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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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    Cited by:

    1. Hilde C. Bjørnland & Vegard H. Larsen & Junior Maih, 2018. "Oil and Macroeconomic (In)stability," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(4), pages 128-151, October.
    2. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks' Macro Models," CEPR Discussion Papers 11405, C.E.P.R. Discussion Papers.
    3. Andrew Binning & Junior Maih, 2016. "Implementing the Zero Lower Bound in an Estimated Regime-Switching DSGE Model," Working Papers No 3/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Sami Alpanda & Alexander Ueberfeldt, 2016. "Should Monetary Policy Lean Against Housing Market Booms?," Staff Working Papers 16-19, Bank of Canada.
    5. Andrew Binning & Junior Maih, 2015. "Sigma point filters for dynamic nonlinear regime switching models," Working Paper 2015/10, Norges Bank.
    6. Barthélemy, Jean & Marx, Magali, 2017. "Solving endogenous regime switching models," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 1-25.
    7. Anette Borge & Gunnar Bårdsen & Junior Maih, 2019. "Expectations switching in a DSGE model for the UK," Working Paper Series 18119, Department of Economics, Norwegian University of Science and Technology.
    8. Zakipour-Saber, Shayan, 2019. "State-dependent Monetary Policy Regimes," Research Technical Papers 4/RT/19, Central Bank of Ireland.
    9. Haroon Mumtaz & Gabor Pinter & Konstantinos Theodoridis, 2018. "What Do Vars Tell Us About The Impact Of A Credit Supply Shock?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 625-646, May.
    10. Lindé, J. & Smets, F. & Wouters, R., 2016. "Challenges for Central Banks’ Macro Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 2185-2262, Elsevier.
    11. Andrew Binning & Junior Maih, 2017. "Modelling Occasionally Binding Constraints Using Regime-Switching," Working Paper 2017/23, Norges Bank.
    12. Mutschler, Willi, 2018. "Higher-order statistics for DSGE models," Econometrics and Statistics, Elsevier, vol. 6(C), pages 44-56.
    13. Marius Clemens & Stefan Gebauer & Tobias König, 2020. "The Macroeconomic Effects of a European Deposit (Re-) Insurance Scheme," Discussion Papers of DIW Berlin 1873, DIW Berlin, German Institute for Economic Research.
    14. Sergey Ivashchenko & Semih Emre Çekin & Kevin Kotzé & Rangan Gupta, 2018. "Forecasting with Second-Order Approximations and Markov Switching DSGE Models," Working Papers 201862, University of Pretoria, Department of Economics.
    15. 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.
    16. Andrew Binning & Hilde C. Bjørnland & Junior Maih, 2019. "Is Monetary Policy Always Effective? Incomplete Interest Rate Pass-through in a DSGE Model," Working Papers No 09/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    17. Alberto Ortiz-Bolaños & Sebastián Cadavid-Sánchez & Gerardo Kattan-Rodríguez, 2018. "Targeting Long-term Rates in a Model with Financial Frictions and Regime Switching," Investigación Conjunta-Joint Research, in: Alberto Ortiz-Bolaños (ed.), Monetary Policy and Financial Stability in Latin America and the Caribbean, edition 1, volume 1, chapter 6, pages 159-219, Centro de Estudios Monetarios Latinoamericanos, CEMLA.
    18. Sebastián Cadavid Sánchez, 2018. "Monetary policy and structural changes in Colombia, 1990-2016: A Markov Switching approach," Documentos CEDE 016970, Universidad de los Andes - CEDE.
    19. 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.
    20. Leonardo Barreto, 2018. "Nonconventional monetary policy in a regime-switching model with endogenous financial crises," Documentos CEDE 016382, Universidad de los Andes - CEDE.
    21. Andrej Drygalla, 2015. "Switching to Exchange Rate Flexibility? The Case of Central and Eastern European Inflation Targeters," FIW Working Paper series 139, FIW.
    22. Gibbs, Christopher G. & McClung, Nigel, 2019. "Does my model predict a forward guidance puzzle?," Research Discussion Papers 19, Bank of Finland.

    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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