Projection-based inference with particle swarm optimization

Lynda Khalaf, Zhenjiang Lin

Research output: Journal PublicationArticlepeer-review


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.

Original languageEnglish
Article number104138
JournalJournal of Economic Dynamics and Control
Publication statusPublished - Jul 2021


  • Identification-robust test
  • New Keynesian model
  • Numerical projection
  • Test inversion

ASJC Scopus subject areas

  • Economics and Econometrics
  • Control and Optimization
  • Applied Mathematics


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