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

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 using approximate dynamic programming schemes. To perform parameter inference, we develop a new adaptive SMC^2 algorithm that employs likelihood estimators from annealed controlled sequential Monte Carlo. 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