R-estimation in semiparametric dynamic location-scale models
Marc Hallin () and
Davide La Vecchia
Journal of Econometrics, 2017, vol. 196, issue 2, 233-247
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.
Keywords: Conditional heteroskedasticity; Distribution-freeness; Discretely observed Lévy processes; Forecasting; R-estimation; Realized volatility; Skew-t family (search for similar items in EconPapers)
JEL-codes: C13 C14 C22 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:196:y:2017:i:2:p:233-247
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