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Working Papers

Working Papers

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


    Andrea Carriero Todd E. Clark Massimiliano Marcellino Elmar Mertens

    Abstract

    We measure the effects of the COVID-19 outbreak on macroeconomic and financial uncertainty, and we assess the consequences of the latter 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, in addition to idiosyncratic components. 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. We also consider an extended version of the model, based on a latent state approach to accommodating outliers in volatility, to reduce the influence of extreme observations from the COVID period. The estimates we obtain yield very large increases in macroeconomic and financial uncertainty over the course of the COVID-19 period. These increases have contributed to the downturn in economic and financial conditions, but with both models, 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 20-33 | Even Keel and the Great Inflation


    Victoria Consolvo Owen F. Humpage Sanchita Mukherjee

    Abstract

    During the early part of the Great Inflation (1965-1975), the Federal Reserve undertook even-keel operations to assist the US Treasury’s coupon security sales. Accordingly, the central bank delayed any tightening of monetary policy and permanently injected reserves into the banking system. Using real-time Taylor-type and McCallum-like reaction functions, we show that the Fed routinely undertook these operations only when it was otherwise tightening monetary policy. Using a quantity-equation framework, we show that the Federal Reserve’s even-keel actions added approximately one percentage point to the overall 5.1 percent average annual inflation rate over these years.   Read More

  • WP 20-31 | Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach


    Edward S. Knotek II Saeed Zaman

    Abstract

    We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework: (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. Together these features provide density nowcasts that can accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our framework using high-frequency real-time data over the period 2000-2015.   Read More

  • WP 15-33R | Industrial Composition and Educational Intergenerational Mobility


    Stephan D. Whitaker

    Original Paper: WP 15-33

    Abstract

    Using the National Longitudinal Surveys of Youth (NLSY), this article examines the influence of a region’s industrial composition on the educational attainment of children raised by parents who do not have college degrees. The NLSY’s geo-coded panel allows for precise measurements of the local industries that shaped the parents’ employment opportunities and the labor market that the children directly observed. For cohorts finishing school in the 1990s and early 2000s, concentrations of manufacturing are positively associated with both high school and college attainment. Concentrations of college-degree intensive industries are positively associated with college attainment. I investigate several potential mechanisms that could relate the industrial composition to educational attainment, including returns to education, opportunity costs, parental inputs, community resources, and information.   Read More

  • WP 20-30 | Fireside Chats: Communication and Consumers’ Expectations in the Great Depression


    Mathieu Pedemonte

    Abstract

    This paper shows how policy announcements can be used to manage expectations and have a role as a policy tool. Using regional variation in radio exposure, I evaluate the impact of President Franklin D. Roosevelt’s 1935 Fireside Chat, in which he showcased the introduction of important social policies, establishing a new cycle of the New Deal. I document that cities with higher exposure to the announcement exhibited a significant increase in spending on durable goods. I provide evidence that this result is not driven by wealth or other potentially confounding variables. The estimated effect is consistent with changes in expectations toward the policies announced. This paper shows the power of communication as a policy tool in affecting economic activity.   Read More

  • WP 20-29 | Quantitative Easing and Direct Lending in Response to the COVID-19 Crisis


    Filippo Occhino

    Abstract

    When the COVID-19 crisis hit the economy in 2020, the Federal Reserve responded with numerous programs designed to prevent a collapse in bank credit and firms’ available funds. I develop a dynamic general equilibrium model to study how these programs work and to evaluate their effectiveness. In the model, quantitative easing works through three channels: the expansion of bank reserves lowers a liquidity premium, the purchase of assets lowers a volatility risk premium, and the economic stimulus lowers a credit risk premium. Since bank reserves are currently larger than in the past, the liquidity premium channel is weaker, and quantitative easing is less effective. Direct lending to firms at a market rate is also less effective. Direct lending to firms at a subsidized rate can be more stimulative than quantitative easing, provided that it lowers firms’ marginal borrowing rate and user cost of capital.   Read More

  • WP 20-28 | On the Importance of Household versus Firm Credit Frictions in the Great Recession


    Patrick Kehoe Pierlauro Lopez Virgiliu Midrigan Elena Pastorino

    Abstract

    Although a credit tightening is commonly recognized as a key determinant of the Great Recession, to date, it is unclear whether a worsening of credit conditions faced by households or by firms was most responsible for the downturn. Some studies have suggested that the household-side credit channel is quantitatively the most important one. Many others contend that the firm-side channel played a crucial role. We propose a model in which both channels are present and explicitly formalized. Our analysis indicates that the household-side credit channel is quantitatively more relevant than the firm-side credit channel. We then evaluate the relative benefits of a fixed-sized transfer to households and to firms that improves each group’s access to credit. We find that the effects of such a transfer on employment are substantially larger when the transfer targets households rather than firms. Hence, we provide theoretical and quantitative support to the view that the employment decline during the Great Recession would have been less severe if instead of focusing on easing firms’ access to credit, the government had expended an equal amount of resources to alleviate households’ credit constraints. This paper has been published: https://doi.org/10.1016/j.red.2020.06.006.   Read More

  • WP 20-13R2 | Nowcasting Tail Risks to Economic Activity with Many Indicators


    Andrea Carriero Todd E. Clark Massimiliano Marcellino

    Original Paper: WP 20-13R

    Abstract

    This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, as well as classical and Bayesian quantile regressions) and also different methods for data reduction (either forecasts from models that incorporate data reduction or the combination of forecasts from smaller models). Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making additional data available, with monthly data more important to accuracy than weekly data. Accuracy also typically improves with the use of financial indicators in addition to a base set of macroeconomic indicators. The better-performing models or methods include the Bayesian regression model with stochastic volatility, Bayesian quantile regression, some approaches to data reduction that make use of factors, and forecast averaging. In contrast, simple quantile regression performs relatively poorly.   Read More

  • WP 20-02R | Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions


    Andrea Carriero Todd E. Clark Massimiliano Marcellino

    Original Paper: WP 20-02

    Abstract

    A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. We note that this pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and variances (up). Building on that insight, we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification with a common factor in volatility that enters the VAR’s conditional mean. Even though the one-step-ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, the model estimates yield more time variation in downside risk as compared to upside risk. Results from the model that includes a volatility factor in the conditional mean and thereby allows for asymmetries in conditional distributions are very similar. Our paper also extends the recent literature by formally evaluating the accuracy of tail risk forecasts and assessing the performance of Bayesian quantile regression, as well as our Bayesian VARs, in this context. Overall, the BVAR models perform comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs’ established performance for forecasting and structural analysis.   Read More

  • WP 20-27 | No-Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates


    Andrea Carriero Todd E. Clark Massimiliano Marcellino

    Abstract

    We derive a Bayesian prior from a no-arbitrage affine term structure model and use it to estimate the coefficients of a vector autoregression of a panel of government bond yields, specifying a common time-varying volatility for the disturbances. Results based on US data show that this method improves the precision of both point and density forecasts of the term structure of government bond yields, compared to a fully fledged term structure model with time-varying volatility and to a no-change random walk forecast. Further analysis reveals that the approach might work better than an exact term structure model because it relaxes the requirements that yields obey a strict factor structure and that the factors follow a Markov process. Instead, the cross-equation no-arbitrage restrictions on the factor loadings play a marginal role in producing forecasting gains.   Read More