The New York Fed DSGE Model Forecast–March 2018

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.

The New York Fed DSGE Model Forecast–November 2017

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.

Just Released: A Monthly Underlying Inflation Gauge

Robert Rich



Today marks the launch of the monthly publication of the Underlying Inflation Gauge (UIG). We are reporting two UIG measures, described recently on Liberty Street Economics, that are constructed to provide an estimate of the trend, or persistent, component of inflation. One measure is derived using a large number of disaggregated price series in the consumer price index (CPI), while the second measure incorporates additional information from macroeconomic and financial variables.

The New York Fed DSGE Model Forecast—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.

Hey, Economist! How Do You Forecast the Present?

A Conversation with Domenico Giannone, Argia Sbordone, and Andrea Tambalotti



New York Fed macroeconomists have been sharing their “nowcast” of GDP growth on the Bank’s public website since April 2016. Now, they’ve launched an interactive version of the Nowcasting Report, which updates the point forecast each week, but also helps users better visualize the impact of the flow of incoming data on the estimate produced by the model. Tables offer more detail on the data series informing the estimate. The interactive version also reports the staff nowcast back to January 2016, a longer nowcast history than has previously been available. Cross-media editor Anna Snider spoke to Domenico Giannone, Argia Sbordone, and Andrea Tambalotti—economists who developed the model underlying the report and produce estimates weekly with the help of research analysts Brandyn Bok and Daniele Caratelli—about nowcasting and its role in the policymaking process.

Measuring Trend Inflation with the Underlying Inflation Gauge

Marlene Amstad, Simon Potter, and Robert Rich



Consumers, financial market participants, and policymakers are particularly interested in the trend, or persistent, component of inflation. But this variable is not observed, which has resulted in a variety of proposed proxy measures. Because each measure has its own strengths and weaknesses, a consensus about a preferred candidate has not emerged. Here, we introduce the Underlying Inflation Gauge (UIG) as a measure of trend inflation. Among its attractive features, the UIG is derived from a large data set that extends beyond price variables and displays greater forecast accuracy than various measures of core inflation.

Forecasting with Julia

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.

Just Released: The New York Fed Staff Forecast—April 2017

Jonathan McCarthy, Richard Peach, and Robert Rich

Today, the Federal Reserve Bank of New York (FRBNY) is hosting the spring meeting of its Economic Advisory Panel (EAP). As has become the custom at this meeting, the FRBNY staff is presenting its forecast for U.S. real GDP growth, the unemployment rate, and inflation. Following the presentation, members of the EAP, which consists of leading economists in academia and the private sector, are asked to critique the staff forecast. Such feedback helps the staff evaluate the assumptions and reasoning underlying its forecast as well as the forecast’s key risks. The feedback is also an important part of the forecasting process because it informs the staff’s discussions with New York Fed President William Dudley about economic conditions. In that same spirit, we are sharing a summary of the staff forecast in this post. For more detail, see the FRBNY Staff Outlook Presentation from the EAP meeting on our website.

From the Vault: Factor This In

Anna Snider

In May 2014, Liberty Street Economics bloggers shared a new approach for calculating the Treasury term premium as well as a link for downloading their estimates of it. It has been gratifying to see the “ACM” model (named for current and former New York Fed economists Tobias Adrian, Richard Crump, and Emanuel Moench) make eye-catching headlines, and become “increasingly canonical,” as one reporter described it.

The FRBNY DSGE Model Forecast—February 2017

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 published forecasts twice a year thereafter. With this post, we move to a quarterly release schedule, and highlight how our forecasts have changed since November 2016.