Search Results
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 key objects of interest. This paper challenges this call. Although the prior distributions of individual impulse responses induced by the conventional method may be nonuniform, they typically do not drive the posteriors if one does not condition on the reduced-form parameters. Importantly, when the focus is on joint inference, the uniform prior over the ...
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 ...
Working Paper
Inference Based on Time-Varying SVARs Identified with Sign Restrictions
We propose an approach for Bayesian inference in time-varying SVARs identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form ...
Working Paper
Alternative Strategies: How Do They Work? How Might They Help?
Several structural developments in the U.S. economy—including lower neutral interest rates and a flatter Phillips curve—have challenged the ability of the current monetary policy framework to deliver on the Federal Open Market Committee’s (FOMC) dual-mandate goals. This paper explores whether makeup strategies, in which policymakers seek to stabilize average inflation around the inflation target over some horizon, could strengthen the FOMC’s ability to fulfill its dual mandate. The quantitative analysis discussed here suggests that credible makeup strategies may provide some moderate ...
Working Paper
The Macroeconomic Risks of Undesirably Low Inflation
This paper investigates the macroeconomic risks associated with undesirably low inflation using a medium-sized New Keynesian model. We consider different causes of persistently low inflation, including a downward shift in long-run inflation expectations, a fall in nominal wage growth, and a favorable supply-side shock. We show that the macroeconomic effects of persistently low inflation depend crucially on its underlying cause, as well as on the extent to which monetary policy is constrained by the zero lower bound. Finally, we discuss policy options to mitigate these effects.
Working Paper
Inference in Bayesian Proxy-SVARs
Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop an algorithm for exact finite sample inference in this class of time series models, commonly known as Proxy-SVARs. Our algorithm makes independent draws from any posterior distribution over the structural parameterization of a Proxy-SVAR. Our approach allows researchers to simultaneously use proxies and traditional zero and sign restrictions to identify structural shocks. We illustrate our methods with two applications. In particular, we show how to generalize the ...
Journal Article
Tracking Business Conditions in Delaware
To meet the need for a gauge of current regional conditions at high frequency, we have built a real-time daily index to monitor business conditions in Delaware. What are the current conditions in the First State? How have these conditions evolved since the 1990s?
Working Paper
Inference Based On Time-Varying SVARs Identified with Time Restrictions
We propose an approach for Bayesian inference in time-varying structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant time-varying SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to orthogonal transformations of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of ...
Working Paper
The Systematic Component of Monetary Policy in SVARs: An Agnostic Identification Procedure
Following Leeper, Sims, and Zha (1996), we identify monetary policy shocks in SVARs by restricting the systematic component of monetary policy. In particular, we impose sign and zero restrictions only on the monetary policy equation. Since we do not restrict the response of output to a monetary policy shock, we are agnostic in Uhlig's (2005) sense. But, in contrast to Uhlig (2005), our results support the conventional view that a monetary policy shock leads to a decline in output. Hence, our results show that the contractionary effects of monetary policy shocks do not hinge on questionable ...
Working Paper
Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs
We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely on a Markov chain Monte Carlo to sample from the posterior distribution. We show how to use the posterior simulation outputs as inputs for exercises in causality assessment. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. Our estimated time-varying-parameters SIRD model ...