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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.

Suggested Citation

  • Pablo A. Guerron, 2007. "What You Match Does Matter: The Effects of Data on DSGE Estimation," Working Paper Series 012, North Carolina State University, Department of Economics.
  • Handle: RePEc:ncs:wpaper:012 Note: First draft 2007-06
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    1. repec:eee:eecrev:v:95:y:2017:i:c:p:142-167 is not listed on IDEAS
    2. Takashi Kano & James M. Nason, 2014. "Business Cycle Implications of Internal Consumption Habit for New Keynesian Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(2-3), pages 519-544, March.
    3. Patrick Fève & Jean‐Guillaume Sahuc, 2017. "In Search of the Transmission Mechanism of Fiscal Policy in the Euro Area," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 704-718, April.
    4. 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.
    5. 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.
    6. Michael Creel & Dennis Kristensen, "undated". "Indirect Likelihood Inference," Working Papers 558, Barcelona Graduate School of Economics.
    7. Pablo A. Guerron-Quintana & Ryo Jinnai, "undated". "Liquidity, Trends and the Great Recession," Working Papers e66, Tokyo Center for Economic Research.
    8. Thorsten Drautzburg, 2014. "A Narrative Approach to a Fiscal DSGE Model," 2014 Meeting Papers 791, Society for Economic Dynamics.
    9. Sheen, Jeffrey & Wang, Ben Zhe, 2016. "Assessing labor market frictions in a small open economy," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 231-251.
    10. Wolters, Maik Hendrik, 2016. "How the Baby Boomers' Retirement Wave Distorts Model-Based Output Gap Estimates," Annual Conference 2016 (Augsburg): Demographic Change 145812, Verein für Socialpolitik / German Economic Association.
    11. 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.
    12. Giorgio Motta & Patrizio Tirelli, 2012. "Optimal Simple Monetary and Fiscal Rules under Limited Asset Market Participation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1351-1374, October.
    13. Enrique Martínez-García & Mark A. Wynne, 2014. "Assessing Bayesian Model Comparison in Small Samples," Advances in Econometrics,in: Bayesian Model Comparison, volume 34, pages 71-115 Emerald Publishing Ltd.
    14. Luca Sala, 2015. "Dsge Models in the Frequency Domains," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 219-240, March.
    15. Fabio Canova & Filippo Ferroni & Christian Matthes, 2014. "Choosing The Variables To Estimate Singular Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1099-1117, November.
    16. Lahcen, Mohammed Ait, 2014. "DSGE models for developing economies: an application to Morocco," MPRA Paper 63404, University Library of Munich, Germany.
    17. 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.
    18. Havranek, Tomas & Rusnak, Marek & Sokolova, Anna, 2017. "Habit formation in consumption: A meta-analysis," European Economic Review, Elsevier, vol. 95(C), pages 142-167.
    19. Zhongjun Qu & Fan Zhuo, 2015. "Likelihood Ratio Based Tests for Markov Regime Switching," Boston University - Department of Economics - Working Papers Series wp2015-003, Boston University - Department of Economics.
    20. Kortelainen, Mika & Paloviita, Maritta & Viren, Matti, 2016. "How useful are measured expectations in estimation and simulation of a conventional small New Keynesian macro model?," Economic Modelling, Elsevier, vol. 52(PB), pages 540-550.
    21. Zhongjun Qu, 2015. "A Composite Likelihood Framework for Analyzing Singular DSGE Models," Boston University - Department of Economics - Working Papers Series wp2015-002, Boston University - Department of Economics.
    22. repec:aea:aejmac:v:9:y:2017:i:3:p:186-221 is not listed on IDEAS
    23. 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.
    24. 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.

    More about this item

    Keywords

    Bayesian Estimation; DSGE; Variable Selection; Impulse Response; Entropy;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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