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Evaluating Specification Tests for Markov‐Switching Time‐Series Models

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  • Daniel R. Smith

Abstract

. We evaluate the performance of several specification tests for Markov regime‐switching time‐series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung–Box tests based on both the generalized residual and a standard‐normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung–Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual‐based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov‐switching generalized ARCH (GARCH) model fitted to the US dollar–British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data.

Suggested Citation

  • Daniel R. Smith, 2008. "Evaluating Specification Tests for Markov‐Switching Time‐Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(4), pages 629-652, July.
    • Handle: RePEc:bla:jtsera:v:29:y:2008:i:4:p:629-652
    DOI: 10.1111/j.1467-9892.2008.00575.x
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    Cited by:

    1. Monica Billio & Silvio Di Sanzo, 2015. "Granger-causality in Markov switching models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 956-966, May.
    2. Willem H. Boshoff & Rossouw van Jaarsveld, 2019. "Recurrent Collusion: Cartel Episodes and Overcharges in the South African Cement Market," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(2), pages 353-380, March.
    3. Trottier, Denis-Alexandre & Lai, Van Son & Godin, Frédéric, 2019. "A characterization of CAT bond performance indices," Finance Research Letters, Elsevier, vol. 28(C), pages 431-437.
    4. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 239-270, July.
    5. Gerdesmeier, Dieter & Reimers, Hans-Eggert & Roffia, Barbara, 2015. "Consumer and asset prices: Some recent evidence," Wismar Discussion Papers 01/2015, Hochschule Wismar, Wismar Business School.
    6. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
    7. Chevallier, Julien, 2012. "Global imbalances, cross-market linkages, and the financial crisis: A multivariate Markov-switching analysis," Economic Modelling, Elsevier, vol. 29(3), pages 943-973.
    8. Haas, Markus & Liu, Ji-Chun, 2015. "Theory for a Multivariate Markov--switching GARCH Model with an Application to Stock Markets," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112855, Verein für Socialpolitik / German Economic Association.
    9. Park, JaeHyun & Hong, TaeHoon, 2013. "Analysis of South Korea’s economic growth, carbon dioxide emission, and energy consumption using the Markov switching model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 543-551.
    10. Chourdakis, Kyriakos & Dendramis, Yiannis & Tzavalis, Elias, 2014. "Are regime-shift sources of risk priced in the market?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 151-170.

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