Efficient Likelihood-based Estimation via Annealing for Dynamic Structural Macrofinance Models

TL;DR

This paper introduces an annealed controlled sequential Monte Carlo method for stable, low-variance likelihood estimation in complex non-linear macrofinance models, enabling improved parameter inference.

Contribution

It develops a novel annealed SMC method with optimal proposals and stability analysis, advancing likelihood estimation in high-dimensional macrofinance models.

Findings

Demonstrates improved likelihood estimation accuracy.

Shows stable and consistent parameter inference.

Successfully applies to complex macrofinance models.

Abstract

Most solved dynamic structural macrofinance models are non-linear and/or non-Gaussian state-space models with high-dimensional and complex structures. We propose an annealed controlled sequential Monte Carlo method that delivers numerically stable and low variance estimators of the likelihood function. The method relies on an annealing procedure to gradually introduce information from observations and constructs globally optimal proposal distributions by solving associated optimal control problems that yield zero variance likelihood estimators. To perform parameter inference, we develop a new adaptive SMC$^2$ algorithm that employs likelihood estimators from annealed controlled sequential Monte Carlo. We provide a theoretical stability analysis that elucidates the advantages of our methodology and asymptotic results concerning the consistency and convergence rates of our SMC$^2$ estimators. We illustrate the strengths of our proposed methodology by estimating two popular macrofinance models: a non-linear new Keynesian dynamic stochastic general equilibrium model and a non-linear non-Gaussian consumption-based long-run risk model.