JuliaCon 2022 (Times are UTC)

In this talk, I will discuss how the Federal Reserve Bank of New York (FRBNY) uses Julia for forecasting. I will first present the FRBNY model and the basics of our estimation methods, noting recent adjustments made necessary by the rapid changes in economic conditions over the last two years. During this discussion I will introduce our packages DSGE.jl, SMC.jl, and ModelConstructors.jl, which provide a user-friendly API for creating and estimating a variety of models, including our workhorse DSGE model.

I will then discuss how Julia allows us to prototype and test new estimation methods, providing examples through our research into adaptive Metropolis-Hastings and sequential Monte Carlo algorithms. Because DSGE models take significant time to estimate, being able to stay on the cutting edge of Bayesian estimation algorithms allows us to provide results efficiently. Metropolis-Hastings algorithms, a class of random-walk Markov Chain Monte Carlo estimators, use a fixed proposal distribution throughout the estimation process. Adaptive Metropolis-Hastings algorithms update the proposal distribution throughout the estimation process in an attempt to gain efficiency. SMC methods combine MH and importance sampling to create an easily parallelizable sampling algorithm. I will show how these two families of algorithms can speed up the estimation process while illustrating potential pitfalls.

This presentation will be useful to anyone who regularly conducts Bayesian estimation, especially in the context of time series and forecasting.

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 author 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 author.