Comment: On Random Scan Gibbs Samplers

Comment on ``On Random Scan Gibbs Samplers'' [arXiv:0808.3852]Comment: Published in at http://dx.doi.org/10.1214/08-STS252B the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Estimating DSGE Models under Partial Information

Most DSGE models and methods make inappropriate asymmetric information assumptions. They assume that all economic agents have full access to measurement of all variables and past shocks, whereas the econometricians have no access to this. An alternative assumption is that there is symmetry, in that the information set available to both agents and econometricians is incomplete. The reality lies somewhere between the two, because agents are likely to be subject to idiosyncratic shocks which they can observe, but are unable to observe other agents’ idiosyncratic shocks, as well as being unable to observe certain economy-wide shocks; however such assumptions generally lead to models that have no closed-form solution. This research aims to compare the two alternatives - the asymmetric case,as commonly used in the literature, and the symmetric case, which uses the partial information solution of Pearlman et al. (1986) using standard EU datasets. We use Bayesian MCMC methods, with log-likelihoods accounting for partial information.The work then extends the data to allow for a greater variety of measurements, and evaluates the effect on estimates, along the lines of work by Boivin and Giannoni (2005).partial information, DSGE models, Bayesian maximum likelihood.

Endogenous Persistence in an Estimated DSGE Model under Imperfect Information

We provide a tool for estimating DSGE models by BayesianMaximum-likelihood methods under very general information assumptions. This framework is applied to a New Keynesian model where we compare the standard approach, that assumes an informational asymmetry between private agents and the econometrician, with an assumption of informational symmetry. For the former, private agents observe all state variables including shocks, whereas the econometrician uses only data for output, inflation and interest rates. For the latter both agents have the same imperfect information set and this corresponds to what we term the 'informational consistency principle'. We first assume rational expectations and then generalize the model to allow some households and firms to form expectations adaptively. We find that in terms of model posterior probabilities, impulse responses, second moments and autocorrelations, the assumption of informational symmetry by rational agents significantly improves the model fit. We also find qualified empirical support for the heterogenous expectations model. JEL Classification: C11, C52, E12, E32.Imperfect Information, DSGE Model, Rational versus Adaptive Expectations, Bayesian Estimation

Endogenous Persistence in an Estimated DSGE Model under Imperfect Information

We provide a tool for estimating DSGE models by Bayesian Maximum-likelihood meth?ods under very general information assumptions. This framework is applied to a New Keynesian model where we compare the standard approach, that assumes an informa?tional asymmetry between private agents and the econometrician, with an assumption of informational symmetry. For the former, private agents observe all state variables including shocks, whereas the econometrician uses only data for output, inflation and interest rates. For the latter both agents have the same imperfect information set and this corresponds to what we term the ¡®informational consistency principle¡¯. We first assume rational expectations and then generalize the model to allow some households and firms to form expectations adaptively. We find that in terms of model posterior probabilities, impulse responses, second moments and autocorrelations, the assumption of informational symmetry by rational agents significantly improves the model fit. We also find qualified empirical support for the heterogenous expectations model.Imperfect Information, DSGE Model, Rational versus Adaptive Expectations, Bayesian Estimation.