ON THE DETERMINATION OF THE NUMBER OF REGIMES IN MARKOV‐SWITCHING AUTOREGRESSIVE MODELS

By Zacharias Psaradakis, Nicola Spagnolo. 2002

176 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5d77146486234b2ab2d8dad1d28c58f145d86781

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Citations in the corpus

A model of carbon price interactions with macroeconomic and energy dynamics (2011, methodology): “46 See for instance Point Carbon or IDEA Carbon.”
Regime Switching Model of US Crude Oil and Stock Market Prices: 1859 to 2013 (2014, unknown): “Saikkonen (1992) and Saikkonen and Luukkonen (1997) show that the Johansen procedure estimates the cointegrating vectors consistently, even in the presence of regime switching.”
The Asymmetric Business Cycle (2012, background): “Meanwhile, in terms of information theory, Smith, Naik, and Tsai (2006) show that AIC places too small of a penalty on Markov-switching parameters from the perspective of minimizing the Kullback-Leibler divergence from the true model when the true model involves Markov switching, while Psaradakis an”
Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching (2006, methodology): “…likelihood criteria on the magnitude of parameter change and the autocorrelation structure of the regime variables was also observed by Psaradakis and Spagnolo (2003) in the context of the simpler problem of identifying the state dimension of a Markov-switching model of known…”
The Switching Relationship between Natural Gas and Crude Oil Prices (2014, methodology): “5 Given unresolved issues in implementing likelihood ratio tests (such as in Hansen (1992)) to determine the number of states, Psaradakis and Spagnolo (2003) employ a Monte Carlo analysis to test the performance of methods based on complexity-penalized likelihood criteria in Markov Switching autoreg”
Revisiting the empirical linkages between stock returns and trading volume (2012, methodology): “[Insert Table 3 Here] Next, we compute the information criteria proposed by Psaradakis and Spagnolo (2003) for Markov-switching autoregressive models to determine the optimal lag length, q, of the MS-AR(q) model in equation (1).”
The asymmetric effects of oil shocks on output growth: a Markov-switching analysis for the G-7 countries (2006, unknown): “Our empirical procedure follows Psaradakis and Spagnolo (2003), who suggest to select the number of regimes using the Akaike Information Criterion (AIC).”
Markov Switching Models in Empirical Finance (2011, methodology): “For instance, Psaradakis and Spagnolo (2003) discuss the effectiveness of procedures based on the AIC as a means of selecting the number of regimes in MSVAR models.”