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Working Papers

Working Papers

  • WP 22-01 | Reconciled Estimates of Monthly GDP in the US

    Gary Koop Stuart McIntyre James Mitchell Aubrey Poon


    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 perspective (that is, the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession.   Read More

  • WP 20-32R | Measuring Uncertainty and Its Effects in the COVID-19 Era

    Andrea Carriero Todd E. Clark Massimiliano Marcellino Elmar Mertens

    Original Paper: WP 20-32


    We measure the effects of the COVID-19 outbreak on uncertainty, and we assess the consequences of the uncertainty for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on uncertainty. The model includes additional latent volatility states in order to accommodate outliers in volatility, to reduce the influence of extreme observations from the COVID period. Our estimates yield large increases in macroeconomic and financial uncertainty since the onset of the COVID-19 period. These increases have contributed to the downturn in economic and financial conditions, but the contributions of uncertainty are small compared to the overall movements in many macroeconomic and financial indicators. That implies that the downturn is driven more by other dimensions of the COVID crisis than shocks to aggregate uncertainty (as measured by our method).   Read More

  • WP 18-03C | Corrigendum to: Assessing International Commonality in Macroeconomic Uncertainty and Its Effects

    Andrea Carriero Todd E. Clark Massimiliano Marcellino

    Original Paper: WP 18-03 | Revisions: WP 18-03R


    Carriero, Clark, and Marcellino (2020, CCM2020) used large BVAR models with a factor structure to stochastic volatility to produce estimates of time-varying international macroeconomic uncertainty and assess uncertainty's effects on the global economy. The results in CCM2020 were based on an estimation algorithm that has recently been shown to be incorrect by Bognanni (2021) and fixed by Carriero, et al. (2021). In this note we use the algorithm correction of Carriero, et al. (2021) to correct the estimates of CCM2020. Although the correction has some impact on the original results, the changes are small and the key findings of CCM2020 are upheld.   Read More

  • WP 16-22C | Corrigendum to: Measuring Uncertainty and Its Impact on the Economy

    Andrea Carriero Todd E. Clark Massimiliano Marcellino

    Original Paper: WP 16-22 | Revisions: WP 16-22R


    Carriero, Clark, and Marcellino (2018, CCM2018) used a large BVAR with a factor structure to stochastic volatility lo produce an estimate of time-varying macroeconomic and financial uncertainty and assess uncertainty's effects on the economy. The results in CCM2018 were based on an estimation algorithm that has recently shown to be incorrect by Bognanni (2021) and fixed by Carriero, et al. (2021). In this note we use the algorithm correction of Carriero, et a1. (2021) to correct the estimates of CCM2018. Although the correction has some impact on the original results, the changes are small and the key findings of CCM2018 are upheld.   Read More

  • WP 21-29 | Effects of Wildfire Destruction on Migration, Consumer Credit, and Financial Distress

    Kathryn McConnell Stephan D. Whitaker Elizabeth Fussell Jack DeWaard Katherine Curtis Kobie Price Lise St. Denis Jennifer Balch


    The scale of wildfire destruction has grown exponentially in recent years, destroying nearly 25,000 buildings in the United States during 2018 alone. However, there is still limited research exploring how wildfires affect migration patterns and household finances. In this study, we evaluate the effects of wildfire destruction on in-migration and out-migration probability at the Census tract level in the United States from 1999 to 2018. We then shift to the individual level and examine changes in homeownership, consumer credit usage, and financial distress among people whose neighborhood suffered damaging fires. We pair quarterly observations from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel with building destruction counts from the US National Incident Management System/Incident Command System database of wildfire events. Our findings show significantly heightened out-migration probability among tracts that experienced the most destructive wildfires, but no effect on in-migration probability. Among the consumer credit measures, we find a significant drop in homeownership among those treated by major fires. This is concentrated in people over the age of 60. Measures of credit distress, including delinquencies, bankruptcies, and foreclosures, improve rather than deteriorate after the fire, but the changes are not statistically significant. While wildfire effects on migration and borrowing are measurable, they are not yet as large as those observed following other natural disasters such as hurricanes.   Read More

  • WP 21-28 | Communicating Data Uncertainty: Multi-Wave Experimental Evidence for UK GDP

    Ana Galvão James Mitchell


    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 uncertainty information improves understanding. It encourages the public not to take estimates at face-value, but does not decrease trust in the data. Quantitative tools to communicate data uncertainty - notably intervals, density strips, and bell curves - are especially beneficial. They reduce dispersion of the public’s subjective probabilistic expectations of data uncertainty, improving alignment with objective estimates.   Read More

  • WP 20-12R | News and uncertainty about COVID-19: Survey evidence and short-run economic impact

    Alexander Dietrich Keith Kuester Gernot Müller Raphael Schoenle

    Original Paper: WP 20-12


    A tailor-made survey documents consumer perceptions of the U.S. economy’s response to a large shock: the advent of the COVID-19 pandemic. The survey ran at a daily frequency between March 2020 and July 2021. Consumer perceptions regarding output and inflation react rapidly. Uncertainty is pervasive. A business-cycle model calibrated to the consumer views provides an interpretation. The rise in household uncertainty amplifies the pandemic recession by a factor of three. Different perceptions about monetary policy can explain why consumers and professional forecasters agree on the recessionary impact, but have sharply divergent views about inflation.   Read More

  • WP 20-24R | Low Interest Rates and the Predictive Content of the Yield Curve

    Michael Bordo Joseph G. Haubrich

    Original Paper: WP 20-24


    Does the yield curve's ability to predict future output and recessions differ when interest rates and inflation are low, as in the current global environment? We explore the issue using historical data going back to the 19th century for the US. This paper is similar in spirit to Ramey and Zubairy (2018), who look at the government spending multiplier in times of low interest rates. If anything, the yield curve tends to predict output growth better in low interest rate environments, though this result is stronger for RGDP than for IP.   Read More

  • WP 20-26R | Average Inflation Targeting and Household Expectations

    Olivier Coibion Yuriy Gorodnichenko Edward S. Knotek II Raphael Schoenle

    Original Paper: WP 20-26


    Using a daily survey of U.S. households, we study how the Federal Reserve’s announcement of its new strategy of average inflation targeting affected households’ expectations. Starting with the day of the announcement, there is a very small uptick in the minority of households reporting that they had heard news about monetary policy relative to prior to the announcement, but this effect fades within a few days. Those hearing news about the announcement do not seem to have understood the announcement: they are no more likely to correctly identify the Fed’s new strategy than others, nor are their expectations different. When we provide randomly selected households with pertinent information about average inflation targeting, their expectations still do not change in a different way than when households are provided with information about traditional inflation targeting. Even one year after the announcement, U.S. households remain mostly unaware of the change in strategy or its implications.   Read More

  • WP 16-17C | Corrigendum to: Large Bayesian Vector Autoregressions with Stochastic Volatility and Non-Conjugate Priors

    Andrea Carriero Todd E. Clark Massimiliano Marcellino Joshua C.C. Chan

    Original Paper: WP 16-17


    The original algorithm contained a mistake that meant the conditional distributions used for the VAR’s coefficients were missing a piece of information. We propose a new algorithm that uses the same factorization but includes the missing term. The new, correct algorithm has the same computational complexity as the old, incorrect one (i.e., O(N4)), and therefore it still allows the estimation of large VARs.   Read More