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Wednesday, June 21 • 1:30pm - 2:06pm
Using Parallel Computing for Macroeconomic Forecasting at the Federal Reserve Bank of New York

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This talk will give an overview of how researchers at the Federal Reserve Bank of New York have implemented economic forecasting and other post-estimation analyses of dynamic stochastic general equilibrium (DSGE) models using Julia’s parallel computing framework.

This is part of the most recent release of our DSGE.jl package, following our ports of the DSGE model solution and estimation steps from MATLAB that were presented at JuliaCon in 2016. I will discuss the technical challenges and constraints we faced in our production environment and how we used Julia’s parallel computing tools to substantially reduce both the time and memory usage required to forecast our models. I will present our experiences with the different means of parallel computing offered in Julia - including an extended attempt at using DistributedArrays.jl - and discuss what we have learned about parallelization, both in Julia and in general.addition, I will provide some of our new perspectives on using Julia in a production setting at an academic and policy institution.

DSGE models are sometimes called the workhorses of modern macroeconomics, applying insights from microeconomics to inform our understanding of the economy as a whole. They are used to forecast economic variables, investigate counterfactual scenarios, and understand the impact of monetary policy. The New York Fed’s DSGE model is a large-scale model of the U.S. economy, which incorporates the zero lower bound, price/wage stickiness, financial frictions, and other realistic features of the economy. Solving, estimating, and forecasting it presents a series of high-dimensional problems which are well suited for implementation in Julia.

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.

avatar for Pearl Li

Pearl Li

Research Analyst, Federal Reserve Bank of New York
I'm a Research Analyst at the New York Fed using Julia to estimate and forecast macroeconomic models. I'm a 2016 graduate of the University of Pennsylvania, where I studied math and economics. I'm interested in applying the frontier of scientific computing to economic research.

Wednesday June 21, 2017 1:30pm - 2:06pm
West Pauley Pauley Ballroom, Berkeley, CA