Uniform Asymptotic Normality in Stationary and Unit Root Autoregression
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 which holds uniformly in the autoregressive coefficient rho including stationary and unit root cases. The rate of convergence is root of n when |rho|
|Date of creation:||2010|
|Publication status:||Published in Econometric Theory (December 2011), 27(6): 1117-1151|
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References listed on IDEAS
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"Gaussian Inference in AR(1) Time Series with or without a Unit Root,"
Cowles Foundation Discussion Papers
1546, Cowles Foundation for Research in Economics, Yale University.
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