arXiv 27 Dec 2024 · Econometrics
arXiv:2412.19555 · PDF · DOI · OpenAlex · Extracted main text
A Markov-switching observation-driven model is a stochastic process $((S_t,Y_t))_{t \in \mathbb{Z}}$ where (i) $(S_t)_{t \in \mathbb{Z}}$ is an unobserved Markov process taking values in a finite set and (ii) $(Y_t)_{t \in \mathbb{Z}}$ is an observed process such that the conditional distribution of $Y_t$ given all past $Y$'s and the current and all past $S$'s depends only on all past $Y$'s and $S_t$. In this paper, we prove the consistency and asymptotic normality of the maximum likelihood estimator for such model. As a special case hereof, we give conditions under which the maximum likelihood estimator for the widely applied Markov-switching generalised autoregressive conditional heteroscedasticity model introduced by Haas et al. (2004b) is consistent and asymptotic normal.
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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 | R. Douc, Éric Moulines, and T. Rydén (2004) Asymptotic properties of the maximum likelihood estimator in autoregressive models with markov regime | 1.000 | 14 | 4 | 100% |
| 2 | M. Haas, S. Mittnik, and M. S. Paolella (2004) A new approach to markov-switching garch models | 1.000 | 13 | 6 | 100% |
| 3 | H. Kasahara and K. Shimotsu (2019) Asymptotic properties of the maximum likelihood estimator in regime switching econometric models | 1.000 | 13 | 4 | 100% |
| 4 | B. M. Kandji and A. Misko (2024) Markov-switching normal-mixture garch | 1.000 | 6 | 3 | 100% |
| 5 | C. Francq, M. Roussignol, and J.-M. Zakoïan (2001) Conditional heteroskedasticity driven by hidden markov chains | 1.000 | 5 | 4 | 100% |
| 6 | P. Bougerol (1993) Kalman filtering with random coefficients and contractions | 0.843 | 5 | 3 | 60% |
| 7 | C. Francq and M. Roussignol (1998) Ergodicity of autoregressive processes with markov-switching and consistency of the maximum-likelihood estimator | 0.843 | 3 | 3 | 100% |
| 8 | M. Haas, S. Mittnik, and M. S. Paolella (2004) Mixed normal conditional heteroskedasticity | 0.843 | 3 | 3 | 100% |
| 9 | F. Blasques, P. Gorgi, S. J. Koopman, and O. Wintenberger (2018) Feasible invertibility conditions and maximum likelihood estimation for observation-driven models | 0.737 | 3 | 2 | 100% |
| 10 | D. Straumann and T. Mikosch (2006) Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: A stochastic recurrence equations approach | 0.667 | 27 | 5 | 30% |
Showing the top 10 of 45 scored citations.