EstRegime: Estimated Regimes for the univariate Gaussian HMM

In GaussianHMM1d: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models

Estimated Regimes for the univariate Gaussian HMM

Description

This function computes and plots the most likely regime for univariate Gaussian HMM using probabilities of being in regime k at time t given all observations (lambda) and probabilities of being in regime k at time t given observations up to time t (eta).

Usage

EstRegime(t, y, lambda, eta)

Arguments

t	(nx1) vector of dates (years, ...); if no dates then t=[1:length(y)]
y	(nx1) vector of data;
lambda	(nxreg) probabilities of being in regime k at time t given all observations;
eta	(nxreg) probabilities of being in regime k at time t given observations up to time t;

Value

A	Estimated Regime using lambda
B	Estimated Regime using eta
runsA	Estimated number of runs using lambda
runsB	Estimated number of runs using eta
pA	Graph for the estimated regime for each observation using lambda
pB	Graph for the estimated regime for each observation using eta

Author(s)

Bouchra R Nasri and Bruno N Rémillard, January 31, 2019

References

Chapter 10.2 of B. Rémillard (2013). Statistical Methods for Financial Engineering, Chapman and Hall/CRC Financial Mathematics Series, Taylor & Francis.

Examples

 Q <- matrix(c(0.8, 0.3, 0.2, 0.7),2,2); mu <- c(-0.3 ,0.7) ; sigma <- c(0.15,0.05);
data <- Sim.HMM.Gaussian.1d(mu,sigma,Q,eta0=1,100)$x
t=c(1:100);
est <- EstHMM1d(data, 2)
EstRegime(t,data,est$lambda, est$eta)

GaussianHMM1d documentation built on July 9, 2023, 6:52 p.m.