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Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence

Author

Listed:
  • George Kapetanios

    (Queen Mary, University of London)

  • Zacharias Psaradakis

    (Birkbeck, University of London)

Abstract

This paper considers the problem of statistical inference in linear regression models whose stochastic regressors and errors may exhibit long-range dependence. A time-domain sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the generating mechanism of the errors. The asymptotic properties of the sieve-type GLS estimator are established. A Monte Carlo study examines the finite-sample properties of the method for testing regression hypotheses.

Suggested Citation

  • George Kapetanios & Zacharias Psaradakis, 2007. "Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence," Working Papers 587, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:587
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2007/items/wp587.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Autoregressive approximation; Generalized least squares; Linear regression; Long-range dependence; Spectral density;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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