Scientific article

R-estimation in semiparametric dynamic location-scale models

Published inJournal of econometrics, vol. 196, no. 2, p. 233-247
Publication date2017

We propose rank-based estimation (R-estimators) as an alternative to Gaussian quasi-likelihood and standard semiparametric estimation in time series models, where conditional location and/or scale depend on a Euclidean parameter of interest, while the unspecified innovation density is a nuisance. We show how to construct R-estimators achieving semiparametric efficiency at some predetermined reference density while preserving root-n consistency and asymptotic normality irrespective of the actual density. Contrary to the standard semiparametric estimators, our R-estimators neither require tangent space calculations nor innovation density estimation. Numerical examples illustrate their good performances on simulated and real data.

  • Conditional heteroskedasticity
  • Distribution-freeness
  • Discretely observed Lévy process
  • Forecasting
  • R-estimation
  • Realized volatility
  • Skew-t family
Citation (ISO format)
HALLIN, Marc, LA VECCHIA, Davide. R-estimation in semiparametric dynamic location-scale models. In: Journal of econometrics, 2017, vol. 196, n° 2, p. 233–247. doi: 10.1016/j.jeconom.2016.08.002
Main files (1)
Article (Published version)
ISSN of the journal0304-4076

Technical informations

Creation02/08/2017 3:37:00 PM
First validation02/08/2017 3:37:00 PM
Update time03/15/2023 1:22:50 AM
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