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SUMMARY:Andre LUCAS (VU Amsterdam) "Consistency\, distributional convergence\, and optimality of score-driven filters
DESCRIPTION:[vc_row][vc_column][vc_column_text]Finance & Financial Econometrics : \nTime: 10.00 am\nDate: 04th of April 2024\nRoom 3001 \nAndre LUCAS (VU Amsterdam) “Consistency\, distributional convergence\, and optimality of score-driven filters” \nAbstract : We study the in-fill asymptotics of score-driven time series models. For general forms of model mis-specification\, we show that score-driven filters are consistent for the Kullback-Leibler (KL) optimal time-varying parameter path\, which minimizes the pointwise KL divergence between the statistical model and the unknown dynamic data generating process. This directly implies that for a correctly specified predictive conditional density\, score-driven filters consistently estimate the time-varying parameter path even if the model is mis-specified in other respects. We also obtain distributional convergence results for the filtering errors and derive the filter that minimizes  the asymptotic filter error variance. Score-driven filters turn out to be optimal under correct specification of the predictive conditional density. The results considerably generalize earlier\nfindings on the continuous-time consistency of volatility filters under mis-specification: they apply to biased filters\, use weaker assumptions\, allow for more general forms of mis-specification\, and consider general time-varying parameters in non-linear time series models beyond the volatility case. Several examples are used to illustrate the theory\, including time-varying tail shape models\, dynamic copulas\, and time-varying regression models. \nJoint work: E. Beutner and Y. Lin \nOrganizers:\n\nJean-Michel ZAKOIAN (CREST) \nSponsors:\nCREST[/vc_column_text][/vc_column][/vc_row]\n
URL:https://crest.science/event/andre-lucas-vu-amsterdam-t-b-a/
CATEGORIES:Finance-Insurance,Financial Econometrics,Seminars
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