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Modeling Parametric Evolution in a Random Utility Framework
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jin Gyo | - |
dc.contributor.author | Menzefricke, Ulrich | - |
dc.contributor.author | M. Feinberg, Fred | - |
dc.date.accessioned | 2010-06-28T23:33:06Z | - |
dc.date.available | 2010-06-28T23:33:06Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Journal of Business & Economic Statistics, 23, 282-294 | en |
dc.identifier.issn | 0735-0015 | - |
dc.identifier.uri | http://www.jstor.org/action/showPublication?journalCode=jbusieconstat&cookieSet=1 | - |
dc.identifier.uri | https://hdl.handle.net/10371/67958 | - |
dc.description.abstract | 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 captures 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 corection for potential aggregation biases. We illustrate the method using choices for a common supermarket good, where we find strong support for parameter dynamics. | en |
dc.language.iso | en | en |
dc.publisher | American Statistical Association | en |
dc.subject | Bayesian model | en |
dc.subject | Choice model | en |
dc.subject | Dynamic model | en |
dc.subject | Logit model | en |
dc.subject | Scanner panel data | en |
dc.subject | Varying-parameter model | en |
dc.subject | Vector autoregressive process | en |
dc.title | Modeling Parametric Evolution in a Random Utility Framework | en |
dc.type | Article | en |
dc.contributor.AlternativeAuthor | 김진교 | - |
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