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Sushant Acharya, Michael Cai, Marco Del Negro, Abhi Gupta, and Pearl Li
This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since March 2018. As usual, we wish to remind our readers that the DSGE model forecast is not an official New York Fed forecast, but only an input to the Research staff’s overall forecasting process. For more information about the model and variables discussed here, see our DSGE model Q & A.
One of the major debates in open economy macroeconomics is the extent to which capital inflows are beneficial for growth. In principle, these flows allow countries to increase their consumption and investment spending beyond their income by enabling them to tap into foreign saving. Periods of such borrowing, however, are associated with large trade deficits, external debt accumulation, and, in some cases, overheating when these economies operate beyond their potential output level for an extended period of time. The relevant question in this context is whether the rate at which a country is taking on external debt has useful predictive information about financial crises.
Michael Cai, Marco Del Negro, Marc Giannoni, Abhi Gupta, and Pearl Li
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 (FOMC) Summary of Economic Projections (SEP).
Michael Cai, Marco Del Negro, Abhi Gupta, and Pearl Li
This post presents a quarterly update of the economic forecast generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe our forecast very briefly and highlight its change since November 2017.
Marco Del Negro, Domenico Giannone, Marc Giannoni, Abhi Gupta, Pearl Li, and Andrea Tambalotti
Third of three posts
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) model.
Ozge Akinci, Michael Cai, Abhi Gupta, Pearl Li, and Andrea Tambalotti
This post presents our quarterly update of the economic forecast generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since August 2017.
Michael Cai, Marc Giannoni, Abhi Gupta, Pearl Li, and Argia Sbordone
This post presents our quarterly update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since May 2017.
Abhi Gupta, Pearl Li, Erica Moszkowski, Marco Del Negro, and Marc Giannoni
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 written in MATLAB and is posted here on Github, a public repository hosting service, also performed some of these functions, but not quite as fast.
The global financial crisis has put financial stability risks—and the potential role of macroprudential policies in addressing them—at the forefront of policy debates. The challenge for macroeconomists is to develop new models that are consistent with the data while being able to capture the highly nonlinear nature of crisis episodes. In this post, we evaluate the impact of a macroprudential policy that has the government tilt incentives for banks to encourage them to build up their equity positions. The government has a role since individual banks do not internalize the systemic benefit of having more bank equity. Our model allows for an evaluation of the tradeoff between the size of such incentives and the probability of a future financial crisis.
Marco Del Negro, Marc Giannoni, Abhi Gupta, Pearl Li, and Erica Moszkowski
This post presents the latest update of the economic forecasts generated by the Federal Reserve Bank of New York’s (FRBNY) dynamic stochastic general equilibrium (DSGE) model. We introduced this model in a series of blog posts in September 2014 and have since published forecasts twice a year. Here we describe our current forecast and highlight how it has changed since May 2016.
Liberty Street Economics features insight and analysis from New York Fed economists working at the intersection of research and policy. Launched in 2011, the blog takes its name from the Bank’s headquarters at 33 Liberty Street in Manhattan’s Financial District.
The editors are Michael Fleming, Andrew Haughwout, Thomas Klitgaard, and Asani Sarkar, all economists in the Bank’s Research Group.
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