Time-varying Uncertainty of the Federal Reserve’s Output Gap Estimate
A factor stochastic volatility model estimates the common component to estimates of the output gap produced by the staff of the Federal Reserve, its time-varying volatility, and time-varying, horizon-specific forecast uncertainty. Output gap estimates are very uncertain, even well after the fact, especially at business cycle turning points. However, the common component of the output gap estimates is clearly procyclical, and innovations to the common factor produce persistent positive effects on economic activity. Output gaps estimated by the Congressional Budget Office have very similar ...
Which Output Gap Estimates Are Stable in Real Time and Why?
Output gaps that are estimated in real time can differ substantially from those estimated after the fact. We aim to understand the real-time instability of output gap estimates by comparing a suite of reduced-form models. We propose a new statistical decomposition and find that including a Okun’s law relationship improves real-time stability by alleviating the end-point problem. Models that include the unemployment rate also produce output gaps with relevant economic content. However, we find that no model of the output gap is clearly superior to the others along each metric we consider.
The mismeasured personal saving rate is still useful: using real-time data to improve forecasting
People make decisions based on information. Often, with hindsight, they could have made better choices. Economics faces a similar problem: Economic data, when first released, are often inaccurate and may subsequently be revised. In "The Mismeasured Personal Saving Rate Is Still Useful: Using Real-Time Data to Improve Forecasting," Leonard Nakamura uses the U.S. personal saving rate - a statistic that has often been initially low, then substantially revised upward - to discuss how modern economic statistical techniques can improve forecasting.
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 ...
Real-time macroeconomic monitoring: real activity, inflation, and interactions
The authors sketch a framework for monitoring macroeconomic activity in real-time and push it in new directions. In particular, they focus not only on real activity, which has received most attention to date, but also on inflation and its interaction with real activity. As for the recent recession, the authors find that (1) it likely ended around July 2009; (2) its most extreme aspects concern a real activity decline that was unusually long but less unusually deep, and an inflation decline that was unusually deep but brief; and (3) its real activity and inflation interactions were strongly ...
Real-time data and fiscal policy analysis: a survey of the literature
This paper surveys the empirical research on fiscal policy analysis based on real-time data. This literature can be broadly divided in three groups that focus on: (1) the statistical properties of revisions in fiscal data; (2) the political and institutional determinants of fiscal data revisions and of one-year-ahead projection errors by governments and (3) the reaction of fiscal policies to the business cycle. It emerges that, first, fiscal data revisions are large and initial releases are biased estimates of final values. Second, the presence of strong fiscal rules and institutions leads to ...
Lessons from the latest data on U.S. productivity
Productivity growth is carefully scrutinized by macroeconomists because it plays key roles in understanding private savings behaviour, the sources of macroeconomic shocks, the evolution of international competitiveness and the solvency of public pension systems, among other things. However, estimates of recent and expected productivity growth rates suffer from two potential problems: (i) recent estimates of growth trends are imprecise, and (ii) recently published data often undergo important revisions. This paper documents the statistical (un)reliability of several measures of aggregate ...
Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting
We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produces for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these new data to study whether the staff forecasts efficiently and whether efficiency, or lack thereof, is time-varying. Prespecified regressions of forecast errors on forecast revisions show that the staff's ...
UK World War I and interwar data for business cycle and growth analysis
This article contributes new time series for studying the UK economy during World War I and the interwar period. The time series are per capita hours worked and average capital income, labor income, and consumption tax rates. Uninterrupted time series of these variables are provided for an annual sample that runs from 1913 to 1938. The authors highlight the usefulness of these time series with several empirical applications. The per capita hours worked data are used in a growth accounting exercise to measure the contributions of capital, labor, and productivity to output growth. The average ...
Evaluating real-time VAR forecasts with an informative democratic prior
This paper proposes Bayesian forecasting in a vector autoregression using a democratic prior. This prior is chosen to match the predictions of survey respondents. In particular, the unconditional mean for each series in the vector autoregression is centered around long-horizon survey forecasts. Heavy shrinkage toward the democratic prior is found to give good real-time predictions of a range of macroeconomic variables, as these survey projections are good at quickly capturing endpoint-shifts.