Four Essays on Identification-Robust Numerical and Statistical Tools with Applications to Dynamic Stochastic General Equilibrium Models

Abstract

In this thesis, we propose identification-robust numerical and statistical confidence-set estimation tools for Dynamic Stochastic General Equilibrium [DSGE] models. The first chapter introduces Particle Swarm Optimization [PSO] to econometrics with focus on test inversion and numerical projections. Empirically, the paper analyzes a three-equation New-Keynesian model. In contrast to PSO, the genetic algorithm, simulated annealing and even grid searches converge to local optima that suggest misleading economic decisions on: (i) the nature of the New-Keynesian Phillips Curve [NKPC], (ii) determinacy of monetary policies, and (iii) the persistence of the Taylor rule. Given this evidence and using PSO, the next three chapters introduce new and improved econometric methods for inference on DSGEs.