Search Results
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
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, ...
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
Communicating Data Uncertainty: Multi-Wave Experimental Evidence for UK GDP
Economic statistics are commonly published without any explicit indication of their uncertainty. To assess if and how the UK public interprets and understands data uncertainty, we conduct two waves of a randomized controlled online experiment. A control group is presented with the headline point estimate of GDP, as emphasized by the statistical office. Treatment groups are then presented with alternative qualitative and quantitative communications of GDP data uncertainty. We find that most of the public understands that uncertainty is inherent in official GDP numbers. But communicating ...
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
Bayesian Modeling of Time-Varying Parameters Using Regression Trees
In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference ...
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 ...
Journal Article
Regional Economic Sentiment: Constructing Quantitative Estimates from the Beige Book and Testing Their Ability to Forecast Recessions
We use natural language processing methods to quantify the sentiment expressed in the Federal Reserve's anecdotal summaries of current economic conditions in the national and 12 Federal Reserve District-level economies as published eight times per year in the Beige Book since 1970. We document that both national and District-level economic sentiment tend to rise and fall with the US business cycle. But economic sentiment is extremely heterogeneous across Districts, and we find that national economic sentiment is not always the simple aggregation of District-level sentiment. We show that the ...
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
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 ...