Incorporating Model Uncertainty in Market Response Models with Multiple Endogenous Variables by Bayesian Model Averaging

Jonathan Lee and Alex Lenkoski

A chapter in Econometrics - Recent Advances and Applications from IntechOpen

Abstract: We develop a method to incorporate model uncertainty by model averaging in generalized linear models subject to multiple endogeneity and instrumentation. Our approach builds on a Gibbs sampler for the instrumental variable framework that incorporates model uncertainty in both outcome and instrumentation stages. Direct evaluation of model probabilities is intractable in this setting. However, we show that by nesting model moves inside the Gibbs sampler, a model comparison can be performed via conditional Bayes factors, leading to straightforward calculations. This new Gibbs sampler is slightly more involved than the original algorithm and exhibits no evidence of mixing difficulties. We further show how the same principle may be employed to evaluate the validity of instrumentation choices. We conclude with an empirical marketing study: estimating opening box office by three endogenous regressors (prerelease advertising, opening screens, and production budget).

Keywords: multiple endogeneity; instrumental variables; Bayesian model averaging; conditional Bayes factors; box office forecasting (search for similar items in EconPapers)
JEL-codes: C01 (search for similar items in EconPapers)
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.intechopen.com/chapters/85212 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:287005

DOI: 10.5772/intechopen.108927

Access Statistics for this chapter

More chapters in Chapters from IntechOpen
Bibliographic data for series maintained by Slobodan Momcilovic (ai@intechopen.com).