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Semiparametrically Efficient R-Estimation for Dynamic Location-Scale Models

Hallin, Marc
Publication Brussels: ECARES European Center for Advanced Research in Economics and Statistics, Université Libre de Bruxelles and ORFE, Princeton University, 2014
Collection ECARES, Working Paper; 2014-45
Description 44 p.
Abstract We define rank-based estimators (R-estimators) for semiparametric time series models in whichthe conditional location and scale depend on a Euclidean parameter, while the innovation density isan infinite-dimensional nuisance. Applications include linear and nonlinear models, featuring eitherhomo- or heteroskedasticity (e.g. AR-ARCH and discretely observed diffusions with jumps). We showhow to construct easy-to-implement R-estimators, which achieve semiparametric efficiency at somepredetermined reference density while preserving root-n consistency, irrespective of the actual density.Numerical examples illustrate the good performances of the proposed estimators. An empirical analysisof the log-return and log-transformed two-scale realized volatility concludes the paper.
Keywords Conditional heteroskedasticityDistribution-freenessForecastingLévy processesOne-step R-Estimators
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LA VECCHIA, Davide, HALLIN, Marc. Semiparametrically Efficient R-Estimation for Dynamic Location-Scale Models. 2014 https://archive-ouverte.unige.ch/unige:75157

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Deposited on : 2015-09-13

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