NBER Working Papers by Edward P. Herbst

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Working Papers

May 2017Tempered Particle Filtering
with Frank Schorfheide: w23448
The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter, this distribution is generated by the state-transition equation. While straightforward to implement, the practical performance is often poor. We develop a self-tuning particle filter in which the proposal distribution is constructed adaptively through a sequence of Monte Carlo steps. Intuitively, we start from a measurement error distribution with an inflated variance, and then gradually reduce the variance to its nominal level in a sequence of tempering steps. We show that the filter generates an unbiased and consistent approximation of the likelihood function. Holding the ru...
October 2014Effective Monetary Policy Strategies in New Keynesian Models: A Re-examination
with Hess Chung, Michael T. Kiley: w20611
We explore the importance of the nature of nominal price and wage adjustment for the design of effective monetary policy strategies, especially at the zero lower bound. Our analysis suggests that sticky-price and sticky-information models fit standard macroeconomic time series comparably well. However, the model with information rigidity responds differently to anticipated shocks and persistent zero-lower bound episodes - to a degree important for monetary policy and for understanding the effects of fundamental disturbances when monetary policy cannot adjust. These differences may be important for understanding other policy issues as well, such as fiscal multipliers. Despite these differences, many aspects of effective policy strategy are common across the two models: In particular, highly...

Published: Hess Chung & Edward Herbst & Michael T. Kiley, 2015. "Effective Monetary Policy Strategies in New Keynesian Models: A Reexamination," NBER Macroeconomics Annual, University of Chicago Press, vol. 29(1), pages 289 - 344. citation courtesy of

June 2013Sequential Monte Carlo Sampling for DSGE Models
with Frank Schorfheide: w19152
We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples consisting of an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohé and Uribe's (2012) news shock model we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely-used random walk Metropolis- Hastings algorithm. We find that a more diffuse prior for the Smets and Wouters (2007) model improves its marginal data density and that a slight modification of the prior for the news shock model leads to drastic changes in the posterior inference about the im...

Published: Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November. citation courtesy of

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