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Filters for Short Nonstationary Sequences

Author

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  • Stephen Pollock

    (Queen Mary, University of London)

Abstract

This paper describes a methodology for implementing bidirectional frequency-selective filters in cases where the data sequence is short and nonstationary. A simple method is proposed for dealing with the start-up problem. The method has a firm theoretical basis and it is computationally efficient.

Suggested Citation

  • Stephen Pollock, 2000. "Filters for Short Nonstationary Sequences," Working Papers 423, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:423
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2000/items/wp423.pdf
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    Citations

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    Cited by:

    1. Stephen Pollock, 2001. "Signal Extraction, Maximum Likelihood Estimation and the Start-up Problem," Working Papers 433, Queen Mary University of London, School of Economics and Finance.
    2. Stephen Pollock, 2001. "Signal Extraction, Maximum Likelihood Estimation and the Start-up Problem," Working Papers 433, Queen Mary University of London, School of Economics and Finance.

    More about this item

    Keywords

    Signal extraction; Linear Filtering; Frequency-domain analysis; Trend estimation;
    All these keywords.

    JEL classification:

    • 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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