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

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  • Nikolay Iskrev

    (Bank of Portugal)

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    Abstract

    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: http://www.economicdynamics.org/meetpapers/2013/paper_339.pdf
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    Bibliographic Info

    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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    1. Müller, Ulrich K., 2012. "Measuring prior sensitivity and prior informativeness in large Bayesian models," Journal of Monetary Economics, Elsevier, vol. 59(6), pages 581-597.
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    9. 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.
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    11. Peter A. Zadrozny, 1988. "Analytic Derivatives for Estimation of Linear Dynamic Models," Working Papers 88-5, Center for Economic Studies, U.S. Census Bureau.
    12. Iskrev, Nikolay, 2008. "Evaluating the information matrix in linearized DSGE models," Economics Letters, Elsevier, vol. 99(3), pages 607-610, June.
    13. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S63-84, Suppl. De.
    14. 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.
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