arXiv 10 Feb 2020 · Econometrics · 7 citations (OpenAlex)
arXiv:2002.03598 · PDF · DOI · OpenAlex · Extracted main text
Markov switching models are a popular family of models that introduces time-variation in the parameters in the form of their state- or regime-specific values. Importantly, this time-variation is governed by a discrete-valued latent stochastic process with limited memory. More specifically, the current value of the state indicator is determined only by the value of the state indicator from the previous period, thus the Markov property, and the transition matrix. The latter characterizes the properties of the Markov process by determining with what probability each of the states can be visited next period, given the state in the current period. This setup decides on the two main advantages of the Markov switching models. Namely, the estimation of the probability of state occurrences in each of the sample periods by using filtering and smoothing methods and the estimation of the state-specific parameters. These two features open the possibility for improved interpretations of the parameters associated with specific regimes combined with the corresponding regime probabilities, as well as for improved forecasting performance based on persistent regimes and parameters characterizing them.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Frühwirth-Schnatter, S (2006) Finite mixture and Markov switching models | 0.811 | 4 | 2 | 100% |
| 2 | Hamilton, J. D (1989) A new approach to the economic analysis of nonstationary time series and the business cycle | 0.811 | 4 | 2 | 100% |
| 3 | Kim, C. J., Piger, J., & Startz, R (2008) Estimation of Markov regime-switching regression models with endogenous switching | 0.644 | 4 | 1 | 100% |
| 4 | Fox, E. B., Sudderth, E. B., Jordan, M. I., Willsky, A. S. et al (2011) A sticky hdp-hmm with application to speaker diarization | 0.644 | 4 | 1 | 100% |
| 5 | Sims, C. A., & Zha, T (2006) Were There Regime Switches in U.S. Monetary Policy? | 0.644 | 2 | 2 | 100% |
| 6 | Sims, C. A., Waggoner, D. F., & Zha, T (2008) Methods for inference in large multiple-equation Markov-switching models | 0.585 | 3 | 1 | 100% |
| 7 | Bauwens, L., Carpantier, J.-F., & Dufays, A (2017) Autoregressive moving average infinite hidden markov-switching models | 0.585 | 3 | 1 | 100% |
| 8 | Hamilton, J. D (1994) Time series analysis | 0.585 | 3 | 1 | 100% |
| 9 | Song, Y (2014) Modelling regime switching and structural breaks with an infinite hidden markov model self | 0.585 | 3 | 1 | 100% |
| 10 | Chang, Y., Choi, Y., & Park, J. Y (2017) A new approach to model regime switching | 0.511 | 2 | 1 | 100% |
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