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On the distribution of information in the moment structure of DSGE models

  • Nikolay Iskrev

    (Bank of Portugal)

There is a long tradition in macroeconomics of using selected moments of the data to determine empirically relevant values of structural parameters. This paper presents a formal approach for evaluating the implications of DSGE models for the distribution of information in the moment structure of their variables. Specifically, it shows how to address the following questions: (1) what are the efficiency gains from using more instead of fewer moments; (2) what is the efficiency loss from assigning suboptimal weights on the used moments; and (3) which particular dimensions of the data - first and second order moments in the time domain, and sets of frequencies in the fre quency domain - are most informative about individual structural parameters. The analysis is based on the asymptotic properties of maximum likelihood and moment matching estimators and is simple to perform for general linearized models. A standard real business cycle model is used as an illustration.

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File URL: https://economicdynamics.org/meetpapers/2013/paper_339.pdf
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Paper provided by Society for Economic Dynamics in its series 2013 Meeting Papers with number 339.

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Date of creation: 2013
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Handle: RePEc:red:sed013:339
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Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA

Web page: http://www.EconomicDynamics.org/
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  1. Ben S. Bernanke & Julio J. Rotemberg, 1997. "NBER Macroeconomics Annual 1997, Volume 12," NBER Books, National Bureau of Economic Research, Inc, number bern97-1, September.
  2. RUGE-MURCIA, Francisco J., 2003. "Methods to Estimate Dynamic Stochastic General Equilibrium Models," Cahiers de recherche 17-2003, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  3. Ben S. Bernanke & Julio J. Rotemberg, 1997. "Editorial in "NBER Macroeconomics Annual 1997, Volume 12"," NBER Chapters, in: NBER Macroeconomics Annual 1997, Volume 12, pages 1-6 National Bureau of Economic Research, Inc.
  4. Zhongjun Qu & Denis Tkachenko, 2012. "Identification and frequency domain quasi‐maximum likelihood estimation of linearized dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 3(1), pages 95-132, 03.
  5. Fuentes-Albero, Cristina & Kryshko, Maxym & Ríos-Rull, José-Víctor & Santaeulàlia-Llopis, Raül & Schorfheide, Frank, 2009. "Methods versus Substance: Measuring the Effects of Technology Shocks on Hours," CEPR Discussion Papers 7474, C.E.P.R. Discussion Papers.
  6. Jondeau E. & Le Bihan H. & Galles C., 2004. "Assessing Generalized Method-of-Moments Estimates of the Federal Reserve Reaction Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 225-239, April.
  7. Peter A. Zadrozny, 1988. "Analytic Derivatives for Estimation of Linear Dynamic Models," Working Papers 88-5, Center for Economic Studies, U.S. Census Bureau.
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