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NHMSAR (version 1.1)

Non-Homogeneous Markov Switching Autoregressive Models

Description

Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations. Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only.

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Version

Install

install.packages('NHMSAR')

Monthly Downloads

26

Version

1.1

License

GPL

Maintainer

Valerie Monbet

Last Published

June 9th, 2015

Functions in NHMSAR (1.1)

simule.nh.MSAR

Simulation of (non) homogeneous Markov Stiwtching autoregressive models
Mstep.hh.MSAR.with.constraints

M step of the EM algorithm for fitting homogeneous multivariate Markov switching auto-regressive models with constraints on VAR models.
Mstep.nn.MSAR

M step of the EM algorithm.
fit.MSAR.VM

Fit von Mises (non) homogeneous Markov switching autoregressive models
NH-MSAR-package

(Non) Homogeneous Markov switching autoregressive model
test.model.MSAR

Performs bootstrap statistical tests to validate MSAR models.
Mstep.hh.SCAD.MSAR

M step of the EM algorithm for fitting homogeneous multivariate Markov switching auto-regressive models with penalization of parameters of the VAR(1) models.
cor.MSAR

Empirical correlation functions comparison .
nhforwards_backwards

Forward Backward for MSAR models with non homogeneous transitions
Mstep.nh.MSAR.VM

M step of the EM algorithm for von Mises MSAR models
MeanDurOver

Mean Duration of sojourn over a treshold
init.theta.MSAR.VM

Initialisation function for von Mises MSAR model fitting
forecast.prob.MSAR

Forecast probabilities for (non) homogeneous MSAR models
Wind

Winter wind data at 18 locations offshore of France
Mstep.hh.MSAR.VM

M step of the EM algorithm for fitting von Mises Markov switching auto-regressive models.
forwards_backwards

Forward Backward for homogeneous MSAR models
init.theta.MSAR (NH-MSAR)

Initialisation function for MSAR model fitting
simule_MC

Simulates Markov chain of length T
cross.cor.MSAR

empirical cross-correlation for multivariate MSAR time series
meteo.data

Meteorological at Brest (France) for January month from 1973 to 2013
Estep.MSAR

Estep of the EM algorithm for fitting (non) homogeneous Markov switching auto-regressive models.
regimes.plot.MSAR

Plot MSAR time series with regimes
fit.MSAR (NH-MSAR)

Fit (non) homogeneous Markov switching autoregressive models
Cond.prob.MSAR

Conditional probabilities for (non) homogeneous MSAR models
MeanDurUnder

Mean Duration of sojourn under a treshold
PibDetteDemoc

Annual GDP and Debt data 1970-2010
Mstep.nh.MSAR

M step of the EM algorithm.
valid_all

Statistics plotting for validation of MSAR models
Mstep.hn.MSAR

M step of the EM algorithm for fitting Markov switching auto-regressive models with non homogeneous emissions.
test.model.vect.MSAR

Performs bootstrap statistical tests on covariance to validate MSVAR models.
Estep.MSAR.VM

Estep of the EM algorithm for fitting von Mises (non) homogeneous Markov switching auto-regressive models.
Mstep.classif

fit an AR model for each class of C
WindDir

January wind direction at Ouessant
Mstep.hh.reduct.MSAR

M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive models with constraints on the matrices.
prediction.MSAR

One step ahead predict for (non) homogeneous MSAR models
ENu_graph

Plots empirical expected number of upcrossings of level u with respect to P(Y
log_dens_Von_Mises

von Mises log likelihood.
Mstep.hh.MSAR

M step of the EM algorithm for fitting homogeneous Markov switching auto-regressive models.
emisprob.MSAR.VM

Emission probabilities for von Mises MSAR
simule.nh.MSAR.VM

Simulation of (non) homogeneous Markov Stiwtching autoregressive models von Mises innovations