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Mr. Maxym Kryshko

dynamic factors that drive large U.S. macroeconomic panels – ranging from four to seven. The dynamics in DSGE models are also 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. They argue that the richer information coming from large macroeconomic and financial panels can provide better estimates of the DSGE states

Mr. Maxym Kryshko
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.
Mr. Maxym Kryshko
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.
Mr. Maxym Kryshko

I. I ntroduction When estimating dynamic stochastic general equilibrium (DSGE) models, the number of observable economic variables is usually kept small, and for convenience it is assumed that the model variables are perfectly measured by a single – often quite arbitrarily selected – data series. In this paper, we relax these two assumptions and estimate a version of the monetary DSGE model with a standard New Keynesian core on a richer data set. Building upon Boivin and data-rich DSGE model can be seen as a combination of a regular DSGE model and a

Mr. Maxym Kryshko

Front Matter Page IMF Institute Authorized for distribution by Alexandros Mourmouras Contents I. INTRODUCTION II. DATA-RICH DSGE MODEL A. R egular vs . D ata- R ich DSGE M odels B. E nvironment Households Final Good Firms Intermediate Goods Firms Monetary and Fiscal Policy Aggregation III. ECONOMETRIC METHODOLOGY A. E stimation of the D ata -R ich DSGE M odel B. S peed -U p : J ungbacker and K oopman 2008 IV. DATA AND TRANSFORMATIONS V. EMPIRICAL RESULTS A. P riors B. P osteriors : R

International Monetary Fund. Research Dept.

, Rodolphe Working Paper No. 11/214 Assessing Systemic Trade Interconnectedness—An Empirical Approach Errico, Luca; Massara, Alexander Working Paper No. 11/215 What Fuels the Boom Drives the Bust: Regulation and the Mortgage Crisis Dagher, Jihad; Fu, Ning Working Paper No. 11/216 Data-Rich DSGE and Dynamic Factor Models Kryshko, Maxym Working Paper No. 11/217 Effciency-Adjusted Public Capital and Growth Gupta, Sanjeev; Kangur, Alvar; Papageorgiou, Chris; Wane, Abdoul Aziz Working Paper No. 11/218 Growth Spillover Dynamics from

International Monetary Fund. Research Dept.
The Q&A in this issue features seven questions about Large Fiscal Consolidation Attempts in the Past and Implications for Policymakers Today (by Fuad Hasanov and Paolo Mauro). The research summaries are "Booms and Busts" (by Roberto Piazza) and " Did Export Diversification Soften the Impact of the Global Financial Crisis?" (by Rafael Romeu). The issue also provides details on visiting scholars at the IMF (mainly from September through December 2011), as well as recently published IMF Working Papers and Staff Discussion Notes.
Lien Laureys, Mr. Roland Meeks, and Boromeus Wanengkirtyo

policy lean against the wind?: An analysis based on a DSGE model with banking ,” Journal of Economic Dynamics and Control , Vol. 43 , pp. 146 – 174 . Gelain , Paolo , and Pelin Ilbas , 2017 , “ Monetary and macroprudential policies in an estimated model with financial intermediation ,” Journal of Economic Dynamics and Control , Vol. 78 , pp. 164 – 189 . Gelfer , Sacha , 2019 , “ Data-rich DSGE model forecasts of the great recession and its recovery ,” Review of Economic Dynamics , Vol. 32 , pp. 18 – 41 . Gertler , Mark , and

Mr. Jordi Gali Garreta and Mr. Pau Rabanal
Our answer: Not so well. We reached that conclusion after reviewing recent research on the role of technology as a source of economic fluctuations. The bulk of the evidence suggests a limited role for aggregate technology shocks, pointing instead to demand factors as the main force behind the strong positive comovement between output and labor input measures.
Mr. Jordi Gali Garreta and Mr. Pau Rabanal

in Smets and Wouters (2003a) in the context of a much richer DSGE model. In particular, those authors show that even in the presence of the substantial price and wage rigidities estimated for the U.S. economy, a positive I-shock causes output and labor input to increase simultaneously, in a way consistent with the Fisher (2003) VAR evidence. In fact, as shown in Smets and Wouters (2003a) , the qualitative pattern of the joint response of output and hours to an I-shock is not affected much when they simulate the model with all nominal rigidities turned off