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Autocorrelation robust inference using the Daniell kernel with fixed bandwidth

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  • Javier Hualde
  • Fabrizio Iacone

Abstract

We consider alternative asymptotics for frequency domain estimates of the long run variance, in which the bandwidth is kept fixed. For a weakly dependent process, this does not yield a consistent estimateof the long run variance, but the standardized mean has t limit distribution, which, for any given bandwidth, appears to be more precise than the traditional Gaussian limit. In presence of fractionally integrated data, the limit distribution of the estimate is not standard, and we derive critical values for the standardized mean for various bandwidths. Again, we find that this asymptotic result provides a better approximation than other proposals like the Memory Autocorrelation Consistent (MAC) estimate. In multivariate set up, fixed bandwidth asymptotics may be also used to provide a characterization to the limit distribution of estimates of cointegrating parameter which differs substantially from the conventional Narrow Band asymptotics.

Suggested Citation

  • Javier Hualde & Fabrizio Iacone, 2015. "Autocorrelation robust inference using the Daniell kernel with fixed bandwidth," Discussion Papers 15/14, Department of Economics, University of York.
    • Handle: RePEc:yor:yorken:15/14
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    References listed on IDEAS

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    Cited by:

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    2. Laura Coroneo & Fabrizio Iacone, 2015. "Comparing predictive accuracy in small samples," Discussion Papers 15/15, Department of Economics, University of York.

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    More about this item

    Keywords

    long run variance estimation; long memory; large-m and fixed-masymptotic theory;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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