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Working Paper
The Distributional Predictive Content of Measures of Inflation Expectations
This paper examines the predictive relationship between the distribution of realized inflation in the US and measures of inflation expectations from households, firms, financial markets, and professional forecasters. To allow for nonlinearities in the predictive relationship we use quantile regression methods. We find that the ability of households to predict future inflation, relative to that of professionals, firms, and the market, increases with inflation. While professional forecasters are more accurate in the middle of the inflation density, households’ expectations are more useful in ...
Working Paper
The Effects of Interest Rate Increases on Consumers' Inflation Expectations: The Roles of Informedness and Compliance
We study how monetary policy communications associated with increasing the federal funds rate causally affect consumers' inflation expectations. In a large-scale, multi-wave randomized controlled trial (RCT), we find weak evidence on average that communicating policy changes lowers consumers' medium-term inflation expectations. However, information differs systematically across demographic groups, in terms of ex ante informedness about monetary policy and ex post compliance with the information treatment. Monetary policy communications have a much stronger effect on people who had not ...
Working Paper
Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
Recent decades have seen advances in using econometric methods to produce more timely and higher-frequency estimates of economic activity at the national level, enabling better tracking of the economy in real time. These advances have not generally been replicated at the sub–national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed– frequency Bayesian VAR model to address common ...
Working Paper
Reconciled Estimates of Monthly GDP in the US
In the US, income and expenditure-side estimates of GDP (GDPI and GDPE) measure "true" GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDPE, GDPI, unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error ...
Working Paper
Communicating Data Uncertainty: Multi-Wave Experimental Evidence for UK GDP
Economic statistics are commonly published without estimates of their uncertainty. We conduct two waves of a randomized controlled online experiment to assess if and how the UK public understands data uncertainty. A control group observes only the point estimate of GDP. Treatment groups are presented with alternative qualitative and quantitative communications of GDP data uncertainty. We find that most of the public understands that GDP numbers are uncertain. Quantitative communications of data uncertainty help align the public’s subjective probabilistic expectations of data uncertainty ...
Working Paper
The FOMC versus the Staff: Do Policymakers Add Value in Their Tales?
Using close to 40 years of textual data from FOMC transcripts and the Federal Reserve staff's Greenbook/Tealbook, we extend Romer and Romer (2008) to test if the FOMC adds information relative to its staff forecasts not via its own quantitative forecasts but via its words. We use methods from natural language processing to extract from both types of document text-based forecasts that capture attentiveness to and sentiment about the macroeconomy. We test whether these text-based forecasts provide value-added in explaining the distribution of outcomes for GDP growth, the unemployment rate, and ...
Working Paper
Censored Density Forecasts: Production and Evaluation
This paper develops methods for the production and evaluation of censored density forecasts. Censored density forecasts quantify forecast risks in a middle region of the density covering a specified probability, and ignore the magnitude but not the frequency of outlying observations. We propose a new estimator that fits a potentially skewed and fat-tailed density to the inner observations, acknowledging that the outlying observations may be drawn from a different but unknown distribution. We also introduce a new test for calibration of censored density forecasts. An application using ...
Working Paper
Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics
Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the "data speak." Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile ...
Working Paper
Incorporating Short Data into Large Mixed-Frequency VARs for Regional Nowcasting
Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and "short" data imply a "ragged edge" at both the beginning and the end of regional data sets, ...
Journal Article
A New Measure of Consumers’ (In)Attention to Inflation
Since the onset of the SARS-CoV-2 (COVID-19) pandemic in March 2020, the Federal Reserve Bank of Cleveland has been running a daily survey that asks consumers for their views on how they are responding to COVID-19 and how COVID-19 is likely to affect the economy. Among the many questions asked, the survey solicits consumers’ inflation expectations. This is an important data set given that such expectations, while affected by current and past inflation, have long been believed to influence future inflation. In this Commentary, we use these daily expectations data to propose a new measure of ...