Improving the Numerical Performance of BLP Static and Dynamic Discrete Choice Random Coefficients Demand Estimation
2009/12/08
研討會日期 : 2009-12-08
時間 : 14:30
主講人 : 蘇哲霖助理教授
地點 : B110
演講者簡介 : 蘇哲霖教授為Ph.D. in Management Science and Engineering，Stanford University (2007)。現為美國芝加哥大學布斯商學院助理教授。其主要研究領域為經濟問題的計算方法。
演講摘要 : The widely-used estimator of Berry, Levinsohn and Pakes (1995) produces estimates of consumer preferences from a discrete-choice demand model with random coefficients, market-level demand shocks and endogenous prices. We derive numerical theory results characterizing the properties of the nested fixed point algorithm used to evaluate the objective function of BLP’s estimator. We discuss problems with typical implementations, including cases that can lead to incorrect parameter estimates. As a solution, we recast estimation as a mathematical program with equilibrium constraints, which can be faster and which avoids the numerical issues associated with nested inner loops. The advantages are even more pronounced for forward-looking demand models where Bellman’s equation must also be solved repeatedly. Several Monte Carlo and real-data experiments support our numerical concerns about the nested fixed point approach and the advantages of constrained optimization.
