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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.

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Bibliographic Info

Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 635.

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Date of creation: Jul 2002
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Handle: RePEc:upf:upfgen:635

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Web page: http://www.econ.upf.edu/

Related research

Keywords: Bootstrap; confidence intervals; studentization; time series regressions; prewhitening;

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  1. Horowitz, Joel L., 2001. "The bootstrap and hypothesis tests in econometrics," Journal of Econometrics, Elsevier, Elsevier, vol. 100(1), pages 37-40, January.
  2. Sílvia Gonçalves & Halbert White, 2001. "The Bootstrap of the Mean for Dependent Heterogeneous Arrays," CIRANO Working Papers, CIRANO 2001s-19, CIRANO.
  3. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  4. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, Econometric Society, vol. 64(4), pages 891-916, July.
  5. repec:att:wimass:9220 is not listed on IDEAS
  6. Politis, D. N. & Romano, Joseph P. & Wolf, Michael, 1997. "Subsampling for heteroskedastic time series," Journal of Econometrics, Elsevier, Elsevier, vol. 81(2), pages 281-317, December.
  7. Donald W.K. Andrews, 1999. "Higher-Order Improvements of a Computationally Attractive-Step Bootstrap for Extremum Estimators," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1230, Cowles Foundation for Research in Economics, Yale University.
  8. Donald W.K. Andrews & Christopher J. Monahan, 1990. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 942, Cowles Foundation for Research in Economics, Yale University.
  9. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, Econometric Society, vol. 57(2), pages 357-84, March.
  10. Davidson, Russell & MacKinnon, James G., 1981. "Efficient estimation of tail-area probabilities in sampling experiments," Economics Letters, Elsevier, Elsevier, vol. 8(1), pages 73-77.
  11. Kenneth D. West & Whitney K. Newey, 1995. "Automatic Lag Selection in Covariance Matrix Estimation," NBER Technical Working Papers 0144, National Bureau of Economic Research, Inc.
  12. Fitzenberger, Bernd, 1998. "The moving blocks bootstrap and robust inference for linear least squares and quantile regressions," Journal of Econometrics, Elsevier, Elsevier, vol. 82(2), pages 235-287, February.
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Cited by:
  1. Joseph P. Romano & Michael Wolf, 2003. "Stepwise multiple testing as formalized data snooping," Economics Working Papers, Department of Economics and Business, Universitat Pompeu Fabra 712, Department of Economics and Business, Universitat Pompeu Fabra.
  2. 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, Elsevier, vol. 100(3), pages 475-495, June.
  3. Joseph P & Romano & Azeem M. Shaikh & Michael Wolf, 2005. "Formalized Data Snooping Based on Generalized Error Rates," IEW - Working Papers 259, Institute for Empirical Research in Economics - University of Zurich.
  4. Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, Elsevier, vol. 15(5), pages 850-859, December.
  5. 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, Springer, vol. 17(3), pages 417-442, November.
  6. Auer, Benjamin R. & Schuhmacher, Frank, 2013. "Performance hypothesis testing with the Sharpe ratio: The case of hedge funds," Finance Research Letters, Elsevier, Elsevier, vol. 10(4), pages 196-208.

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