In this talk, I will highlight StateSpaceRoutines.jl, a repository containing state space filtering and smoothing methods such as the Kalman filter, Durbin Koopman smoother, and others. Specifically, I will discuss our latest addition to StateSpaceRoutines.jl, the “tempered particle filter” (TPF) which was developed by Ed Herbst (Federal Reserve Board of Governors) and Frank Schorfheide (University of Pennsylvania). TPF provides a robust way to simulate the distribution of the states and to calculate the likelihood in a general state space model. Furthermore, I will explain why TPF produces more accurate approximations than the standard bootstrap particle filter, and provide some results showing this. Because TPF is an inherently parallelizable algorithm, I will also delve into some details of the computational implementation and some lessons that we have learned along the way about Julia’s parallel framework. I will highlight a few issues we are still currently facing with regards to optimizing the performance of TPF. StateSpaceRoutines.jl should prove useful to economists, statisticians, and those generally interested in Bayesian methods as a stand-alone suite of tools, which can be used to estimate a variety of both linear and non-linear state space models. Lastly, I will briefly highlight the NY Fed DSGE team’s recent work on implementing various kinds of heterogeneous agent models, both in discrete and continuous time, and discuss the relevance of our implementations of TPF and other sequential Monte Carlo methods to estimating these models in the future.
Disclaimer: This talk reflects the experience of the author and does not represent an endorsement by the Federal Reserve Bank of New York or the Federal Reserve System of any particular product or service. The views expressed in this talk are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.
Michael Cai is a recent graduate of NYU and a senior research analyst working on dynamic stochastic general equilibrium (DSGE) models at the New York Fed. I am interested in using Julia to make the research and policy work at the New York Fed more performant, extensible, and beautiful. More broadly, I want to engage in the frontier of economic research to produce actionable public policies and to improve effective governance and societal welfare.
