Macroeconomic nowcasting and forecasting with big data
Data, data, data . . . Economists know it well, especially when it comes to monitoring macroeconomic conditions?the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before ?big data? became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate the best practices of forecasters on trading desks, at central banks, and in other ...
Safety, liquidity, and the natural rate of interest
Why are interest rates so low in the Unites States? We find that they are low primarily because the premium for safety and liquidity has increased since the late 1990s, and to a lesser extent because economic growth has slowed. We reach this conclusion using two complementary perspectives: a flexible time-series model of trends in Treasury and corporate yields, inflation, and long-term survey expectations, and a medium-scale dynamic stochastic general equilibrium (DSGE) model. We discuss the implications of this finding for the natural rate of interest.
We study the conditional distribution of GDP growth as a function of economic and financial conditions. Deteriorating financial conditions are associated with an increase in the conditional volatility and a decline in the conditional mean of GDP growth, leading the lower quantiles of GDP growth to vary with financial conditions and the upper quantiles to be stable over time: Upside risks to GDP growth are low in most periods while downside risks increase as financial conditions become tighter. We argue that amplification mechanisms in the financial sector generate the observed growth ...
Monitoring Economic Conditions during a Government Shutdown
The recent partial shutdown of the federal government has disrupted publication schedules for many U.S. Census Bureau and Bureau of Economic Analysis (BEA) data releases. Most notably, the release of GDP for the fourth quarter of 2018—originally scheduled for January 30—has been postponed indefinitely. Even without the full slate of Census Bureau and BEA releases, forecasters have continued to make predictions for 2018:Q4 GDP growth; as of February 1, the New York Fed Staff Nowcast stands at 2.6 percent, the Atlanta Fed’s GDPNow stands at 2.5 percent, and the Blue Chip Financial ...
Changing Risk-Return Profiles
Are stock returns predictable? This question is a perennially popular subject of debate. In this post, we highlight some results from our recent working paper, where we investigate the matter. Rather than focusing on a single object like the forecasted mean or median, we look at the entire distribution of stock returns and find that the realized volatility of stock returns, especially financial sector stock returns, has strong predictive content for the future distribution of stock returns. This is a robust feature of the data since all of our results are obtained with real-time analyses ...
A Large Bayesian VAR of the United States Economy
We model the United States macroeconomic and financial sectors using a formal and unified econometric model. Through shrinkage, our Bayesian VAR provides a flexible framework for modeling the dynamics of thirty-one variables, many of which are tracked by the Federal Reserve. We show how the model can be used for understanding key features of the data, constructing counterfactual scenarios, and evaluating the macroeconomic environment both retrospectively and prospectively. Considering its breadth and versatility for policy applications, our modeling approach gives a reliable, reduced form ...
Forecasting Macroeconomic Risks
We construct risks around consensus forecasts of real GDP growth, unemployment, and inflation. We find that risks are time-varying, asymmetric, and partly predictable. Tight financial conditions forecast downside growth risk, upside unemployment risk, and increased uncertainty around the inflation forecast. Growth vulnerability arises as the conditional mean and conditional variance of GDP growth are negatively correlated: downside risks are driven by lower mean and higher variance when financial conditions tighten. Similarly, employment vulnerability arises as the conditional mean and ...
The effectiveness of nonstandard monetary policy measures: evidence from survey data
We assess the perception of professional forecasters regarding the effectiveness of unconventional monetary policy measures announced by the U.S. Federal Reserve after the collapse of Lehman Brothers. Using survey data collected at the individual level, we analyze the change in forecasts of Treasury and corporate bond yields around the announcement dates of nonstandard monetary policy measures. We find that professional forecasters expect bond yields to drop significantly for at least one year after the announcement of accommodative policies.
Opening the Toolbox: The Nowcasting Code on GitHub
In April 2016, we unveiled--and began publishing weekly--the New York Fed Staff Nowcast, an estimate of GDP growth using an automated platform for tracking economic conditions in real time. Today we go a step further by publishing the MATLAB code for the nowcasting model, available here on GitHub, a public repository hosting service. We hope that sharing our code will make it easier for people interested in monitoring the macroeconomy to understand the details underlying the nowcast and to replicate our results.
Reading the Tea Leaves of the U.S. Business Cycle—Part One
The study of the business cycle—fluctuations in aggregate economic activity between times of widespread expansion and contraction—is one of the foremost pursuits in macroeconomics. But even distinguishing periods of expansion and recession can be challenging. In this post, we discuss different conceptual approaches to dating the business cycle, study their past performance for the U.S. economy, and highlight the informativeness of labor market indicators.