Estimation of Spatially Correlated Random Scaling Factors based on Markov Random Field Priors
Alexander Razen (),
Stefan Lang () and
Judith Santer ()
Working Papers from Faculty of Economics and Statistics, University of Innsbruck
Multiplicative random effects allow for cluster-specific scaling of covariate effects. In many applications with spatial clustering, however, the random effects additionally show some geographical pattern, which usually can not sufficiently be captured with existing estimation techniques. Relying on Markov random fields, we present a fully Bayesian inference procedure for spatially correlated scaling factors. The estimation is based on highly efficient Markov Chain Monte Carlo (MCMC) algorithms and is smoothly incorporated into the framework of distributional regression. We run a comprehensive simulation study for different response distributions to examine the statistical properties of our approach. We also compare our results to those of a general estimation procedure for independent random scaling factors. Furthermore, we apply the method to German real estate data and show that exploiting the spatial correlation of the scaling factors further improves the performance of the model.
Keywords: distributional regression; iteratively weighted least squares proposals; MCMC; multiplicative random effects; spatial smoothing; 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 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: http://EconPapers.repec.org/RePEc:inn:wpaper:2016-33
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 ().