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Uniform Inference for Cointegrated Vector Autoregressive Processes

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  • Christian Holberg
  • Susanne Ditlevsen

Abstract

Uniformly valid inference for cointegrated vector autoregressive processes has so far proven difficult due to certain discontinuities arising in the asymptotic distribution of the least squares estimator. We extend asymptotic results from the univariate case to multiple dimensions and show how inference can be based on these results. Furthermore, we show that lag augmentation and a recent instrumental variable procedure can also yield uniformly valid tests and confidence regions. We verify the theoretical findings and investigate finite sample properties in simulation experiments for two specific examples.

Suggested Citation

  • Christian Holberg & Susanne Ditlevsen, 2023. "Uniform Inference for Cointegrated Vector Autoregressive Processes," Papers 2306.03632, arXiv.org, revised Dec 2023.
    • Handle: RePEc:arx:papers:2306.03632
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    References listed on IDEAS

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    1. Phillips, Peter C.B. & Magdalinos, Tassos, 2007. "Limit theory for moderate deviations from a unit root," Journal of Econometrics, Elsevier, vol. 136(1), pages 115-130, January.
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    Cited by:

    1. Christis Katsouris, 2024. "Robust Estimation in Network Vector Autoregression with Nonstationary Regressors," Papers 2401.04050, arXiv.org.
    2. Christis Katsouris, 2023. "Estimating Conditional Value-at-Risk with Nonstationary Quantile Predictive Regression Models," Papers 2311.08218, arXiv.org, revised Apr 2024.

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