Gaussian Inference In Ar(1) Time Series With Or Without A Unit Root
This paper introduces a simple first-difference-based approach to estimation and inference for the AR(1) model. The estimates have virtually no finite-sample bias and are not sensitive to initial conditions, and the approach has the unusual advantage that a Gaussian central limit theory applies and is continuous as the autoregressive coefficient passes through unity with a uniform null rate of convergence. En route, a useful central limit theorem (CLT) for sample covariances of linear processes is given, following Phillips and Solo (1992, Annals of Statistics , 20, 971–1001). The approach also has useful extensions to dynamic panels.
Volume (Year): 24 (2008)
Issue (Month): 03 (June)
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- Peter C.B. Phillips & Victor Solo, 1989. "Asymptotics for Linear Processes," Cowles Foundation Discussion Papers 932, Cowles Foundation for Research in Economics, Yale University.
- Peter C.B. Phillips & Tassos Magdalinos, 2004.
"Limit Theory for Moderate Deviations from a Unit Root,"
Cowles Foundation Discussion Papers
1471, Cowles Foundation for Research in Economics, Yale University.
- Phillips, Peter C.B. & Magdalinos, Tassos, 2007. "Limit theory for moderate deviations from a unit root," Journal of Econometrics, Elsevier, vol. 136(1), pages 115-130, January.
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