Estimating Fractional Cointegration in the Presence of Polynomial Trends

27 Pages Posted: 31 Oct 2008

See all articles by Willa W. Chen

Willa W. Chen

Texas A&M University - Department of Statistics

Clifford M. Hurvich

Stern School of Business, New York University; New York University (NYU) - Department of Information, Operations, and Management Sciences

Multiple version iconThere are 2 versions of this paper

Date Written: February 2003

Abstract

We propose and derive the asymptotic distribution of a tapered narrow-band least squares estimator(NBLSE) of the cointegration parameter β in the framework of fractional cointegration.This tapered estimator is invariant to deterministic polynomial trends. In particular, we allowfor arbitrary linear time trends that often occur in practice. Our simulations show that, in thecase of no deterministic trends, the estimator is superior to ordinary least squares (OLS) and thenontapered NBLSE proposed by P.M. Robinson when the levels have a unit root and the cointegratingrelationship between the series is weak. In terms of rate of convergence, our estimatorconverges faster under certain circumstances, and never slower, than either OLS or the nontaperedNBLSE. In a data analysis of interest rates, we find stronger evidence of cointegration ifthe tapered NBLSE is used for the cointegration parameter than if OLS is used.

Keywords: Fractional cointegration, Long memory, Tapering, Periodogram

Suggested Citation

Chen, Willa W. and Hurvich, Clifford M., Estimating Fractional Cointegration in the Presence of Polynomial Trends (February 2003). Statistics Working Papers Series, Vol. , pp. -, 2003. Available at SSRN: https://ssrn.com/abstract=1290976

Willa W. Chen (Contact Author)

Texas A&M University - Department of Statistics ( email )

155 Ireland Street
447 Blocker
College Station, TX 77843
United States

Clifford M. Hurvich

Stern School of Business, New York University ( email )

44 West 4th Street
New York, NY 10012-1126
United States

New York University (NYU) - Department of Information, Operations, and Management Sciences

44 West Fourth Street
New York, NY 10012
United States

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