowly varying factor of n. A fully aggregated estimator (FAE) is shown to have the same limit behavior in the stationary case and to have nonstandard limit distributions in unit root and near integrated cases, which reduce both the bias and the variance of the MLE. This result shows that it is possible to improve on the asymptotic behavior of the MLE without using an artificial shrinkage technique or otherwise accelerating convergence at unity at the cost of performance in the neighborhood of unity. Confidence intervals constructed from the FAE using local asymptotic theory around unity also lead to improvements over the MLE.

Type
ARTICLES
Information
Econometric Theory , Volume 27 , Issue 6 , December 2011 , pp. 1117 - 1151
Copyright
Copyright © Cambridge University Press 2011

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