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A bootstrap algorithm for testing cointegration rank in VAR models in the presence of stationary variables

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  • Swensen, Anders Rygh

Abstract

In this paper, a bootstrap algorithm for a reduced rank vector autoregressive (VAR) model which also includes stationary regressors, is analyzed. It is shown that the bootstrap distribution for estimating the rank converges to the distribution derived from the usual asymptotic framework. Because the asymptotic distribution will typically depend on unknown parameters, bootstrap distributions are of considerable interest in this context. The result of an application and some Monte Carlo experiments are also presented.

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  • Swensen, Anders Rygh, 2011. "A bootstrap algorithm for testing cointegration rank in VAR models in the presence of stationary variables," Journal of Econometrics, Elsevier, vol. 165(2), pages 152-162.
    • Handle: RePEc:eee:econom:v:165:y:2011:i:2:p:152-162
    DOI: 10.1016/j.jeconom.2011.07.002
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    Cited by:

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    2. Sven Schreiber, 2019. "On (bootstrapped) cointegration tests in partial systems," IMK Working Paper 199-2019, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    3. Jentsch, Carsten & Paparoditis, Efstathios & Politis, Dimitris N., 2014. "Block Bootstrap Theory for Multivariate Integrated and Cointegrated Processes," Working Papers 14-18, University of Mannheim, Department of Economics.
    4. Rushdi, Mustabshira & Kim, Jae H. & Silvapulle, Param, 2012. "ARDL bounds tests and robust inference for the long run relationship between real stock returns and inflation in Australia," Economic Modelling, Elsevier, vol. 29(3), pages 535-543.
    5. Schreiber, Sven, 2018. "Are bootstrapped cointegration test findings unreliable?," Discussion Papers 2018/8, Free University Berlin, School of Business & Economics.

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    More about this item

    Keywords

    VAR models; Reduced rank; Stationary regressors; Bootstrap;
    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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