Data Rich Dsge And Dynamic Factor Models
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Author | : Mr.Maxym Kryshko |
Publisher | : International Monetary Fund |
Total Pages | : 51 |
Release | : 2011-09-01 |
Genre | : Business & Economics |
ISBN | : 1463903499 |
Download Data-Rich DSGE and Dynamic Factor Models Book in PDF, Epub and Kindle
Dynamic factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics. The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common unobserved factors. Similarly, the dynamics in DSGE models are often governed by a handful of state variables and exogenous processes such as preference and/or technology shocks. Boivin and Giannoni(2006) combine a DSGE and a factor model into a data-rich DSGE model, in which DSGE states are factors and factor dynamics are subject to DSGE model implied restrictions. We compare a data-richDSGE model with a standard New Keynesian core to an empirical dynamic factor model by estimating both on a rich panel of U.S. macroeconomic and financial data compiled by Stock and Watson (2008).We find that the spaces spanned by the empirical factors and by the data-rich DSGE model states are very close. This proximity allows us to propagate monetary policy and technology innovations in an otherwise non-structural dynamic factor model to obtain predictions for many more series than just a handful of traditional macro variables, including measures of real activity, price indices, labor market indicators, interest rate spreads, money and credit stocks, and exchange rates.
Author | : Siem Jan Koopman |
Publisher | : Emerald Group Publishing |
Total Pages | : 685 |
Release | : 2016-01-08 |
Genre | : Business & Economics |
ISBN | : 1785603523 |
Download Dynamic Factor Models Book in PDF, Epub and Kindle
This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.
Author | : Jean Boivin |
Publisher | : |
Total Pages | : 62 |
Release | : 2006 |
Genre | : Economics |
ISBN | : |
Download Dynamic Stochastic General Equilibrium Models in a Data-rich Environment Book in PDF, Epub and Kindle
Author | : Mr.Maxym Kryshko |
Publisher | : International Monetary Fund |
Total Pages | : 62 |
Release | : 2011-09-01 |
Genre | : Business & Economics |
ISBN | : 1463904215 |
Download Bayesian Dynamic Factor Analysis of a Simple Monetary DSGE Model Book in PDF, Epub and Kindle
When estimating DSGE models, the number of observable economic variables is usually kept small, and it is conveniently assumed that DSGE model variables are perfectly measured by a single data series. Building upon Boivin and Giannoni (2006), we relax these two assumptions and estimate a fairly simple monetary DSGE model on a richer data set. Using post-1983 U.S.data on real output, inflation, nominal interest rates, measures of inverse money velocity, and a large panel of informational series, we compare the data-rich DSGE model with the regular - few observables, perfect measurement - DSGE model in terms of deep parameter estimates, propagation of monetary policy and technology shocks and sources of business cycle fluctuations. We document that the data-rich DSGE model generates a higher implied duration of Calvo price contracts and a lower slope of the New Keynesian Phillips curve. To reduce the computational costs of the likelihood-based estimation, we employed a novel speedup as in Jungbacker and Koopman (2008) and achieved the time savings of 60 percent.
Author | : Mario Forni |
Publisher | : |
Total Pages | : 38 |
Release | : 2022 |
Genre | : Econometric models |
ISBN | : |
Download Validating DSGE Models Through Dynamic Factor Models Book in PDF, Epub and Kindle
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 | : Luca Di Bonaventura |
Publisher | : |
Total Pages | : 0 |
Release | : 2018 |
Genre | : Economic forecasting |
ISBN | : |
Download The Forcasting Performance of Dynamic Factor Models with Vintage Data Book in PDF, Epub and Kindle
Author | : Gerhard Rünstler |
Publisher | : |
Total Pages | : 0 |
Release | : 2016 |
Genre | : |
ISBN | : |
Download On the Design of Data Sets for Forecasting with Dynamic Factor Models Book in PDF, Epub and Kindle
Author | : Paolo Andreini |
Publisher | : |
Total Pages | : 0 |
Release | : 2023 |
Genre | : |
ISBN | : |
Download Deep Dynamic Factor Models Book in PDF, Epub and Kindle
Author | : Caterina Schiavoni |
Publisher | : |
Total Pages | : |
Release | : 2019 |
Genre | : |
ISBN | : |
Download A Dynamic Factor Model Approach to Incorporate Big Data in State Space Models for Official Statistics Book in PDF, Epub and Kindle
Author | : Fabio Della Marra |
Publisher | : |
Total Pages | : 21 |
Release | : 2017 |
Genre | : |
ISBN | : |
Download A Forecasting Performance Comparison of Dynamic Factor Models Based on Static and Dynamic Methods Book in PDF, Epub and Kindle
We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson. The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin. The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni. The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in-sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to, but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.