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https://hdl.handle.net/10419/37181
Kompletter Metadatensatz
DublinCore-Feld | Wert | Sprache |
---|---|---|
dc.contributor.author | Scharnagl, Michael | en |
dc.contributor.author | Schumacher, Christian | en |
dc.date.accessioned | 2010-08-11T09:02:20Z | - |
dc.date.available | 2010-08-11T09:02:20Z | - |
dc.date.issued | 2010 | - |
dc.identifier.uri | http://hdl.handle.net/10419/37181 | - |
dc.description.abstract | This paper evaluates a novel sampling algorithm, called shotgun stochastic search (S³), for Bayesian model averaging in the context of finding predictors for inflation when the set of potential predictors is large. This is a relevant case in the forecasting literature, where often hundreds of predictors are compared with autoregressive distributed lag models for inflation. With such a large model space, standard Bayesian approaches like MCMC model composition (MC³) tend to converge slowly. On the other hand, S³ systematically searches in the neighborhood of good models and concentrates on regions of high posterior probability in the model space. We carry out a Monte Carlo simulations to compare the computational efficiency of S³ to MC³, based on standard data generating processes from the literature. When many potential predictors are available, S³ outperforms MC³. In an empirical exercise, we apply the two algorithms to find predictors for US inflation from a set of about one hundred indicators and their lags. S³ absorbs posterior mass much quicker than MC³ and makes Bayesian estimation of the standard inflation equations with many predictors computationally feasible. | en |
dc.language.iso | eng | en |
dc.publisher | |aVerein für Socialpolitik |cFrankfurt a. M. | en |
dc.relation.ispartofseries | |aBeiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Forecasting Methods |xA11-V2 | en |
dc.subject.jel | E31 | en |
dc.subject.jel | C52 | en |
dc.subject.jel | C11 | en |
dc.subject.ddc | 330 | en |
dc.title | Finding good predictors for inflation by shotgun stochastic search | - |
dc.type | Conference Paper | en |
dc.identifier.ppn | 655517898 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
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