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R-estimation in semiparametric dynamic location-scale models

Hallin, Marc
Published in Journal of Econometrics. 2017, vol. 196, no. 2, p. 233-247
Abstract 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 heteroskedasticityDistribution-freenessDiscretely observed Lévy processForecastingR-estimationRealized volatilitySkew-t family
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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. https://archive-ouverte.unige.ch/unige:91879

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Deposited on : 2017-02-15

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