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Working Paper
Likelihood Evaluation of Models with Occasionally Binding Constraints
Applied researchers interested in estimating key parameters of DSGE models face an array of choices regarding numerical solution and estimation methods. We focus on the likelihood evaluation of models with occasionally binding constraints. We document how solution approximation errors and likelihood misspecification, related to the treatment of measurement errors, can interact and compound each other.
Working Paper
Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints
We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly reduces ...
Working Paper
Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints
We develop an algorithm to construct approximate decision rules that are piecewise-linear and continuous for DSGE models with an occasionally binding constraint. The functional form of the decision rules allows us to derive a conditionally optimal particle filter (COPF) for the evaluation of the likelihood function that exploits the structure of the solution. We document the accuracy of the likelihood approximation and embed it into a particle Markov chain Monte Carlo algorithm to conduct Bayesian estimation. Compared with a standard bootstrap particle filter, the COPF significantly ...
Discussion Paper
Forecasting During the COVID-19 Pandemic: A Structural Analysis of Downside Risk
The global collapse in economic activity triggered by individual and policy-mandated responses to the spread of COVID-19 is unprecedented both in scale and origin. At the time of writing, U.S. GDP is expected by professional forecasters to contract a staggering 6 percent over the course of 2020 driven by its 32 percent collapse in the second quarter (measured at an annual rate).
Working Paper
Macroeconomic Dynamics Near the ZLB : A Tale of Two Countries
We compute a sunspot equilibrium in an estimated small-scale New Keynesian model with a zero lower bound (ZLB) constraint on nominal interest rates and a full set of stochastic fundamental shocks. In this equilibrium a sunspot shock can move the economy from a regime in which inflation is close to the central bank's target to a regime in which the central bank misses its target, inflation rates are negative, and interest rates are close to zero with high probability. A nonlinear filter is used to examine whether the U.S. in the aftermath of the Great Recession and Japan in the late 1990s ...
Working Paper
Understanding Persistent Stagnation
We theoretically explore long-run stagnation at the zero lower bound in a representative agent framework. We analytically compare expectations-driven stagnation to a secular stagnation episode and find contrasting policy implications for changes in government spending, supply shocks and neo-Fisherian policies. On the other hand, a minimum wage policy is expansionary and robust to the source of stagnation. Using Bayesian methods, we estimate a DSGE model that can accommodate two competing hypotheses of long-run stagnation in Japan. We document that equilibrium selection under indeterminacy ...
Discussion Paper
Monitoring the World Economy: A Global Conditions Index
In this note we present a Global Conditions Index (GCI), a real-time measure of the health of the global economy constructed using a small set of world economic variables.