English, Article, Journal or magazine article edition: Improved Nonparametric Confidence Intervals in Time Series Regressions Joseph P. Romano; Michael Wolf

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Bookmark: http://trove.nla.gov.au/version/154438
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Improved Nonparametric Confidence Intervals in Time Series Regressions
Author
  • Joseph P. Romano
  • Michael Wolf
Physical Description
  • preprint
Notes
  • Confidence intervals in 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 proposes a method that combines prewhitening and the studentized bootstrap. While both prewhitening and the studentized bootstrap each provides improvement over standard normal theory intervals, one can achieve a further improvement by conjoining them in an appropriate way. As a side note, it is stressed that symmetric confidence intervals equal-tailed ones, since they exhibit improved coverage accuracy. We propose concrete ways to deal with the issues of block size, choice of kernel, and choice of bandwidth. The improvements in small sample performance are supported by a simulation study.
  • RePEc:cte:wsrepe:ws010201
  • Confidence intervals in econometric time series regressions suer 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.
  • RePEc:zur:iewwpx:273
  • Condence 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.
  • Bootstrap, confidence intervals, studentization, time series regressions, prewhitening
  • RePEc:upf:upfgen:635
Language
  • English
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