Noisy, Non-Smooth, Non-Convex Estimation of Moment Condition Models


Jean-Jacques Forneron (Boston U)

Date & Time

29 November 2022




The Institute for Fiscal Studies
7 Ridgmount Street,

Abstract: A practical challenge in structural estimation is the requirement to minimize a sample objective function which is often non-smooth, non-convex, or both. This paper proposes a simple algorithm designed to find accurate solutions without performing an exhaustive search. It augments each iteration from a new Gauss-Newton algorithm with a grid search step. A finite sample analysis derives its optimization and statistical properties simultaneously under standard econometric assumptions. After a finite number of iterations, the algorithm transitions from global to fast local convergence, producing accurate estimates with high-probability. Simulated examples and an empirical application illustrate the properties and performance of the algorithm. Comparisons with commonly used optimizers and quasi-Bayesian estimation using MCMC are also given.

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Venue address:

The Institute for Fiscal Studies 
7 Ridgmount Street  London WC1E 7AE

*Please use the IFS Conference entrance, to the left of the car parking area, and not the glass CILIP entrance.