Dubâe, Jean-Pierre.
Improving the numerical performance of BLP static and dynamic discrete choice random coefficients demand estimation / Jean-Pierre H. Dubâe, Jeremy T. Fox, Che-Lin Su. - Cambridge, Mass. : National Bureau of Economic Research, 2009. - 51 p. : ill. ; 22 cm. - NBER working paper series ; no. 14991. . - Working paper series (National Bureau of Economic Research) ; no. 14991. .
Includes bibliographical references.
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.
Economics--Statistical methods
HB1 / .N38 no. 14991
Improving the numerical performance of BLP static and dynamic discrete choice random coefficients demand estimation / Jean-Pierre H. Dubâe, Jeremy T. Fox, Che-Lin Su. - Cambridge, Mass. : National Bureau of Economic Research, 2009. - 51 p. : ill. ; 22 cm. - NBER working paper series ; no. 14991. . - Working paper series (National Bureau of Economic Research) ; no. 14991. .
Includes bibliographical references.
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.
Economics--Statistical methods
HB1 / .N38 no. 14991
