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Grocer >> Johansen cointegration mmethod > johansen_eigen

johansen_eigen

calculate eigen values of a johansen procdure

CALLING SEQUENCE

[flag,lambda,dt,lr1,lr2,pi,s00]=johansen_eigen(dx,exo_st,exo_lt)

PARAMETERS

Input

* dx = a a (nobs x ny) vector of differentiated endogenous variables

* exo_st = a (nobs x (ny*k+nexo_st) vector of exogenous variables in the short term dynamic ( = the lagged differentiated endogenous variables + the exogenous variables outside the cointegration vectors)

* exo_lt = a (nobs x (ny+nexo_lt) vector of lagged endogenous variables (in level) and exogenous variables incorporated to the cointegration vectors

 

Output

* flag = a flag ('Ok'/'not OK') indicating whether the problem is well specified

* lambda = a (ny x 1) vector of eigenvalues of the reduced rank regression

* dt = a (nobs x ny) matrix, each column being a cointegration vector

* lr1 = a (ny x 1) vector of trace tests statistics

* lr2 = a (ny x 1) vector of lambda max

* pi = a ((ny+nexo_lt) x ny) matrix of combined effects of the variables in the cointegration relations on the differentiated endogenous variables

* s00 = a (ny x ny) matrix, equal to the variance of the residuals of the regression of dx on exo_st

DESCRIPTION

Calculate eigenvalues of a johansen procedure.

EXAMPLE

dy = tdiff(y,1);
exo=trimr([mlag(dy,grocer_k) exo_st],grocer_k+1,0)
lagy = trimr([lag(y,1) exo_lt],grocer_k+1,0)
dy=trimr(dy,grocer_k+1,0)
[flag,a,evec,lr1,lr2,pi]=johansen_eigen(dy,exo,lagy)
// Example taken from function johansen. Starting from a matrix y of variables in level,
// a matrix of short run exogenous variables exo_st and a matrix of long run exogenous variables exo_lt,
// builds the lagged matrix dy, the matrix of the short run dynamic exo_st and the matrix of
// the long run cointegration space exo_lt and calculates the corresponding cointegration eigenvalues and eigenvectors.

AUTHOR

Eric Dubois 2009

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