SSRN Author: Edward HerbstEdward Herbst SSRN Content
http://www.ssrn.com/author=1724364
http://www.ssrn.com/rss/en-usSat, 03 Jun 2017 02:45:53 GMTeditor@ssrn.com (Editor)Sat, 03 Jun 2017 02:45:53 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0New: Evaluating DSGE Model Forecasts of ComovementsThis paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. We construct posterior predictive checks to evaluate conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. We find that the additional features incorporated into the Smets-Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates.
http://www.ssrn.com/abstract=2976622
http://www.ssrn.com/1596462.htmlFri, 02 Jun 2017 08:43:40 GMTNew: Using the "Chandrasekhar Recursions" for Likelihood Evaluation of DSGE ModelsIn likelihood-based estimation of linearized Dynamic Stochastic General Equilibrium (DSGE) models, the evaluation of the Kalman Filter dominates the running time of the entire algorithm. In this paper, we revisit a set of simple recursions known as the "Chandrasekhar Recursions" developed by Morf (1974) and Morf, Sidhu, and Kalaith (1974) for evaluating the likelihood of a Linear Gaussian State Space System. We show that DSGE models are ideally suited for the use of these recursions, which work best when the number of states is much greater than the number of observables. In several examples, we show that there are substantial benefits to using the recursions, with likelihood evaluation up to five times faster. This gain is especially pronounced in light of the trivial implementation costs--no model modification is required. Moreover, the algorithm is complementary with other approaches.
http://www.ssrn.com/abstract=2976646
http://www.ssrn.com/1595283.htmlTue, 30 May 2017 09:15:39 GMTUpdate: The Empirical Implications of the Interest-Rate Lower BoundUsing Bayesian methods, we estimate a nonlinear DSGE model in which the interest-rate lower bound is occasionally binding. We quantify the size and nature of disturbances that pushed the U.S. economy to the lower bound in late 2008 as well as the contribution of the lower bound constraint to the resulting economic slump. We find that the interest-rate lower bound was a significant constraint on monetary policy that exacerbated the recession and inhibited the recovery, as our mean estimates imply that the zero lower bound (ZLB) accounted for about 25 percent of the sharp contraction in U.S. GDP that occurred in 2009 and an even larger fraction of the slow recovery that followed.<br/><i>New PDF Uploaded</i>
http://www.ssrn.com/abstract=2197515
http://www.ssrn.com/1563705.htmlSat, 04 Feb 2017 08:05:22 GMTNew: Tempered Particle FilteringThe 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 steps that we call tempering. We show that the filter generates an unbiased and consistent approximation of the likelihood function. Holding the run time fixed, our filter is substantially more accurate in two DSGE model applications than the bootstrap particle filter.
http://www.ssrn.com/abstract=2858991
http://www.ssrn.com/1538998.htmlWed, 26 Oct 2016 21:33:28 GMT