Estimating Dsge Model Consistent Trends For Use In Forecasting
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Estimating Dsge Model Consistent Trends For Use In Forecasting
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Author : Jean-Philippe Cayen
language : en
Publisher:
Release Date : 2009
Estimating Dsge Model Consistent Trends For Use In Forecasting written by Jean-Philippe Cayen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Economic forecasting categories.
Estimating Dsge Model Consistent Trends For Use In Forecasting December 2009
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Author : Bank of Canada
language : en
Publisher:
Release Date : 2009
Estimating Dsge Model Consistent Trends For Use In Forecasting December 2009 written by Bank of Canada and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
Dsge Models In Macroeconomics
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Author : Nathan Balke
language : en
Publisher: Emerald Group Publishing
Release Date : 2012-11-29
Dsge Models In Macroeconomics written by Nathan Balke and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-29 with Business & Economics categories.
This volume of Advances in Econometrics contains articles that examine key topics in the modeling and estimation of dynamic stochastic general equilibrium (DSGE) models. Because DSGE models combine micro- and macroeconomic theory with formal econometric modeling and inference, over the past decade they have become an established framework for analy
Evaluating Uk Point And Density Forecasts From An Estimated Dsge Model
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Author :
language : en
Publisher:
Release Date : 2015
Evaluating Uk Point And Density Forecasts From An Estimated Dsge Model written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
How Useful Are Estimated Dsge Model Forecasts
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Author : Rochelle M. Edge
language : en
Publisher:
Release Date : 2011
How Useful Are Estimated Dsge Model Forecasts written by Rochelle M. Edge and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Estimating Dsge Models With Long Memory Dynamics
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Author : Gianluca Moretti
language : en
Publisher:
Release Date : 2008
Estimating Dsge Models With Long Memory Dynamics written by Gianluca Moretti and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Recent literature claims that key variables such as aggregate productivity and inflation 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 show that long memory data produce substantial bias in the deep parameter estimates when a standard Kalman Filter-MLE procedure is used. We propose a modification of the Kalman Filter to effectively deal with this problem. The augmented Kalman Filter can consistently estimate the model parameters as well as produce more accurate out-of-sample forecasts compared to the standard Kalman filter.
Estimation And Forecasting Using Mixed Frequency Dsge Models
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Author : Alexander Meyer-Gohde
language : de
Publisher:
Release Date : 2022
Estimation And Forecasting Using Mixed Frequency Dsge Models written by Alexander Meyer-Gohde and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
In this paper, we propose a new method to forecast macroeconomic variables that combines two existing approaches to mixed-frequency data in DSGE models. The first existing approach estimates the DSGE model in a quarterly frequency and uses higher frequency auxiliary data only for forecasting (see Giannone, Monti and Reichlin (2016)). The second method transforms a quarterly state space into a monthly frequency and applies, e.g., the Kalman filter when faced missing observations (see Foroni and Marcellino (2014)). Our algorithm combines the advantages of these two existing approaches, using the information from monthly auxiliary variables to inform in-between quarter DSGE estimates and forecasts. We compare our new method with the existing methods using simulated data from the textbook 3-equation New Keynesian model (see, e.g., Galí (2008)) and real-world data with the Smets and Wouters (2007) model. With the simulated data, our new method outperforms all other methods, including forecasts from the standard quarterly model. With real world data, incorporating auxiliary variables as in our method substantially decreases forecasting errors for recessions, but casting the model in a monthly frequency delivers better forecasts in normal times.
Consistent Variance Of The Laplace Type Estimators
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Author : Anna Kormilitsina
language : en
Publisher:
Release Date : 2016
Consistent Variance Of The Laplace Type Estimators written by Anna Kormilitsina and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
The Laplace-type estimator has become popular in applied macroeconomics, in particular for estimation of dynamic stochastic general equilibrium (DSGE) models. It is often obtained as the mean and variance of a parameter's quasi-posterior distribution, which is defined using a classical estimation objective. We demonstrate that the objective must be properly scaled; otherwise, arbitrarily small confidence intervals can be obtained if calculated directly from the quasi-posterior distribution. We estimate a standard DSGE model and find that scaling up the objective may be useful in estimation with problematic parameter identification. It this case, however, it is important to adjust the quasi-posterior variance to obtain valid confidence intervals.
Bayesian Estimation Of Dsge Models
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Author : Edward P. Herbst
language : en
Publisher: Princeton University Press
Release Date : 2015-12-29
Bayesian Estimation Of Dsge Models written by Edward P. Herbst and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-29 with Business & Economics categories.
Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. Bayesian Estimation of DSGE Models is essential reading for graduate students, academic researchers, and practitioners at policy institutions.
Online Estimation Of Dsge Models
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Author : Michael D. Cai
language : en
Publisher:
Release Date : 2020
Online Estimation Of Dsge Models written by Michael D. Cai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for "online" estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity of the predictive performance to changes in the prior distribution. We find that making priors less informative (compared to the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy.