S-Space College of Business Administration/Business School (경영대학/대학원) Dept. of Business Administration (경영학과) Journal Papers (저널논문_경영학과)
Modeling Parametric Evolution in a Random Utility Framework
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- Issue Date
- American Statistical Association
- Journal of Business & Economic Statistics, 23, 282-294
- Bayesian model ; Choice model ; Dynamic model ; Logit model ; Scanner panel data ; Varying-parameter model ; Vector autoregressive process
- 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.
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