Scientific article

Saddlepoint approximations for short and long memory time series: a frequency domain approach

Published inJournal of Econometrics, vol. 213, no. 2, p. 578-592
Publication date2019

Saddlepoint techniques provide numerically accurate, small sample approximations to the distribution of estimators and test statistics. Except for a few simple models, these approximations are not available in the framework of stationary time series. We contribute to fill this gap. Under short or long range serial dependence, for Gaussian and non Gaussian processes, we show how to derive and implement saddlepoint approximations for two relevant classes of frequency domain statistics: ratio statistics and Whittle's estimator. We compare our new approximations to the ones obtained by the standard asymptotic theory and by two widely-applied bootstrap methods. The numerical exercises for Whittle's estimator show that our approximations yield accuracy's improvements, while preserving analytical tractability. A real data example concludes the paper.

  • Periodogram
  • Tilted edgeworth expansion
  • Macroeconomic time series
  • Relative error
  • Whittle's estimator
Citation (ISO format)
LA VECCHIA, Davide, RONCHETTI, Elvezio. Saddlepoint approximations for short and long memory time series: a frequency domain approach. In: Journal of Econometrics, 2019, vol. 213, n° 2, p. 578–592. doi: 10.1016/j.jeconom.2018.10.009
Main files (1)
Article (Published version)
ISSN of the journal0304-4076

Technical informations

Creation10/28/2019 11:07:00 AM
First validation10/28/2019 11:07:00 AM
Update time03/15/2023 6:17:54 PM
Status update03/15/2023 6:17:54 PM
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