Uniform Asymptotic Normality In Stationary And Unit Root Autoregression
AbstractWhile 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 which holds uniformly in the autoregressive coefficient rho including stationary and unit root cases. The rate of convergence is root of n when |rho|
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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 27 (2011)
Issue (Month): 06 (December)
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Other versions of this item:
- Chirok Han & Peter C.B. Phillips & Donggyu Sul, 2010. "Uniform Asymptotic Normality in Stationary and Unit Root Autoregression," Cowles Foundation Discussion Papers 1746, Cowles Foundation for Research in Economics, Yale University.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Phillips, Peter C.B. & Han, Chirok, 2008.
"Gaussian Inference In Ar(1) Time Series With Or Without A Unit Root,"
Cambridge University Press, vol. 24(03), pages 631-650, June.
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Cowles Foundation Discussion Papers
1655, Cowles Foundation for Research in Economics, Yale University.
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- Peter C.B. Phillips, 1993.
"Fully Modified Least Squares and Vector Autoregression,"
Cowles Foundation Discussion Papers
1047, Cowles Foundation for Research in Economics, Yale University.
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"Estimators For Persistent And Possibly Nonstationary Data With Classical Properties,"
Cambridge University Press, vol. 28(05), pages 1003-1036, October.
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