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What You Match Does Matter: The Effects of Data on DSGE Estimation

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  • Pablo A. Guerron

    ()
    (Department of Economics, North Carolina State University)

Abstract

This paper explores the effects of using alternative data sets for the estimation of DSGE models. I find that the estimated structural parameters and the model's outcomes are sensitive to the variables used for estimation. Depending on the set of variables the point estimate for habit formation ranges from 0.70 to 0.97. Similarly, the interest-smoothing coefficient in the Taylor rule fluctuates between 0.06 and 0.76. In terms of the model's predictions, if interest rates are excluded during estimation, the estimated structural coefficients are such that the model forecasts a strong deflation following an expansionary monetary expansion. More importanlty, three ways to assess different observable sets are proposed. Based on these measures, I find that that including the price of investment in the data set delivers the best results.

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Bibliographic Info

Paper provided by North Carolina State University, Department of Economics in its series Working Paper Series with number 012.

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Length: 46 pages
Date of creation: Jul 2007
Date of revision:
Handle: RePEc:ncs:wpaper:012

Note: First draft 2007-06
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Keywords: Bayesian Estimation; DSGE; Variable Selection; Impulse Response; Entropy;

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  1. Canova, Fabio & Sala, Luca, 2009. "Back to square one: identification issues in DSGE models," CEPR Discussion Papers 7234, C.E.P.R. Discussion Papers.
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  18. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Proceedings, Federal Reserve Bank of San Francisco, issue Jun.
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Citations

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Cited by:
  1. Creel, Michael & Kristensen, Dennis, 2011. "Indirect Likelihood Inference," Dynare Working Papers 8, CEPREMAP.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  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. Luca Sala, 2013. "DSGE models in the frequency domain," Working Papers 504, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  8. 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.
  9. 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.
  10. Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," 2010 Meeting Papers 1117, Society for Economic Dynamics.

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