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Regression in Long-Memory Time Series

  • Conference paper
Athens Conference on Applied Probability and Time Series Analysis

Part of the book series: Lecture Notes in Statistics ((LNS,volume 115))

Abstract

We consider the estimation of regression coefficients when the residuals form a stationary time series. Hannan proposed a method for doing this, via a form of Gaussian estimation, in the case when the time series has some parametric form such as ARMA, but we are interested in semiparametric cases incorporating long-memory dependence. After reviewing recent results by Robinson, on semiparametric estimation of long-memory dependence in the case where there are no covariates present, we propose an extension to the case where there are covariates. For this problem it appears that a direct extension of Robinson’s method leads to inefficient estimates of the regression parameters, and an alternative is proposed. Our mathematical arguments are heuristic, but rough derivations of the main results are outlined. As an example, we discuss some issues related to climatological time series.

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Author information

Authors and Affiliations

  1. Cambridge University and University of North, Carolina, USA

    Richard L. Smith

  2. University of North, Carolina, USA

    Fan-Ling Chen

Authors
  1. Richard L. Smith
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  2. Fan-Ling Chen
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Editor information

Editors and Affiliations

  1. Department of Economics, London School of Economics, Houghton Street, London, WC2A 2AE, Great Britain

    P. M. Robinson

  2. Department of Mathematics, University of California, San Diego, La Jolla, CA, 92093, USA

    Murray Rosenblatt

  3. Department of Statistics, Colorado State University, Ft. Collins, CO, 80523, USA

    Murray Rosenblatt

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© 1996 Springer-Verlag New York, Inc.

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Smith, R.L., Chen, FL. (1996). Regression in Long-Memory Time Series. In: Robinson, P.M., Rosenblatt, M. (eds) Athens Conference on Applied Probability and Time Series Analysis. Lecture Notes in Statistics, vol 115. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2412-9_28

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  • DOI: https://doi.org/10.1007/978-1-4612-2412-9_28

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94787-7

  • Online ISBN: 978-1-4612-2412-9

  • eBook Packages: Springer Book Archive

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