Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/286390 
Year of Publication: 
2024
Series/Report no.: 
Working Papers in Economics and Statistics No. 2024-01
Publisher: 
University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon), Innsbruck
Abstract: 
Gaussian Structured Additive Regression provides a flexible framework for additive decomposition of the expected value with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity, and complex interactions between covariates of different types. Within this framework, we present a simultaneous estimation approach for highly complex multiplicative interaction effects. In particular, a possibly nonlinear function f(z) of a covariate z may be scaled by a multiplicative effect of the form exp(˜η), where ˜η is another possibly structured additive predictor. Inference is fully Bayesian and based on highly efficient Markov Chain Monte Carlo (MCMC) algorithms. We investigate the statistical properties of our approach in extensive simulation experiments. Furthermore, we apply and illustrate the methodology to an analysis of asking prices for 200000 dwellings in Germany.
Subjects: 
IWLS proposals
MCMC
multiplicative interaction effects
structured additive predictor
Document Type: 
Working Paper

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