Modeling Parametric Evolution in a Random Utility Framework

Autor:	Ulrich Menzefricke, Fred M. Feinberg, Jin Gyo Kim
Rok vydání:	2005
Předmět:	Statistics and Probability Economics and Econometrics Bayes estimator Inference Bayesian inference Logistic regression Autoregressive model Covariate Econometrics Economics Statistics Probability and Uncertainty Categorical variable Social Sciences (miscellaneous) Parametric statistics
Zdroj:	Journal of Business & Economic Statistics. 23:282-294
ISSN:	1537-2707 0735-0015
DOI:	10.1198/073500104000000550
Popis:	Random utility models have become standard econometric tools, allowing parameter inference for individual-level categorical choice data. Such models typically presume that changes in observed choices over time can be attributed to changes in either covariates or unobservables. We study how choice dynamics can be captured more faithfully by also directly modeling temporal changes in parameters, using a vector autoregressive process and Bayesian estimation. This approach offers a number of advantages for theorists and practitioners, including improved forecasts, prediction of long-run parameter levels, and correction for potential aggregation biases. We illustrate the method using choices for a common supermarket good, where we find strong support for parameter dynamics.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::993c987c831f06bc56ec47bade1bcbfb https://doi.org/10.1198/073500104000000550 Zobrazit plný text záznamu