Finite Sample Improvement in Statistical Inference with I(1) Processes
AbstractRobinson and Marinucci (1998) investigated the asymptotic behaviour of a narrow-band semiparametric procedure termed Frequency Domain Least Squares (FDLS) in the broad context of fractional cointegration analysis. Here we restrict to the standard case when the data are I(1) and the cointegrating errors are I(0), proving that modifications of the Fully-Modified Ordinary Least Squares (FM-OLS) procedure of Phillips and Hansen (1990) which use the FDLS idea have the same asymptotically desirable properties as FM-OLS, and, on the basis of a Monte Carlo study, find evidence that they have superior finite-sample properties; the new procedures are also shown to compare satisfactorily with parametric estimates.
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Bibliographic InfoPaper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2001/422.
Date of creation: Jul 2001
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Fully-modified ordinary least squares; finite sample improvements; statistical inference with I(1) processes; Monte Carlo study; parametric estimates.;
Other versions of this item:
- D. Marinucci & P. M. Robinson, 2001. "Finite sample improvements in statistical inference with I(1) processes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 431-444.
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