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Efficient Perturbation Methods for Solving Regime-Switching DSGE Models

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  • Junior Maih

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Abstract

In an environment where economic structures break, variances change, distributions shift, conventional policies weaken and past events tend to reoccur, economic agents have to form expectations over different regimes. This makes the regime-switching dynamic stochastic general equilibrium (RS-DSGE) model the natural framework for analyzing the dynamics of macroeconomic variables. We present efficient solution methods for solving this class of models, allowing for the transition probabilities to be endogenous and for agents to react to anticipated events. The solution algorithms derived use a perturbation strategy which, unlike what has been proposed in the literature, does not rely on the partitioning of the switching parameters. These algorithms are all implemented in RISE, a flexible object-oriented toolbox that can easily integrate alternative solution methods. We show that our algorithms replicate various examples found in the literature. Among those is a switching RBC model for which we present a third-order perturbation solution.

Suggested Citation

  • Junior Maih, 2014. "Efficient Perturbation Methods for Solving Regime-Switching DSGE Models," Working Papers No 10/2014, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  • Handle: RePEc:bny:wpaper:0028
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks´ Macro Models," Working Paper Series 323, Sveriges Riksbank (Central Bank of Sweden).
    2. Andrew Binning & Junior Maih, 2016. "Forecast uncertainty in the neighborhood of the effective lower bound: How much asymmetry should we expect?," Working Paper 2016/13, Norges Bank.
    3. Andrew Binning & Junior Maih, 2016. "Implementing the zero lower bound in an estimated regime-switching DSGE model," Working Paper 2016/3, Norges Bank.
    4. Andrew Binning & Junior Maih, 2017. "Modelling Occasionally Binding Constraints Using Regime-Switching," Working Paper 2017/23, Norges Bank.
    5. Hilde C. Bjørnland & Vegard H. Larsen, 2015. "Oil and macroeconomic (in)stability," Working Papers No 7/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    6. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2014. "What do VARs Tell Us about the Impact of a Credit Supply Shock? An Empirical Analysis," Working Papers 716, Queen Mary University of London, School of Economics and Finance.
    7. repec:eee:ecosta:v:6:y:2018:i:c:p:44-56 is not listed on IDEAS
    8. Andrew Binning & Junior Maih, 2015. "Sigma Point Filters For Dynamic Nonlinear Regime Switching Models," Working Papers No 4/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    9. repec:eee:macchp:v2-2185 is not listed on IDEAS
    10. Andrej Drygalla, 2015. "Switching to Exchange Rate Flexibility? The Case of Central and Eastern European Inflation Targeters," FIW Working Paper series 139, FIW.
    11. Sami Alpanda & Alexander Ueberfeldt, 2016. "Should Monetary Policy Lean Against Housing Market Booms?," Staff Working Papers 16-19, Bank of Canada.
    12. Barthélemy, Jean & Marx, Magali, 2017. "Solving endogenous regime switching models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 1-25.

    More about this item

    Keywords

    DSGE; Markov switching; Sylvester equation; Newton algorithm; perturbation; matrix polynomial;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • G1 - Financial Economics - - General Financial Markets

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