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Author: Mario Forni Publisher: ISBN: Category : Econometric models Languages : en Pages : 38
Book Description
We urge the use of Structural Dynamic Factor Models (DFM) to validate and to guide the construction of Dynamic Stochastic General Equilibrium (DSGE) models. The main reason is that the log-linear solution of a DSGE model has a factor structure which ensures consistency between the representations of the two models. We assess, by means of a few simulations, the validity of SDFM as an empirical tool to complement DSGE analysis. Using a DSGE model as data generating process, the factor model provides very accurate estimates of the true impulse response functions. As an application, we validate a theory of TFP news and surprise shocks.
Author: Mario Forni Publisher: ISBN: Category : Econometric models Languages : en Pages : 38
Book Description
We urge the use of Structural Dynamic Factor Models (DFM) to validate and to guide the construction of Dynamic Stochastic General Equilibrium (DSGE) models. The main reason is that the log-linear solution of a DSGE model has a factor structure which ensures consistency between the representations of the two models. We assess, by means of a few simulations, the validity of SDFM as an empirical tool to complement DSGE analysis. Using a DSGE model as data generating process, the factor model provides very accurate estimates of the true impulse response functions. As an application, we validate a theory of TFP news and surprise shocks.
Author: Edward P. Herbst Publisher: Princeton University Press ISBN: 0691161089 Category : Business & Economics Languages : en Pages : 295
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: Siem Jan Koopman Publisher: ISBN: Category : Languages : en Pages : 45
Book Description
We consider the dynamic factor model where the loading matrix, the dynamic factors and the disturbances are treated as latent stochastic processes. We present empirical Bayes methods that enable the efficient shrinkage-based estimation of the loadings and the factors. We show that our estimates have lower quadratic loss compared to the standard maximum likelihood estimates. We investigate the methods in a Monte Carlo study where we document the finite sample properties. Finally, we present and discuss the results of an empirical study concerning the forecasting of U.S. macroeconomic time series using our empirical Bayes methods.
Author: Matteo Luciani Publisher: ISBN: Category : Languages : en Pages : 31
Book Description
In this paper I review the literature on Large-Dimensional Dynamic Factor Models for real-time applications. I first present the Dynamic Factor model, the implications of using large-dimensional databases, and the challenges of real-time applications. Then, I discuss how the literature has solved these problems, and I present numerous empirical applications that show the usefulness of these models in both constructing business cycle indicators, and predicting economic activity. Finally, I present two recent extensions of the Dynamic Factor model, one in a Bayesian and one in a non-stationary setting.
Author: Mr.Jaromir Benes Publisher: International Monetary Fund ISBN: 1475524986 Category : Business & Economics Languages : en Pages : 59
Book Description
This paper presents the theoretical structure of MAPMOD, a new IMF model designed to study vulnerabilities associated with excessive credit expansions, and to support macroprudential policy analysis. In MAPMOD, bank loans create purchasing power that facilitates adjustments in the real economy. But excessively large and risky loans can impair balance sheets and sow the seeds of a financial crisis. Banks respond to losses through higher spreads and rapid credit cutbacks, with adverse effects for the real economy. These features allow the model to capture the basic facts of financial cycles. A companion paper studies the simulation properties of MAPMOD.
Author: Mario Forni
Author: Mario Forni
Author: Jörg Breitung
Author: Paolo Andreini
Author: Jushan Bai
Author: Fabio Canova
Author: Edward P. Herbst
Author: Victor Bystrov
Author: Siem Jan Koopman
Author: Matteo Luciani
Author: Mr.Jaromir Benes