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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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    1. Boswijk, H. Peter & Cavaliere, Giuseppe & Rahbek, Anders & Taylor, A.M. Robert, 2016. "Inference on co-integration parameters in heteroskedastic vector autoregressions," Journal of Econometrics, Elsevier, vol. 192(1), pages 64-85.
    2. Cavaliere, Giuseppe & Georgiev, Iliyan & Taylor, A.M.Robert, 2018. "Unit Root Inference For Non-Stationary Linear Processes Driven By Infinite Variance Innovations," Econometric Theory, Cambridge University Press, vol. 34(2), pages 302-348, April.
    3. Bunzel, Helle & Vogelsang, Timothy J., 2005. "Powerful Trend Function Tests That Are Robust to Strong Serial Correlation, With an Application to the Prebisch-Singer Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 381-394, October.
    4. Cavaliere, Giuseppe & Taylor, A.M. Robert, 2007. "Testing for unit roots in time series models with non-stationary volatility," Journal of Econometrics, Elsevier, vol. 140(2), pages 919-947, October.
    5. Chung, Heetaik & Park, Joon Y., 2007. "Nonstationary nonlinear heteroskedasticity in regression," Journal of Econometrics, Elsevier, vol. 137(1), pages 230-259, March.
    6. Pierre Perron & Eduardo Zorita & Timothy J. Vogelsang & Nasreen Nawaz, 2017. "Estimation and Inference of Linear Trend Slope Ratios With an Application to Global Temperature Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 640-667, September.
    7. Mohitosh Kejriwal & Claude Lopez, 2013. "Unit Roots, Level Shifts, and Trend Breaks in Per Capita Output: A Robust Evaluation," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 892-927, November.
    8. David I. Harvey & Neil M. Kellard & Jakob B. Madsen & Mark E. Wohar, 2010. "The Prebisch-Singer Hypothesis: Four Centuries of Evidence," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 367-377, May.
    9. Peter C. B. Phillips & Xiaohu Wang & Yonghui Zhang, 2019. "HAR Testing for Spurious Regression in Trend," Econometrics, MDPI, vol. 7(4), pages 1-28, December.
    10. Perron, Pierre & Yabu, Tomoyoshi, 2009. "Estimating deterministic trends with an integrated or stationary noise component," Journal of Econometrics, Elsevier, vol. 151(1), pages 56-69, July.
    11. Xu, Ke-Li, 2012. "Robustifying multivariate trend tests to nonstationary volatility," Journal of Econometrics, Elsevier, vol. 169(2), pages 147-154.
    12. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2007. "A simple, robust and powerful test of the trend hypothesis," Journal of Econometrics, Elsevier, vol. 141(2), pages 1302-1330, December.
    13. Pierre Perron & Eduardo Zorita & Francisco Estrada & Pierre Perron, 2017. "Extracting and Analyzing the Warming Trend in Global and Hemispheric Temperatures," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(5), pages 711-732, September.
    14. Durlauf, Steven N & Phillips, Peter C B, 1988. "Trends versus Random Walks in Time Series Analysis," Econometrica, Econometric Society, vol. 56(6), pages 1333-1354, November.
    15. Ibragimov, Rustam & Müller, Ulrich K., 2010. "t-Statistic Based Correlation and Heterogeneity Robust Inference," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 453-468.
    16. Timothy J. Vogelsang, 1998. "Trend Function Hypothesis Testing in the Presence of Serial Correlation," Econometrica, Econometric Society, vol. 66(1), pages 123-148, January.
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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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