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Estimating dynamic equilibrium models with stochastic volatility

Author

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  • Fernández-Villaverde, Jesús
  • Guerrón-Quintana, Pablo
  • Rubio-Ramírez, Juan F.

Abstract

This paper develops a particle filtering algorithm to estimate dynamic equilibrium models with stochastic volatility using a likelihood-based approach. The algorithm, which exploits the structure and profusion of shocks in stochastic volatility models, is versatile and computationally tractable even in large-scale models. As an application, we use our algorithm and Bayesian methods to estimate a business cycle model of the US economy with both stochastic volatility and parameter drifting in monetary policy. Our application shows the importance of stochastic volatility in accounting for the dynamics of the data.

Suggested Citation

  • Fernández-Villaverde, Jesús & Guerrón-Quintana, Pablo & Rubio-Ramírez, Juan F., 2015. "Estimating dynamic equilibrium models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 185(1), pages 216-229.
    • Handle: RePEc:eee:econom:v:185:y:2015:i:1:p:216-229
    DOI: 10.1016/j.jeconom.2014.08.010
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Andrew Binning & Junior Maih, 2015. "Sigma point filters for dynamic nonlinear regime switching models," Working Paper 2015/10, Norges Bank.
    3. Kristoffer P. Nimark, 2014. "Man-Bites-Dog Business Cycles," American Economic Review, American Economic Association, vol. 104(8), pages 2320-2367, August.
    4. Christopher Otrok & Andrew Foerster & Alessandro Rebucci & Gianluca Benigno, 2017. "Estimating Macroeconomic Models of Financial Crises: An Endogenous Regime Switching Approach," 2017 Meeting Papers 572, Society for Economic Dynamics.
    5. Jump, Robert Calvert & Levine, Paul, 2019. "Behavioural New Keynesian models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 59-77.
    6. Ciccarone, Giuseppe & Giuli, Francesco & Marchetti, Enrico & Tancioni, Massimiliano, 2020. "Leaning against the bubble. Can theoretical models match the empirical evidence?," MPRA Paper 105004, University Library of Munich, Germany.
    7. Andrew Foerster & Juan F. Rubio‐Ramírez & Daniel F. Waggoner & Tao Zha, 2016. "Perturbation methods for Markov‐switching dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 7(2), pages 637-669, July.
    8. Pablo Cuba‐Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019. "Likelihood evaluation of models with occasionally binding constraints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1073-1085, November.
    9. Pablo A. Guerron-Quintana & Tomohiro Hirano & Ryo Jinnai, 2021. "Bubbles, Crashes, Ups and Downs in Economic Growth Theory and Evidence," CIGS Working Paper Series 21-006E, The Canon Institute for Global Studies.
    10. Dietrich, Alexander M., 2023. "Consumption categories, household attention, and inflation expectations: Implications for optimal monetary policy," University of Tübingen Working Papers in Business and Economics 157, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    11. Le Thanh Ha & To Trung Thanh & Doan Ngoc Thang, 2021. "Welfare costs of monetary policy uncertainty in the economy with shifting trend inflation," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(1), pages 126-154, February.
    12. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.
    13. Nigar Hashimzade & Oleg Kirsanov & Tatiana Kirsanova & Junior Maih, 2024. "On Bayesian Filtering for Markov Regime Switching Models," CESifo Working Paper Series 10941, CESifo.
    14. Patella, Valeria & Tancioni, Massimiliano, 2021. "Confidence Swings and Sovereign Risk Dynamics," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 195-206.

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    More about this item

    Keywords

    Dynamic equilibrium models; Stochastic volatility; Parameter drifting; Bayesian methods;
    All these keywords.

    JEL classification:

    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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