Improving the Numerical Performance of Static and Dynamic Aggregate Discrete Choice Random Coefficients Demand Estimation by Jean-pierre Dubé,

By Jean-Pierre Dubé, Jeremy T. Fox, Che-Lin Su. 2012

243 citations recorded by the source corpus.

Subfields: Management Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/72075ee2982feef93e89740617898a7e38fbe60f

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Citations in the corpus

Local Protectionism, Market Structure, and Social Welfare: China’s Automobile Market (2017, unknown): “As pointed out by Dube et al. (2012); Knittel and Metaxoglou (2014) and a few others, computational issues are important in nonlinear estimations like BLP.”
Estimation of Random-Coefficient Demand Models: Two Empiricists' Perspective (2014, methodology): “More specifically, the NFP rate of convergence is measured by the Lipschitz constant, which is the norm of a matrix involving the own- and cross-demand elasticities with respect to the demand shock ξ (Dube et al., 2012).”
Dynamics of Consumer Demand for New Durable Goods (2012, unknown): “A variant of our model in which consumers can purchase only once and all current and future model attri- 17 Dube, Fox, and Su ð2012Þ and Judd and Su ð2012Þ suggest using a mathematical program with equilibrium constraints ðMPECÞ to solve this problem rather than successive approximations.”
Best practices for differentiated products demand estimation with PyBLP (2020, methodology): “As shown by Dubé et al. (2012), a smaller outside good share implies a larger value for the contraction’s Lipschitz constant, which generally increases the number of required iterations.”
Frictions in a Competitive, Regulated Market: Evidence from Taxis (2015, methodology): “Applications of MPEC to demand models and dynamic oligopoly models can be found in, for example Conlon (2010) and Dubé et al. (2012). An intuitive explanation for the computational advantage of MPEC is that the constraints imposed by the economic model are”
Nested Logit or Random Coefficients Logit? A Comparison of Alternative Discrete Choice Models of Product Differentiation (2014, methodology): “…role of starting values and different optimization algorithms (Knittel & Metaxoglou, 2012), problems with numerically solving δt using BLP’s contracting mapping (Dubé et al., 2012), and problems with approximating the integral over the logit probabilities using simulation (Judd & Skrainka, 2011).”