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In this paper, we consider a stochastic SIR epidemic model with regime switching.
In this paper, we consider a stochastic SI epidemic model with regime switching.
Formal dynamic analysis in the model with regime switches is clearly more complicated than in the standard NK model.
A disequilibrium model with regime switches behaves in an intrinsically nonlinear manner due to the occurrence of regime switches.
Taking into consideration the evidence of non-linearity of the series, Hamilton (1989) proposes a non-linear model with regime changes.
In this paper, we consider the existence of a stationary distribution of a stochastic SI epidemic model with regime switching and its stability.
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In particular, we employ a multivariate generalized autoregressive conditionally heteroskedastic (MVGARCH) model involving time-varying settings and multivariate Markov switching autoregressive conditionally heteroskedastic (MVSWARCH) model with regime-switching techniques and compare them with a conventional linear regression-based (LRB) model.
Krolzig (1997) made a representation with the general MS models with regime-dependent parameters in order to establish a common notation for each model, such as MSM-VAR, MSH-VAR, and MSIH-VAR.
We tested differences in host choice between selection regimes using a generalized lineal mixed-effects model, with Selection regime as a fixed effect, and Selection line as a random effect, using the lmer function in the lme4 package in R [ 59].
The MSIAH (3) model, which is based on a Markov switching model with three regimes and an AR (4) from the output in differences, generates recession periods that are more in line with the empirical dating of recessions4.
The evolution of the floral shape (PC2) was best explained by an OUMVA model with two regimes, which are defined by the pollination types.
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