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What you match does matter: the effects of data on DSGE estimation

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  • Pablo A. Guerron-Quintana

    (Department of Economics, North Carolina State University, Raleigh, NC, USA)

Abstract

This paper explores the effects of using alternative combinations of observables for the estimation of Dynamic Stochastic General Equilibrium (DSGE) models. I find that the estimation of structural parameters describing the Taylor rule and sticky contracts in prices and wages is particularly sensitive to the set of observables. In terms of the model's predictions, the exclusion of some observables may lead to estimated parameters with unexpected outcomes, such as recessions following a positive technology shock. More importantly, two ways to assess different sets of observables are proposed. These measures favor a dataset consisting of seven observables. Copyright © 2009 John Wiley & Sons, Ltd.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 25 (2010)
Issue (Month): 5 ()
Pages: 774-804

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Handle: RePEc:jae:japmet:v:25:y:2010:i:5:p:774-804

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  1. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2006. "Estimating Macroeconomic Models: A Likelihood Approach," NBER Technical Working Papers 0321, National Bureau of Economic Research, Inc.
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  7. Adolfson, Malin & Laseen, Stefan & Linde, Jesper & Villani, Mattias, 2007. "Bayesian estimation of an open economy DSGE model with incomplete pass-through," Journal of International Economics, Elsevier, vol. 72(2), pages 481-511, July.
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Cited by:
  1. Takashi Kano & James M. Nason, 2009. "Business Cycle Implications of Internal Consumption Habit for New Keynesian Model," CARF F-Series CARF-F-151, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
  2. Ríos-Rull, José-Víctor & Schorfheide, Frank & Fuentes-Albero, Cristina & Kryshko, Maxym & Santaeulàlia-Llopis, Raül, 2012. "Methods versus substance: Measuring the effects of technology shocks," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 826-846.
  3. Luca Sala, 2013. "DSGE models in the frequency domain," Working Papers 504, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  4. Michael Creel & Dennis Kristensen, 2011. "Indirect likelihood inference," Working Papers 558, Barcelona Graduate School of Economics.
  5. Martin Fukac & Vladimir Havlena, 2011. "Note on the role of natural condition of control in the estimation of DSGE models," Research Working Paper RWP 11-03, Federal Reserve Bank of Kansas City.
  6. Hall, Jamie & Pitt, Michael K. & Kohn, Robert, 2014. "Bayesian inference for nonlinear structural time series models," Journal of Econometrics, Elsevier, vol. 179(2), pages 99-111.
  7. Kim, Kwang Hwan & Katayama, Munechika, 2013. "Non-separability and sectoral comovement in a sticky price model," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1715-1735.
  8. Matus Senaj & Milan Vyskrabka & Juraj Zeman, 2010. "MUSE: Monetary Union and Slovak Economy model," Working and Discussion Papers WP 1/2010, Research Department, National Bank of Slovakia.
  9. Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," 2010 Meeting Papers 1117, Society for Economic Dynamics.
  10. Martin Fukaè & Vladimír Havlena, 2011. "A Note on the Role of the Natural Condition of Control in the Estimation of DSGE Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 453-466, November.

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