Instrumental Variable Interpretation Of Cointegration With Inference Results For Fractional Cointegration
AbstractIn this paper we propose an alternative characterization of the central notion of cointegration, exploiting the relationship between the autocovariance and the cross-covariance functions of the series. This characterization leads us to propose a new estimator of the cointegrating parameter based on the instrumental variables (IV) methodology. The instrument is a delayed regressor obtained from the conditional bivariate system of nonstationary fractionally integrated processes with a weakly stationary error correction term. We prove the consistency of this estimator and derive its limiting distribution. We also show that, in the I(1) case, with a semiparametric correction simpler than the one required for the fully modified ordinary least squares (FM-OLS), our fully modified instrumental variables (FM-IV) estimator is median-unbiased, a mixture of normals, and asymptotically efficient. As a consequence, standard inference can be conducted with this new FM-IV estimator of the cointegrating parameter. We show by the use of Monte Carlo simulations that the small sample gains with the new IV estimator over OLS are remarkable.
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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 18 (2002)
Issue (Month): 03 (June)
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- M. Gerolimetto & Peter M Robinson, 2006.
"Instrumental Variables Estimation of Stationaryand Nonstationary Cointegrating Regressions,"
STICERD - Econometrics Paper Series
/2006/500, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- P. M. Robinson & M. Gerolimetto, 2006. "Instrumental variables estimation of stationary and non-stationary cointegrating regressions," Econometrics Journal, Royal Economic Society, vol. 9(2), pages 291-306, 07.
- Peter M. Robinson & M. Gerolimetto, 2006. "Instrumental variables estimation of stationary and nonstationary cointegrating regressions," LSE Research Online Documents on Economics 4539, London School of Economics and Political Science, LSE Library.
- Alvaro Escribano & M. Santos & Ana Sipols, 2008. "Testing for cointegration using induced-order statistics," Computational Statistics, Springer, vol. 23(1), pages 131-151, January.
- Mauro Costantini & Roy Cerqueti, 2007. "Non parametric Fractional Cointegration Analysis," ISAE Working Papers 78, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
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