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Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance

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العنوان: Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance
المؤلفون: Casarin, R., Grassi, S., Ravazzolo, F., van Dijk, H.K.
المساهمون: Econometrics and Operations Research
المصدر: Casarin, R, Grassi, S, Ravazzolo, F & van Dijk, H K 2015 ' Dynamic Predictive Density Combinations for Large Data Sets in Economics and Finance ' TI Discussion Paper, no. 15-084/III, Tinbergen Institute, Amsterdam . < http://papers.tinbergen.nl/15084.pdf >
بيانات النشر: Tinbergen Institute, 2015.
سنة النشر: 2015
مصطلحات موضوعية: nonlinear state space, E37, jel:C53, Bayesian inference, jel:C11, GPU computing, compositional factor models, jel:E37, large set of predictive densities, jel:C15, Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212 [VDP], JEL: C11, JEL: C53, ddc:330, C15, C53, JEL: E37, C11, JEL: C15, density combination
الوصف: A Bayesian nonparametric predictive model is introduced to construct time-varying weighted combinations of a large set of predictive densities. A clustering mechanism allocates these densities into a smaller number of mutually exclusive subsets. Using properties of the Aitchinson's geometry of the simplex, combination weights are defined with a probabilistic interpretation. The classpreserving property of the logistic-normal distribution is used to define a compositional dynamic factor model for the weight dynamics with latent factors defined on a reduced dimension simplex. Groups of predictive models with combination weights are updated with parallel clustering and sequential Monte Carlo filters. The procedure is applied to predict Standard & Poor's 500 index using more than 7000 predictive densities based on US individual stocks and finds substantial forecast and economic gains. Similar forecast gains are obtained in point and density forecasting of US real GDP, Inflation, Treasury Bill yield and employment using a large data set.
اللغة: English
URL الوصول: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ea5cd81b7a3094cd1e6c2bce2e7b3e75
https://research.vu.nl/en/publications/a017f43a-9612-43cb-9c51-4b1d324360bb
Rights: OPEN
رقم الانضمام: edsair.dedup.wf.001..ea5cd81b7a3094cd1e6c2bce2e7b3e75
قاعدة البيانات: OpenAIRE
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