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Forecasting with Julia
A little more than a year ago, in this post, we announced DSGE.jl?a package for working with dynamic stochastic general equilibrium (DSGE) models using Julia, the open-source computing language. At that time, DSGE.jl contained only the code required to specify, solve, and estimate such models using Bayesian methods. Now, we have extended the package to provide the additional code needed to produce economic forecasts, counterfactual simulations, and inference on unobservable variables, such as the natural rate of interest or the output gap. The old, pre-Julia version of the code, which was ...
A DSGE Perspective on Safety, Liquidity, and Low Interest Rates
The preceding two posts in this series documented that interest rates on safe and liquid assets, such as U.S. Treasury securities, have declined significantly in the past twenty years. Of course, short-term interest rates in the United States are under the control of the Federal Reserve, at least in nominal terms. So it is legitimate to ask, To what extent is this decline driven by the Federal Reserve?s interest rate policy? This post addresses this question by coupling the results presented in the previous post with those obtained from an estimated dynamic stochastic general equilibrium ...
DSGE forecasts of the lost recovery
The years following the Great Recession were challenging for forecasters. Unlike other deep downturns, this recession was not followed by a swift recovery, but generated a sizable and persistent output gap that was not accompanied by deflation as a traditional Phillips curve relationship would have predicted. Moreover, the zero lower bound and unconventional monetary policy generated an unprecedented policy environment. We document the real real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model during this period and explain the results using ...
The FRBNY DSGE Model Meets Julia
We have implemented the FRBNY DSGE model in a free and open-source language called Julia. The code is posted here on GitHub, a public repository hosting service. This effort is the result of a collaboration between New York Fed staff and folks from the QuantEcon project, whose aim is to coordinate development of high performance open-source code for quantitative economic modeling.
Forecasts of the Lost Recovery
The years following the Great Recession were challenging for forecasters for a variety of reasons, including an unprecedented policy environment. This post, based on our recently released working paper, documents the real-time forecasting performance of the New York Fed dynamic stochastic general equilibrium (DSGE) model in the wake of the Great Recession. We show that the model’s predictive accuracy was on par with that of private forecasters and proved to be quite a bit better, at least in terms of GDP growth, than that of the median forecasts from the Federal Open Market Committee’s ...