Projection-based inference with particle swarm optimization

Lynda Khalaf and Zhenjiang Lin

Journal of Economic Dynamics and Control, 2021, vol. 128, issue C

Abstract: This paper introduces Particle Swarm Optimization [PSO] to econometrics with focus on projection-based test inversion. Econometricians have developed such methods to enable a robust analysis of imperfectly identified models. Despite important theoretical breakthroughs, computational and numerical tool kits have not followed suit. This paper compares stochastic solvers including PSO on speed and accuracy for the problem. Empirically, the paper analyzes a three-equation New Keynesian model for the U.S.. Results are confirmed via a synthetic sample with relevant and weak instruments. In contrast to PSO, we find that popular solvers may converge to local optima suggesting misleading decisions on the nature of the New Keynesian Phillips Curve, determinacy of monetary policies, and the persistence of the Taylor rule. Results confirm that far more attention needs to be paid to numerical precision as test inversion duly gains popularity in applied econometrics.

Keywords: Identification-robust test; New Keynesian model; Numerical projection; Test inversion (search for similar items in EconPapers)
JEL-codes: C32 C51 C63 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:128:y:2021:i:c:s0165188921000737

DOI: 10.1016/j.jedc.2021.104138

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