Browsing by Subject "Discrete choice models"

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    On Efficiency of Methods of Simulated Moments and Maximum Simulated Likelihood Estimation of Discrete Response Models
    (Center for Economic Research, Department of Economics, University of Minnesota, 1990-09) Lee, Lung-Fei
    This article has considered methods of simulated moments for estimation of discrete response models. We have introduced a modified method of simulated moments of McFadden [1989]. Using the same number of Monte Carlo draws as in McFadden's method of simulated moments, our estimator is asymptotically efficient relative to McFadden's estimator. In addition to the method of simulated moments, we have considered also maximum simulated likelihood estimation methods. The estimators are shown to be consistent and asymptotically normal without excessive number of Monte Carlo draws.
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    Understanding Multi-product Health Insurance Marketplaces: An Advancement in Aggregated Demand Estimation Using Bayesian Statistics
    (2020-11) Huang, Tsan-Yao
    Demand functions estimated by aggregated data used in economics and marketingoften employ the approach of Berry, Levinsohn and Pakes (Berry et al., 1995). To apply the method, researchers are required to collect market shares for each product of interest along with product-level attributes. Yet in many applications observed market shares are aggregated by firms or brands which sell multiple products. My thesis addresses this empirical issue by advancing existing BLP estimation procedure from Musalem et al. (2009) by using aggregated market shares at the firm level (port- folio market shares) and product-level attributes. I provide a solution to recovering the distributions of preference weights and price elasticities when researchers are limited to data containing only market shares at firm-level but consumers make choices over product-level attributes. The applications are specifically applied to the Health Insurance Marketplaces in the US.