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Bias-correction in vector autoregressive models: A simulation study

Author

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  • Tom Engsted

    () (Aarhus University and CREATES)

  • Thomas Q. Pedersen

    () (Aarhus University and CREATES)

Abstract

We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also investigate the properties of an iterative scheme when applying the analytical bias formula, and we ?find that this can imply slightly better fi?nite-sample properties for very small sample sizes while for larger sample sizes there is no gain by iterating. Finally, we also pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias.

Suggested Citation

  • Tom Engsted & Thomas Q. Pedersen, 2011. "Bias-correction in vector autoregressive models: A simulation study," CREATES Research Papers 2011-18, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2011-18
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    References listed on IDEAS

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    1. Kilian, Lutz, 1999. "Exchange Rates and Monetary Fundamentals: What Do We Learn from Long-Horizon Regressions?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 491-510, Sept.-Oct.
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    Citations

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    Cited by:

    1. Juraj Hucek & Alexander Karsay & Marian Vavra, 2015. "Short-term Forecasting of Real GDP Using Monthly Data," Working and Discussion Papers OP 1/2015, Research Department, National Bank of Slovakia.
    2. Engsted, Tom & Pedersen, Thomas Q., 2012. "Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 241-253.
    3. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    4. Engsted, Tom & Hviid, Simon J. & Pedersen, Thomas Q., 2016. "Explosive bubbles in house prices? Evidence from the OECD countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 14-25.
    5. Giuseppe Cavaliere & A. M. Robert Taylor & Carsten Trenkler, 2015. "Bootstrap Co-integration Rank Testing: The Effect of Bias-Correcting Parameter Estimates," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(5), pages 740-759, October.
    6. Stefan Bruder, 2014. "Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions," ECON - Working Papers 181, Department of Economics - University of Zurich, revised Dec 2015.
    7. Stefan Bruder & Michael Wolf, 2017. "Balanced bootstrap joint confidence bands for structural impulse response functions," ECON - Working Papers 246, Department of Economics - University of Zurich, revised Jan 2018.
    8. Hendrik Kaufmannz & Robinson Kruse, 2013. "Bias-corrected estimation in potentially mildly explosive autoregressive models," CREATES Research Papers 2013-10, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Bias reduction; VAR model; analytical bias formula; bootstrap; iteration; Yule-Walker; non-stationary system; skewed and fat-tailed data.;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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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