Arbeitspapier
A primer on Bayesian distributional regression
Bayesian methods have become increasingly popular in the past two decades. With the constant rise of computational power even very complex models can be estimated on virtually any modern computer. Moreover, interest has shifted from conditional mean models to probabilistic distributional models capturing location, scale, shape and other aspects of a response distribution, where covariate effects can have flexible forms, e.g., linear, nonlinear, spatial or random effects. This tutorial paper discusses how to select models in the Bayesian distributional regression setting, how to monitor convergence of the Markov chains, evaluate relevance of effects using simultaneous credible intervals and how to use simulation-based inference also for quantities derived from the original model parameterisation. We exemplify the work flow using daily weather data on (i) temperatures on Germany's highest mountain and (ii) extreme values of precipitation all over Germany.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Papers in Economics and Statistics ; No. 2017-13
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Semiparametric and Nonparametric Methods: General
Optimization Techniques; Programming Models; Dynamic Analysis
Computational Techniques; Simulation Modeling
- Thema
-
Distributional regression
generalized additive models for location scale and shape
Markov chain Monte Carlo simulations
semiparametric regression
tutorial
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Kneib, Thomas
Umlauf, Nikolaus
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
- (wo)
-
Innsbruck
- (wann)
-
2017
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Kneib, Thomas
- Umlauf, Nikolaus
- University of Innsbruck, Research Platform Empirical and Experimental Economics (eeecon)
Entstanden
- 2017
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