In this paper, we present an alternative strategy for estimation of DSGE models when data is available at di¤erent time intervals. Our method is based on a data augmentation technique within Bayesian estimation of structural models and allows us to jointly use data at di¤erent frequencies. The bene…ts achieved via this methodology will be twofold, resolution of time aggregation bias and identi…...

The identification of reduced-form VAR model had been the subject of numerous debates in the literature. Different sets of identifying assumptions can lead to very different conclusions in the policy debate. This paper proposes a theoretical consistent identification strategy using restrictions implied by a DSGE model. Monte Carlo simulations suggest the proposed identification strategy is succ...

4 A method to estimate DSGE models using the raw data is proposed. The approach 5 links the observables to the model counterparts via a ‡exible speci…cation which does 6 not require the model-based component to be solely located at business cycle frequen7 cies, allows the non model-based component to take various time series patterns, and 8 permits model misspeci…cation. Applying standard data ...

* This paper grew out of the Panel Discussion of the workshop " SDGE Models and their use in monetary policy " , held at the European Central Bank, June 5-6, 2001. I would like to thank the participants of the TSM conference in Touluse for comments and suggestions.

We review the current state of estimation dynamic stochastic general equilibrium (DSGE) models. After introducing a framework for dealing with DSGE models, state-space representation, we discuss how to evaluate moments or likelihood function implied by such structure. discuss, in varying degrees detail, recent advances field, as tempered particle filter, approximated Bayesian computation, Hamil...

Over the past 15 years there has been remarkable progress in the specification and estimation of dynamic stochastic general equilibrium (DSGE) models. Central banks in developed and emerging market economies have become increasingly interested in their usefulness for policy analysis and forecasting. This paper reviews some issues and challenges surrounding the use of these models at central ban...

Recent literature points out that key variables such as aggregate productivity and in‡ation display long memory dynamics. We study the implications of this high degree of persistence on the estimation of Dynamic Stochastic General Equilibrium (DSGE) models. We …rst show that long memory data can produce substantial bias in the deep parameter estimates when a standard Kalman Filter-MLE procedure...

This note documents a DSGE model of Climate Change. I extend the NK with geophysical variables, such as greenhouse gas emissions, carbon cycle, radiative forcing, and climate change. In this model, specify five different policy regimes: no policy, cap, intensive, tax, mandate.

Modern policy analysis makes extensive use of dynamic stochastic general equilibrium (DSGE) models. These models differ significantly from earlier generations of large-scale econometric models. I review what I see as major progress in the ability of economists to conduct model-based policy analysis. This progress has come through the evolution in the types of models being used and in a refineme...

Rational expectations are at the heart of the DSGE models maintained by central banks. A key equation which governs the evolution of prices in those models is the New Keynesian Phillips (NKPC) curve, in which today‟s rate of inflation is linked to expected future inflation. Expected future inflation is in turn modeled using rational expectations, which operationally means that forecast errors a...