Estimating Fractional Cointegration in the Presence of Polynomial Trends

26 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: October 2002

Abstract

We propose and derive the asymptotic distribution of a tapered narrow-band least squaresestimator (NBLSE) of the cointegration parameter β in the framework of fractional cointegration. Thistapered estimator is invariant to deterministic polynomial trends. In particular, we allow for arbitrarylinear time trends that often occur in practice. Our simulations show that, in the case of no deterministictrends, the estimator is superior to ordinary least squares (OLS) and the nontapered NBLSE proposedby P.M. Robinson when the levels have a unit root and the cointegrating relationship between the seriesis weak. In terms of rate of convergence, our estimator converges faster under certain circumstances, andnever slower, than either OLS or the nontapered NBLSE. In a data analysis of interest rates, we findstronger evidence of cointegration if the tapered NBLSE is used for the cointegration parameter than ifOLS 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 (October 2002). Statistics Working Papers Series, Vol. , pp. -, 2002. Available at SSRN: https://ssrn.com/abstract=1290216

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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