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Keywords:MCMC 

Working Paper
Estimating Dynamic Macroeconomic Models : How Informative Are the Data?

Central banks have long used dynamic stochastic general equilibrium (DSGE) models, which are typically estimated using Bayesian techniques, to inform key policy decisions. This paper offers an empirical strategy that quantifies the information content of the data relative to that of the prior distribution. Using an off-the-shelf DSGE model applied to quarterly Euro Area data from 1970:3 to 2009:4, we show how Monte Carlo simulations can reveal parameters for which the model's structure obscures identification. By integrating out components of the likelihood function and conducting a Bayesian ...
International Finance Discussion Papers , Paper 1175

Working Paper
Bayesian Estimation of Time-Changed Default Intensity Models

We estimate a reduced-form model of credit risk that incorporates stochastic volatility in default intensity via stochastic time-change. Our Bayesian MCMC estimation method overcomes nonlinearity in the measurement equation and state-dependent volatility in the state equation. We implement on firm-level time-series of CDS spreads, and find strong in-sample evidence of stochastic volatility in this market. Relative to the widely-used CIR model for the default intensity, we find that stochastic time-change offers modest benefit in fitting the cross-section of CDS spreads at each point in time, ...
Finance and Economics Discussion Series , Paper 2015-2

Working Paper
High-Dimensional DSGE Models: Pointers on Prior, Estimation, Comparison, and Prediction∗

Working Papers , Paper 20-35

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Beltran, Daniel O. 1 items

Chib, Siddhartha 1 items

Draper, David 1 items

Gordy, Michael B. 1 items

Shin, Minchul 1 items

Szerszen, Pawel J. 1 items

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C11 3 items

C15 2 items

C18 1 items

C32 1 items

C58 1 items

E37 1 items

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