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On robust testing for trend

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  • Skrobotov, Anton

Abstract

This paper provides a simple approach for robust testing for the trend function in the time series under uncertainty over the order of integration of the error term. The proposed approach relies on the asymptotic normality of the trend coefficient estimator and utilizes t-statistic approach of Ibragimov and Müller (2010) based on splitting the sample. The Monte-Carlo results demonstrate that the approach has the correct finite sample size and favorable finite sample power properties for all data generating processes considered. The proposed approach is robust to very general assumptions on the error term including various forms of non-stationary volatility and heavy tails

Suggested Citation

  • Skrobotov, Anton, 2022. "On robust testing for trend," Economics Letters, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:ecolet:v:212:y:2022:i:c:s0165176522000040
    DOI: 10.1016/j.econlet.2022.110276
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    References listed on IDEAS

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    More about this item

    Keywords

    Robust inference; Linear trend; Asymptotic normality; Heterogeneous errors; Nonstationarity;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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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