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Author: Bank of Canada
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Author: Bank of Canada
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Author: Jean-Philippe Cayen
Publisher:
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Category : Economic forecasting
Languages : en
Pages :
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Author: Nathan Balke
Publisher: Emerald Group Publishing
ISBN: 1781903069
Category : Business & Economics
Languages : en
Pages : 480
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Book Description
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
Author: Alexander Meyer-Gohde
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Languages : de
Pages : 0
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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.
Author: Gianluca Moretti
Publisher:
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Category :
Languages : en
Pages : 0
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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.
Author: Anna Kormilitsina
Publisher:
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Category :
Languages : en
Pages : 0
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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.
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Category :
Languages : en
Pages :
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Author: Rochelle M. Edge
Publisher:
ISBN:
Category :
Languages : en
Pages : 67
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Author: Edward P. Herbst
Publisher: Princeton University Press
ISBN: 0691161089
Category : Business & Economics
Languages : en
Pages : 295
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Book Description
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.
Author: Michael P. Clements
Publisher: OUP USA
ISBN: 0195398645
Category : Business & Economics
Languages : en
Pages : 732
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Book Description
Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
