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msvar_draw

simulate an artifical MS-VAR process

CALLING SEQUENCE

y_artificial=msvar_draw(T,nb_endo,nlag,nb_states,switching_V,typemod,y0,trans_prob,Const,CMatrix,sigma)

PARAMETERS

Input

* T = a scalar, the # of observations

* nb_endo = a scalar, the number of endogenous variables

* nlag = a scalar, the number of lags in the VAR

* nb_states = a scalar, the number of states

* switching_V = a scalar, either 1 or the # of states, depending on whether the variance matrix switches or not

* typemod = either 'const' ('cte') or 'all', depending on whether the VAR model should have only the constant or all coefficients in the VAR switch

* y0 = a (nlags x nb_endo) vector of starting values for the endogenous variables in the VAR

* trans_prob = a (nb_states x nb_states) matrix of transition probabilities

* Const = a (nb_endo x nb_states) matrix of constant coefficients

* Cmatrix = either a (nb_endo x (nlag*nb_endo) x nb_states) or (nb_endo x (nlag*nb_endo)) matrix of coefficients non constant coefficients, depending on whether the non constant coefficients switch or not

* sigma = either a (nb_endo x (nlag*nb_endo) x nb_states) or (nb_endo x (nlag*nb_endo)) matrix of coefficients non constant coefficients, depending on whether the non constant coefficients switch or not

 

Output

* y_artificial = a (T x nb_endo) matrix of artifical values drawn from the Markov-switching model given as an input

DESCRIPTION

Produces an artificial Markov switching VAR process.

EXAMPLE

T=1000;
nb_endo=2;
nb_states=2;
p=2;
C=matrix([0.5 0.3 0.4 0.2 ; 0.7 -0.5 0.2 -0.1  ; 0.1 0.6 0.8 -0.6 ; 0.1 0.2 0.5 -0.6 ],2,4,2);
cte=[0 1 ; 0.4 -0.4 ];
y0=grand(p,nb_endo,'nor',0,1);
 
y=msvar_draw(T,nb_endo,p,nb_states,1,'all',y0,[0.8 0.15 ; 0.2 0.85],cte,C,[0.2 0.1 ; 0.1 0.3]);
// an artifical msvar with 2 endogenous, 2 states, 2 lags, all coefficients switching,
// transition probabilities matrix = [0.8 0.15 ; 0.2 0.85], variances not switching and
// =[0.8 0.15 ; 0.2 0.85]

AUTHOR

Éric Dubois - Stefan Fiesel 2015

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