Overview
Victor Aguirregabiria is a professor of economics at the University of Toronto. His research focuses on structural estimation of dynamic discrete choice models, with applications to industrial organization (retail market dynamics, firm entry/exit) and labor economics. He is best known for co-developing the NPL (Nested Pseudo Likelihood) algorithm with Pedro Mira, which iteratively updates conditional choice probability estimates to achieve better finite-sample properties than the Hotz-Miller two-step CCP estimator.
Key Contributions / Features
- NPL algorithm (Aguirregabiria-Mira 2002, Econometrica 70(4):1519–1543): iterative CCP fixed-point procedure that asymptotically achieves partial MLE efficiency with substantially reduced computation relative to NFXP.
- Survey of DDC estimation (Aguirregabiria-Mira 2010, Journal of Econometrics 156:38–67): comprehensive review of single-agent, competitive-equilibrium, and dynamic-game DDC estimators.
- Applied DDC methods to retail store entry/exit, bread prices, and occupational dynamics.
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