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Improved nonparametric confidence intervals in time series regressions

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  • Joseph P. Romano
  • Michael Wolf

Abstract

Con dence intervals in econometric time series regressions suffer from notorious coverage problems. This is especially true when the dependence in the data is noticeable and sample sizes are small to moderate, as is often the case in empirical studies. This paper suggests using the studentized block bootstrap and discusses practical issues, such as the choice of the block size. A particular data-dependent method is proposed to automate the method. As a side note, it is pointed out that symmetric confidence intervals are preferred over equal-tailed ones, since they exhibit improved coverage accuracy. The improvements in small sample performance are supported by a simulation study.

Suggested Citation

  • Joseph P. Romano & Michael Wolf, 2002. "Improved nonparametric confidence intervals in time series regressions," Economics Working Papers 635, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:635
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    Cited by:

    1. Jose Dias Curto & Jose Castro Pinto, 2009. "The coefficient of variation asymptotic distribution in the case of non-iid random variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(1), pages 21-32.
    2. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
    3. Olivier Ledoit & Michael Wolf, 2018. "Robust performance hypothesis testing with smooth functions of population moments," ECON - Working Papers 305, Department of Economics - University of Zurich.
    4. Yeonwoo Rho & Xiaofeng Shao, 2015. "Inference for Time Series Regression Models With Weakly Dependent and Heteroscedastic Errors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 444-457, July.
    5. Massimo Guidolin & Erwin Hansen & Martín Lozano-Banda, 2018. "Portfolio performance of linear SDF models: an out-of-sample assessment," Quantitative Finance, Taylor & Francis Journals, vol. 18(8), pages 1425-1436, August.
    6. Joseph Romano & Azeem Shaikh & Michael Wolf, 2008. "Control of the false discovery rate under dependence using the bootstrap and subsampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 417-442, November.
    7. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    8. Romano, Joseph P. & Shaikh, Azeem M. & Wolf, Michael, 2008. "Formalized Data Snooping Based On Generalized Error Rates," Econometric Theory, Cambridge University Press, vol. 24(2), pages 404-447, April.
    9. Bakshi, Gurdip & Panayotov, George & Skoulakis, Georgios, 2011. "Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios," Journal of Financial Economics, Elsevier, vol. 100(3), pages 475-495, June.
    10. Auer, Benjamin R. & Schuhmacher, Frank, 2013. "Performance hypothesis testing with the Sharpe ratio: The case of hedge funds," Finance Research Letters, Elsevier, vol. 10(4), pages 196-208.

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    More about this item

    Keywords

    Bootstrap; confidence intervals; studentization; time series regressions; prewhitening;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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