Economic Predictions with Big Data: The Illusion of Sparsity

Domenico Giannone, Michele Lenza, and Giorgio Primiceri



The availability of large data sets, combined with advances in the fields of statistics, machine learning, and econometrics, have generated interest in forecasting models that include many possible predictive variables. Are economic data sufficiently informative to warrant selecting a handful of the most useful predictors from this larger pool of variables? This post documents that they usually are not, based on applications in macroeconomics, microeconomics, and finance.

Forecasts of the Lost Recovery

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).

Just Released: The New York Fed Staff Forecast—April 2018

Jonathan McCarthy, Richard Peach, and Robert Rich

Today, the Federal Reserve Bank of New York is hosting the spring meeting of its Economic Advisory Panel (EAP). As has become the custom at this meeting, the New York Fed’s Research staff is presenting its forecast for U.S. growth, inflation, and the unemployment rate. 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 short summary of the staff forecast in this post; for more detail, see the New York Fed Staff Outlook Presentation from the EAP meeting on our website.

Vulnerable Growth

Tobias Adrian, Nina Boyarchenko, and Domenico Giannone




Traditional GDP forecasts potentially present an overly optimistic (or pessimistic) view of the state of the economy: by focusing on the point estimate for the conditional mean of growth, such forecasts ignore risks around the central forecast. Yet, policymakers around the world increasingly focus on risks to the central forecast in policy debates. For example, in the United States the Federal Open Market Committee (FOMC) commonly discusses the balance of risks in the economy, with the relative prominence of this discussion fluctuating with the state of the economy. In a recent paper, we propose a method for constructing the full conditional distribution of GDP projected growth as a function of current economic and financial conditions. This blog post reviews some of the findings from that paper and the implications for macroeconomic theory and for policymakers.

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.