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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 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

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

Mr. Maxym Kryshko

Front Matter Page IMF Institute Authorized for distribution by Alexandros Mourmouras Contents i . introduction ii. two models A. D ynamic F actor M odel B. D ata -R ich DSGE M odel iii. econometric methodology A. E stimation of the D ata -R ich DSGE M odel B. E stimation of the D ynamic F actor M odel iv. data v. empirical analysis A. P riors and P osteriors B. E mpirical F actors and E stimated DSGE M odel States C. H ow W ell F actors T race D ata D. C omparing F actor S paces

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.
International Monetary Fund

identification and prioritizing key taxpayer segments; and, High quality security systems to help make the most of the automation process. III. E xchange R ate P olicy and M acroeconomic C osts in A zerbaijan: I nsights from a DSGE M odel 29 The inflation rate in Azerbaijan has been volatile during the past decade, reaching double-digit levels prior to the 2009financial crisis. The de facto stabilized exchange rate regime in Azerbaijan has not been fully effective in controlling inflation and money supply in the face of various shocks. The results from a

Mr. Jan Vlcek and Mr. Scott Roger

with financial features in use by central banks prior to the crisis as well as model developments since the crisis. The Section III then seeks to identify the main weaknesses in existing models and priorities for development in models to be used in forecasting and policy analysis. An appendix reviews the principal approaches to introducing financial frictions and modeling of banking sectors in DSGE models. II. M acrofinancial DSGE M odels in U se by C entral B anks 5. The standard macroeconomic models used by most advanced country central banks for

Mr. Shanaka J Peiris and Mr. Magnus Saxegaard

next section will outline the DSGE model in detail. Section III will briefly outline the estimation procedure and discuss the results of the estimation. This will be followed by an evaluation of the response of the model to aid and technology shocks. The analysis will then be extended by considering the performance of different monetary policy rules—including inflation and exchange rate targeting—when the economy is subject to a more larger and more realistic number of shocks. Section V concludes. II. DSGE M odel In this paper, we develop a macroeconomic

Mr. Maxym Kryshko

the estimated DSGE state variables from our data-rich and from the regular DSGE model. Finally, we explore the differences that the regular and the data-rich DSGE models imply about the sources of business cycle fluctuations and about the propagation of structural innovations, notably the monetary policy and technology shocks, to the real output, inflation, interest rates and real money balances. Section VI concludes. II. D ata -R ich DSGE M odel In this section, we begin by defining what we refer to as the data-rich DSGE model and contrast it with the

Mr. Alessandro Rebucci, Mr. Akito Matsumoto, Pietro Cova, and Massimiliano Pisani

shocks. And even more so given that, as we shall see in Section 4 , this volatility reducing effect may not be necessarily present in general equilibrium. III. A DSGE M odel We employ a relatively simple dynamic stochastic general equilibrium (DSGE) model to characterize the effects of news shocks on asset prices in general equilibrium. Except for news shocks, the model and its solution are standard. We keep the model simple to obtain analytical solutions. The model is a production economy with unit population, nominal rigidity and news shocks. Goods prices

Mr. Scott Roger and Mr. Jan Vlcek
This paper uses a DSGE model with banks and financial frictions in credit markets to assess the medium-term macroeconomic costs of increasing capital and liquidity requirements. The analysis indicates that the macroeconomic costs of such measures are sensitive to the length of the implementation period as well as to the adjustment strategy used by banks, and the scope for monetary policy to respond to the regulatory changes.