Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/156184
Authors: 
Razen, Alexander
Lang, Stefan
Year of Publication: 
2016
Series/Report no.: 
Working Papers in Economics and Statistics 2016-30
Abstract: 
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 co- variates 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 + ac). 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 with- out random scaling factors.
Subjects: 
iteratively weighted least squares proposals
MCMC
multiplicative random effects
structured additive predictors
Document Type: 
Working Paper

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