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On the Finite-Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series

Author

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  • Berkowitz, J.
  • Birgean, I.
  • Kilian, L.
Abstract
In recent years, there has been increasing interest in nonparametric bootstrap inference for economic time series. Nonparametric resampling techniques help protect against overly optimistic inference in time series models of unknown structure. They are particularly useful for evaluating the fit of dynamic economic models in terms of their spectra, impulse responses, and related statistics, because they do not require a correctly specified economic model. Notwithstanding the potential advantages of nonparametric bootstrap methods, their reliability in small samples is questionable. In this paper, we provide a benchmark for the relative accuracy of several nonparametric resampling algorithms based on ARMA representations of four macroeconomic time series. For each algorithm, we evaluate the effective coverage accuracy of impulse response and spectral density bootstrap confidence intervals for standard sample sizes. We find that the autoregressive sieve approach based on the encompassing model is most accurate. However, care must be exercised in selecting the lag order of the autoregressive approximation.

Suggested Citation

  • Berkowitz, J. & Birgean, I. & Kilian, L., 1999. "On the Finite-Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series," Papers 99-01, Michigan - Center for Research on Economic & Social Theory.
    • Handle: RePEc:fth:michet:99-01
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    Cited by:

    1. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    2. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    3. Bottazzi, G. & Sapio, S. & Secchi, A., 2005. "Some statistical investigations on the nature and dynamics of electricity prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 54-61.
    4. Lusompa, Amaze, 2019. "Local Projections, Autocorrelation, and Efficiency," MPRA Paper 99856, University Library of Munich, Germany, revised 11 Apr 2020.
    5. Ludger Linnemann & Roland Winkler, 2016. "Estimating nonlinear effects of fiscal policy using quantile regression methods," Oxford Economic Papers, Oxford University Press, vol. 68(4), pages 1120-1145.
    6. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    7. International Monetary Fund, 2007. "Oil Shocks and External Balances," IMF Working Papers 2007/110, International Monetary Fund.
    8. Ionel Birgean & Lutz Kilian, 2002. "Data-Driven Nonparametric Spectral Density Estimators For Economic Time Series: A Monte Carlo Study," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 449-476.
    9. Kilian, Lutz & Rebucci, Alessandro & Spatafora, Nikola, 2009. "Oil shocks and external balances," Journal of International Economics, Elsevier, vol. 77(2), pages 181-194, April.
    10. Pierluigi Balduzzi & Emanuele Brancati & Marco Brianti & Fabio Schiantarelli, 2019. "Populism, Political Risk and the Economy: Lessons from Italy," Boston College Working Papers in Economics 989, Boston College Department of Economics, revised 28 Apr 2020.
    11. J. David López-Salido & Francesca Loria, 2020. "Inflation at Risk," Finance and Economics Discussion Series 2020-013, Board of Governors of the Federal Reserve System (U.S.).
    12. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    13. Brianti, Marco & Cormun, Vito, 2023. "Expectation-Driven Boom-Bust Cycles," Working Papers 2023-4, University of Alberta, Department of Economics.
    14. Lutz Kilian & Bruce Hicks, 2013. "Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003–2008?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 385-394, August.
    15. Yao Axel Ehouman, 2020. "Do oil-market shocks drive global liquidity?," EconomiX Working Papers 2020-33, University of Paris Nanterre, EconomiX.
    16. López-Salido, J David & Loria, Francesca, 2019. "Inflation at Risk," CEPR Discussion Papers 14074, C.E.P.R. Discussion Papers.

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    More about this item

    Keywords

    TIME SERIES ; ECONOMIC MODELS;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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