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Estimation and Evaluation of DSGE Models: Progress and Challenges

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  • Frank Schorfheide

Abstract

Estimated dynamic stochastic equilibrium (DSGE) models are now widely used for empirical research in macroeconomics as well as for quantitative policy analysis and forecasting at central banks around the world. This paper reviews recent advances in the estimation and evaluation of DSGE models, discusses current challenges, and provides avenues for future research.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 16781.

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Date of creation: Feb 2011
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Publication status: published as “Estimation and Evaluation of DSGE Models: Progress and Challenges.” Forthcoming in: D. Acemoglu, M. Arellano, and E. Deckel (eds .): “Advances in Economics and Econometrics: Theory and Applications, Tenth World Congress,” Vol 3 , 2 013, Cambridge University Press , 184 - 230.
Handle: RePEc:nbr:nberwo:16781

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  1. José-Víctor Ríos-Rull & Frank Schorfheide & Cristina Fuentes-Albero & Raul Santaeulalia-Llopis & Maxym Kryshko, 2009. "Methods versus substance: measuring the effects of technology shocks on hours," Staff Report 433, Federal Reserve Bank of Minneapolis.
  2. Guerron-Quintana, Pablo A. & Inoue, Atsushi & Kilian, Lutz, 2009. "Frequentist Inference in Weakly Identified DSGE Models," CEPR Discussion Papers 7447, C.E.P.R. Discussion Papers.
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Cited by:
  1. repec:wyi:journl:002201 is not listed on IDEAS
  2. Donald W.K. Andrews & Xu Cheng, 2011. "GMM Estimation and Uniform Subvector Inference with Possible Identification Failure," Cowles Foundation Discussion Papers 1828, Cowles Foundation for Research in Economics, Yale University.
  3. Hashem M. Pesaran & Ron P. Smith, 2011. "Beyond the DSGE Straitjacket," CESifo Working Paper Series 3447, CESifo Group Munich.
  4. Luca Barone, 2013. "An ABM for Economics: Micro Explains Macro," Working papers 016, Department of Economics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
  5. Chen, Xiaoshan & Kirsanova, Tatiana & Leith, Campbell, 2013. "How Optimal is US Monetary Policy?," Stirling Economics Discussion Papers 2013-05, University of Stirling, Division of Economics.
  6. Gunnar Bårdsen & Luca Fanelli, 2013. "Frequentist evaluation of small DSGE models," Working Paper Series 14113, Department of Economics, Norwegian University of Science and Technology.
  7. Zhongjun Qu & Denis Tkachenko, 2011. "Frequency Domain Analysis of Medium Scale DSGE Models with Application to Smets and Wouters (2007)," Boston University - Department of Economics - Working Papers Series WP2011-060, Boston University - Department of Economics.
  8. Zheng, Tingguo & Guo, Huiming, 2013. "Estimating a small open economy DSGE model with indeterminacy: Evidence from China," Economic Modelling, Elsevier, vol. 31(C), pages 642-652.
  9. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, vol. 45(1), pages 635-664, August.
  10. Frank Schorfheide, 2012. "EconomicDynamics Interviews Frank Schorfheide on DSGE Model Estimation," EconomicDynamics Newsletter, Review of Economic Dynamics, vol. 13(2), April.
  11. Schmidt, Sebastian & Wieland, Volker, 2013. "The New Keynesian Approach to Dynamic General Equilibrium Modeling: Models, Methods and Macroeconomic Policy Evaluation," Handbook of Computable General Equilibrium Modeling, Elsevier.

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