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Working Paper
Nowcasting Business Cycles: a Bayesian Approach to Dynamic Heterogeneous Factor Models
We develop a framework for measuring and monitoring business cycles in real time. Following a long tradition in macroeconometrics, inference is based on a variety of indicators of economic activity, treated as imperfect measures of an underlying index of business cycle conditions. We extend existing approaches by permitting for heterogenous lead-lag patterns of the various indicators along the business cycles. The framework is well suited for high-frequency monitoring of current economic conditions in real time - nowcasting - since inference can be conducted in presence of mixed frequency ...
Working Paper
Nowcasting U.S. Headline and Core Inflation
Forecasting future inflation and nowcasting contemporaneous inflation are difficult. We propose a new and parsimonious model for nowcasting headline and core inflation in the U.S. price index for personal consumption expenditures (PCE) and the consumer price index (CPI). The model relies on relatively few variables and is tested using real-time data. The model?s nowcasting accuracy improves as information accumulates over the course of a month or quarter, and it easily outperforms a variety of statistical benchmarks. In head-to-head comparisons, the model?s nowcasts of CPI infl ation ...
Working Paper
Predicting Benchmarked US State Employment Data in Realtime
US payroll employment data come from a survey of nonfarm business establishments and are therefore subject to revisions. While the revisions are generally small at the national level, they can be large enough at the state level to substantially alter assessments of current economic conditions. Researchers and policymakers must therefore exercise caution in interpreting state employment data until they are "benchmarked" against administrative data on the universe of workers some 5 to 16 months after the reference period. This paper develops and tests a state space model that predicts ...
Working Paper
Nowcasting Turkish GDP and News Decomposition
Real gross domestic product (GDP) data in Turkey are released with a very long delay compared with other economies, between 10 and 13 weeks after the end of the reference quarter. To infer the current state of the economy, policy makers, media, and market practitioners examine data that are more timely, that are released at higher frequencies than the GDP. In this paper, we propose an econometric model that automatically allows us to read through these more current and higher-frequency data and translate them into nowcasts for the Turkish real GDP. Our model outperforms nowcasts produced by ...
Discussion Paper
Reintroducing the New York Fed Staff Nowcast
“Nowcasts” of GDP growth are designed to track the economy in real time by incorporating information from an array of indicators as they are released. In April 2016, the New York Fed’s Research Group launched the New York Fed Staff Nowcast, a dynamic factor model that generated estimates of current quarter GDP growth at a weekly frequency. The onset of the COVID-19 pandemic sparked widespread economic disruptions—and unprecedented fluctuations in the economic data that flow into the Staff Nowcast. This posed significant challenges to the model, leading to the suspension of publication ...
Working Paper
Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting
Financial data often contain information that is helpful for macroeconomic forecasting, while multistep forecast accuracy also benefits by incorporating good nowcasts of macroeconomic variables. This paper considers the role of nowcasts of financial variables in making conditional forecasts of real and nominal macroeconomic variables using standard quarterly Bayesian vector autoregressions (BVARs). For nowcasting the quarterly value of a variety of financial variables, we document that the average of the available daily data and a daily random walk forecast to fill in the missing days in the ...
Discussion Paper
Monitoring Real Activity in Real Time: The Weekly Economic Index
Economists are well-practiced at assessing real activity based on familiar aggregate time series, like the unemployment rate, industrial production, or GDP growth. However, these series represent monthly or quarterly averages of economic conditions, and are only available at a considerable lag, after the month or quarter ends. When the economy hits sudden headwinds, like the COVID-19 pandemic, conditions can evolve rapidly. How can we monitor the high-frequency evolution of the economy in “real time”?
Discussion Paper
Tracking the COVID-19 Economy with the Weekly Economic Index (WEI)
At the end of March, we launched the Weekly Economic Index (WEI) as a tool to monitor changes in real activity during the pandemic. The rapid deterioration in economic conditions made it important to assess developments as soon as possible, rather than waiting for monthly and quarterly data to be released. In this post, we describe how the WEI has measured the effects of COVID-19. So far in 2020, the WEI has synthesized daily and weekly data to measure GDP growth remarkably well. We document this performance, and we offer some guidance on evaluating the WEI’s forecasting abilities based on ...
Working Paper
Predicting Benchmarked US State Employment Data in Real Time
US payroll employment data come from a survey of nonfarm business establishments and are therefore subject to revisions. While the revisions are generally small at the national level, they can be large enough at the state level to substantially alter assessments of current economic conditions. Researchers and policymakers must therefore exercise caution in interpreting state employment data until they are “benchmarked” against administrative data on the universe of workers some 5 to 16 months after the reference period. This paper develops and tests a state space model that predicts ...
Working Paper
GDPNow: A Model for GDP \"Nowcasting\"
This paper documents GDPNow, a "nowcasting" model for gross domestic product (GDP) growth that synthesizes the "bridge equation" approach relating GDP subcomponents to monthly source data with the factor model approach used by Giannone, Reichlin, and Small (2008). The GDPNow model forecasts GDP growth by aggregating 13 subcomponents that make up GDP with the chain-weighting methodology used by the U.S. Bureau of Economic Analysis. Using current vintage data, out-of-sample GDPNow model forecasts are found to be more accurate than a number of statistical benchmarks since 2000. Using ...