Search Results
Working Paper
Identification Using Higher-Order Moments Restrictions
We exploit inequality restrictions on higher-order moments of the distribution of structural shocks to sharpen their identification. We show that these constraints can be treated as necessary conditions and used to shrink the set of admissible rotations. We illustrate the usefulness of this approach showing, by simulations, how it can dramatically improve the identification of monetary policy shocks when combined with widely used sign-restriction schemes. We then apply our methodology to two empirical questions: the effects of monetary policy shocks in the U.S. and the effects of sovereign ...
Working Paper
Measuring Labor Supply and Demand Shocks during COVID-19
We measure labor demand and supply shocks at the sector level around the COVID-19 outbreak by estimating a Bayesian structural vector autoregression on monthly statistics of hours worked and real wages. Most sectors were subject to historically large negative labor supply and demand shocks in March and April, with substantial heterogeneity in the size of shocks across sectors. Our estimates suggest that two-thirds of the drop in the aggregate growth rate of hours in March and April 2020 are attributable to labor supply. We validate our estimates of supply shocks by showing that they are ...
Working Paper
Measuring Sectoral Supply and Demand Shocks during COVID-19
We measure labor demand and supply shocks at the sector level around the COVID-19 outbreak by estimating a Bayesian structural vector autoregression on monthly statistics of hours worked and real wages. Our estimates suggest that two-thirds of the 16.24 percentage point drop in the growth rate of hours worked in April 2020 are attributable to supply. Most sectors were subject to historically large negative labor supply and demand shocks in March and April, but there is substantial heterogeneity in the size of shocks across sectors. We show that our estimates of supply shocks are correlated ...
Working Paper
Estimating Hysteresis Effects
In this paper, we identify demand shocks that can have a permanent effect on output through hysteresis effects. We call these shocks permanent demand shocks. They are found to be quantitatively important in the United States, in particular when the sample includes the Great Recession. Recessions driven by permanent demand shocks lead to a permanent decline in employment and investment, although output per worker is largely unaffected. We find strong evidence that hysteresis transmits through a rise in long-term unemployment and a decline in labor force participation and disproportionately ...
Working Paper
Measuring Sectoral Supply and Demand Shocks during COVID-19
We measure labor demand and supply shocks at the sector level around the COVID-19 outbreak, by estimating a Bayesian structural vector autoregression on monthly statistics of hours worked and real wages and applying the methodology proposed by Baumeister and Hamilton (2015). Our estimates suggest that two-thirds of the 16.24 percentage point drop in the growth rate of hours worked in April 2020 are attributable to supply. Most sectors were subject to historically large negative labor supply and demand shocks in March and April 2020, but there is substantial heterogeneity in the size of these ...
Working Paper
Uniform Priors for Impulse Responses
There has been a call for caution when using the conventional method for Bayesian inference in set-identified structural vector autoregressions on the grounds that the uniform prior over the set of orthogonal matrices could be nonuniform for individual impulse responses or other quantity of interest. This paper challenges this call by formally showing that, when the focus is on joint inference, the uniform prior over the set of orthogonal matrices is not only sufficient but also necessary for inference based on a uniform joint prior distribution over the identified set for the vector of ...
Working Paper
Sowing the Seeds of Financial Imbalances: The Role of Macroeconomic Performance
The seeds of financial imbalances are sown in times of buoyant economic growth. We study the link between macroeconomic performance and financial imbalances, focusing on the experience of the United States since the 1960s. We first follow a narrative approach to review historical episodes of significant financial imbalances and find that the onset of financial disturbances typically occurs when the economy is running hot. We then look for evidence of a statistical link between measures of macroeconomic conditions and financial imbalances. In our in-sample analysis, we find that strong ...
Working Paper
Inference Based on SVARs Identified with Sign and Zero Restrictions: Theory and Applications
In this paper, we develop algorithms to independently draw from a family of conjugate posterior distributions over the structural parameterization when sign and zero restrictions are used to identify SVARs. We call this family of conjugate posterior distributions normal-generalized-normal. Our algorithms draw from a conjugate uniform-normal-inverse-Wishart posterior over the orthogonal reduced-form parameterization and transform the draws into the structural parameterization; this transformation induces a normal-generalized-normal posterior distribution over the structural parameterization. ...
Working Paper
Higher-order Moment Inequality Restrictions for SVARs
We introduce a method that exploits some non-Gaussian features of structural shocks to identify structural vector autoregression (SVAR) models. More specifically, we propose combining inequality restrictions on the higher-order moments of the structural shocks of interest with other set-identifying constraints, typically sign restrictions. We illustrate how, in both large and small sample settings, higher-order moment restrictions considerably narrow the identification of monetary policy shocks compared with what is obtained with minimal sign restrictions typically used in the SVAR ...
Working Paper
A Gibbs Sampler for Efficient Bayesian Inference in Sign-Identified SVARs
We develop a new algorithm for inference based on structural vector autoregressions (SVARs) identified with sign restrictions. The key insight of our algorithm is to break from the accept-reject tradition associated with sign-identified SVARs. We show that embedding an elliptical slice sampling within a Gibbs sampler approach can deliver dramatic gains in speed and turn previously infeasible applications into feasible ones. We provide a tractable example to illustrate the power of the elliptical slice sampling applied to sign-identified SVARs. We demonstrate the usefulness of our algorithm by ...