Estimation and inference by stochastic optimization

Jean-Jacques Forneron

Journal of Econometrics, 2024, vol. 238, issue 2

Abstract: In non-linear estimations, it is common to assess sampling uncertainty by bootstrap inference. For complex models, this can be computationally intensive. This paper combines optimization with resampling: turning stochastic optimization into a fast resampling device. Two methods are introduced: a resampled Newton–Raphson (rnr) and a resampled quasi-Newton (rqn) algorithm. Both produce draws that can be used to compute consistent estimates, confidence intervals, and standard errors in a single run. The draws are generated by a gradient and Hessian (or an approximation) computed from batches of data that are resampled at each iteration. The proposed methods transition quickly from optimization to resampling when the objective is smooth and strictly convex. Simulated and empirical applications illustrate the properties of the methods on large scale and computationally intensive problems. Comparisons with frequentist and Bayesian methods highlight the features of the algorithms.

Keywords: Stochastic gradient descent; M-estimation; m out of n and multiplier bootstrap (search for similar items in EconPapers)
JEL-codes: C2 C3 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:238:y:2024:i:2:s0304407623003548

DOI: 10.1016/j.jeconom.2023.105638

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