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R-estimation in semiparametric dynamic location-scale models

Listed author(s):
  • Hallin, Marc
  • La Vecchia, Davide

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.

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File URL: http://www.sciencedirect.com/science/article/pii/S0304407616301725
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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 196 (2017)
Issue (Month): 2 ()
Pages: 233-247

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Handle: RePEc:eee:econom:v:196:y:2017:i:2:p:233-247
DOI: 10.1016/j.jeconom.2016.08.002
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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