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Jel Classification:C5 

Working Paper
On the Aggregation of Probability Assessments: Regularized Mixtures of Predictive Densities for Eurozone Inflation and Real Interest Rates

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters (even the ex post best forecaster) are outperformed by our regularized mixtures. From the Great Recession onward, the optimal regularization tends to move density forecasts’ probability mass from the centers to the tails, correcting for overconfidence.
Working Papers , Paper 21-06

Working Paper
Monetary Policy, Real Activity, and Credit Spreads : Evidence from Bayesian Proxy SVARs

This paper studies the interaction between monetary policy, financial markets, and the real economy. We develop a Bayesian framework to estimate proxy structural vector autoregressions (SVARs) in which monetary policy shocks are identified by exploiting the information contained in high frequency data. For the Great Moderation period, we find that monetary policy shocks are key drivers of fluctuations in industrial output and corporate credit spreads, explaining about 20 percent of the volatility of these variables. Central to this result is a systematic component of monetary policy ...
Finance and Economics Discussion Series , Paper 2016-049

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 ...
Working Papers , Paper 20-13

Working Paper
A Unified Framework to Estimate Macroeconomic Stars

We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" -- i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey expectations ...
Working Papers , Paper 21-23R

Working Paper
Term Structure Modeling with Supply Factors and the Federal Reserve's Large Scale Asset Purchase Programs

This paper estimates an arbitrage-free term structure model with both observable yield factors and Treasury and Agency MBS supply factors, and uses it to evaluate the term premium effects of the Federal Reserve's large-scale asset purchase programs. Our estimates show that the first and the second large-scale asset purchase programs and the maturity extension program jointly reduced the 10-year Treasury yield by about 100 basis points.
Finance and Economics Discussion Series , Paper 2014-07

Working Paper
Does Realized Volatility Help Bond Yield Density Prediction?

We suggest using "realized volatility" as a volatility proxy to aid in model-based multivariate bond yield density forecasting. To do so, we develop a general estimation approach to incorporate volatility proxy information into dynamic factor models with stochastic volatility. The resulting model parameter estimates are highly efficient, which one hopes would translate into superior predictive performance. We explore this conjecture in the context of density prediction of U.S. bond yields by incorporating realized volatility into a dynamic Nelson-Siegel (DNS) model with stochastic ...
Finance and Economics Discussion Series , Paper 2015-115

Working Paper
The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes

We assess the causal impact of epidemic-induced lockdowns on health and macroeconomic outcomes and measure the trade-off between containing the spread of an epidemic and economic activity. To do so, we estimate an epidemiological model with time-varying parameters and use its output as information for estimating SVARs and LPs that quantify the causal effects of nonpharmaceutical policy interventions. We apply our approach to Belgian data for the COVID-19 epidemic during 2020. We find that additional government mandated mobility curtailments would have reduced deaths at a very small cost in ...
Working Papers , Paper 22-18

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 ...
International Finance Discussion Papers , Paper 1163

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 ...
Working Papers , Paper 21-18

Working Paper
A Unified Framework to Estimate Macroeconomic Stars

We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" — i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, the price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey ...
Working Papers , Paper 21-23

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