Greb, Friederike Anne

[["dc.bibliographiccitation.firstpage","171"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Bayesian Analysis"],["dc.bibliographiccitation.lastpage","196"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Greb, Friederike"],["dc.contributor.author","Krivobokova, Tatyana"],["dc.contributor.author","Munk, Axel"],["dc.contributor.author","Cramon-Taubadel, Stephan von"],["dc.date.accessioned","2017-09-07T11:46:55Z"],["dc.date.available","2017-09-07T11:46:55Z"],["dc.date.issued","2014"],["dc.description.abstract","In this article we discuss estimation of generalized threshold regression models in settings when the threshold parameter lacks identifiability. In particular, if estimation of the regression coefficients is associated with high uncertainty and/or the difference between regimes is small, estimators of the threshold and, hence, of the whole model can be strongly affected. A new regularized Bayesian estimator for generalized threshold regression models is proposed. We derive conditions for superiority of the new estimator over the standard likelihood one in terms of mean squared error. Simulations confirm excellent finite sample properties of the suggested estimator, especially in the critical settings. The practical relevance of our approach is illustrated by two real-data examples already analyzed in the literature."],["dc.identifier.doi","10.1214/13-BA850"],["dc.identifier.gro","3142214"],["dc.identifier.isi","000332837600012"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/5799"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.eissn","1936-0975"],["dc.relation.issn","1931-6690"],["dc.title","Regularized Bayesian Estimation of Generalized Threshold Regression Models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","900"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","American Journal of Agricultural Economics"],["dc.bibliographiccitation.lastpage","916"],["dc.bibliographiccitation.volume","95"],["dc.contributor.author","Greb, Friederike"],["dc.contributor.author","Cramon-Taubadel, Stephan von"],["dc.contributor.author","Krivobokova, Tatyana"],["dc.contributor.author","Munk, Axel"],["dc.date.accessioned","2017-09-07T11:47:39Z"],["dc.date.available","2017-09-07T11:47:39Z"],["dc.date.issued","2013"],["dc.description.abstract","The threshold vector error correction model is a popular tool for the analysis of spatial price transmission. In the literature, the profile likelihood estimator is the preferred choice for estimating this model. Yet, in many settings this estimator performs poorly. In particular, if the true thresholds are such that one or more regimes contain only a small number of observations, if unknown model parameters are numerous, or if parameters differ little between regimes, the profile likelihood estimator displays large bias and variance. Such settings are likely when studying price transmission. We analyze the weaknesses of the profile likelihood approach and propose an alternative regularized Bayesian estimator, which was developed for simpler but related threshold models. Simulation results show that the regularized Bayesian estimator outperforms profile likelihood in the estimation of threshold vector error correction models. Two empirical applications demonstrate the relevance of this new estimator for spatial price transmission analysis."],["dc.identifier.doi","10.1093/ajae/aat006"],["dc.identifier.gro","3142332"],["dc.identifier.isi","000322022300006"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/7108"],["dc.language.iso","en"],["dc.notes.intern","WoS Import 2017-03-10"],["dc.notes.status","final"],["dc.notes.submitter","PUB_WoS_Import"],["dc.relation.issn","0002-9092"],["dc.title","The Estimation of Threshold Models in Price Transmission Analysis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original"],["dspace.entity.type","Publication"]]