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Convergence To Stochastic Power Integrals For Dependent Heterogeneous Processes

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  • Sandberg, Rickard

Abstract

Building on work of Hansen (1992, Econometric Theory 8, 489–501), we show weak convergence for power transformations of integrated processes, with possibly serially correlated and heterogeneously distributed increments, to stochastic power integrals. The theory is applicable when testing the unit root or cointegration hypothesis in nonlinear systems by regression-based test statistics.

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  • Sandberg, Rickard, 2009. "Convergence To Stochastic Power Integrals For Dependent Heterogeneous Processes," Econometric Theory, Cambridge University Press, vol. 25(3), pages 739-747, June.
    • Handle: RePEc:cup:etheor:v:25:y:2009:i:03:p:739-747_09
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    Cited by:

    1. Rickard Sandberg, 2018. "Unit Root Testing in Multiple Smooth Break Models with Nonlinear Dynamics," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 942-952, November.
    2. Rickard Sandberg, 2015. "M-estimator based unit root tests in the ESTAR framework," Statistical Papers, Springer, vol. 56(4), pages 1115-1135, November.
    3. Robinson Kruse, 2011. "A new unit root test against ESTAR based on a class of modified statistics," Statistical Papers, Springer, vol. 52(1), pages 71-85, February.
    4. Rickard Sandberg, 2017. "Sample Moments and Weak Convergence to Multivariate Stochastic Power Integrals," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(6), pages 1000-1009, November.
    5. Jean-Yves Pitarakis, 2017. "A Simple Approach for Diagnosing Instabilities in Predictive Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 851-874, October.
    6. Sandberg, Rickard, 2016. "Trends, unit roots, structural changes, and time-varying asymmetries in U.S. macroeconomic data: the Stock and Watson data re-examined," Economic Modelling, Elsevier, vol. 52(PB), pages 699-713.
    7. Jean-Yves Pitarakis, 2017. "A Simple Approach for Diagnosing Instabilities in Predictive Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 851-874, October.

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