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Do Markov-switching models capture nonlinearities in the data?

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  • Breunig, Robert V
  • Pagan, Adrian R

Abstract

Markov-switching models have become popular alternatives to linear autoregressive models. Many papers which estimate nonlinear models make little attempt to demonstrate whether the nonlinearities they capture are of interest or if the models differ substantially from the linear option. By simulating the models and nonparametrically estimating functions of the simulated data, we can evaluate if and how the nonlinear and linear models differ.

Suggested Citation

  • Breunig, Robert V & Pagan, Adrian R, 2004. "Do Markov-switching models capture nonlinearities in the data?," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(3), pages 401-407.
    • Handle: RePEc:eee:matcom:v:64:y:2004:i:3:p:401-407
    DOI: 10.1016/S0378-4754(03)00106-X
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    References listed on IDEAS

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    Cited by:

    1. Fukuda, Kosei, 2009. "Distribution switching in financial time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1711-1720.
    2. Huseyin Tastan & Nuri Yildirim, 2008. "Business cycle asymmetries in Turkey: an application of Markov-switching autoregressions," International Economic Journal, Taylor & Francis Journals, vol. 22(3), pages 315-333.
    3. Marian Vavra, 2016. "Testing the Validity of Assumptions of UC-ARIMA Models for Trend-Cycle Decompositions," Working and Discussion Papers WP 4/2016, Research Department, National Bank of Slovakia.

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