Random Scaling Factors in Bayesian Distributional Regression Models with an Application to Real Estate Data
Alexander Razen () and
Stefan Lang ()
Working Papers from Faculty of Economics and Statistics, University of Innsbruck
Distributional structured additive regression provides a flexible framework for modeling each parameter of a potentially complex response distribution in dependence of covariates. Structured additive predictors allow for an additive decomposition of covariate effects with nonlinear effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. Within this framework, we present a simultaneous estimation approach for multiplicative random effects that allow for cluster-specific heterogeneity with respect to the scaling of a covariate's effect. More specifically, a possibly nonlinear function f(z) of a covariate z may be scaled by a multiplicative cluster-specific random effect (1+alpha). Inference is fully Bayesian and is based on highly efficient Markov Chain Monte Carlo (MCMC) algorithms. We investigate the statistical properties of our approach within extensive simulation experiments for different response distributions. Furthermore, we apply the methodology to German real estate data where we identify significant district-specific scaling factors. According to the deviance information criterion, the models incorporating these factors perform significantly better than standard models without random scaling factors.
Keywords: iteratively weighted least squares proposals; MCMC; multiplicative random effects; structured additive predictors (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:inn:wpaper:2016-30
Access Statistics for this paper
More papers in Working Papers from Faculty of Economics and Statistics, University of Innsbruck Contact information at EDIRC.
Series data maintained by Janette Walde ().