impose and test restrictions on the cointegration relations
[res]=johansen_known_beta(res,nbrel,b,arg1,...,argn)
* res = a johansen result tlist
* nbrel = a scalar, the # cointegration relations
* b = a matrix, the imposed cointegration relations
* argi = arguments which can be:
- the string 'NBoot=n' where n is the number of bootstrap draws (default: 999)
- the string 'noprint' if the user doesn't want the to print the results of the regression
* res = a results tlist:
-res('namey') = the name of the variables (m x 1)
-res('y') = matrix of values for the variables (m x 1)
-res('namexo_lt') = the names of the exogenous variables in the cointegrating vectors
-res('exo_lt') = the matrix of the exogenous variables in the cointegrating vectors
-res('namexo_st') = the names of the exogenous variables in the short run dynamics of the VAR
-res('exo_st') = the matrix of the exogenous variables in the short run dynamics of the VAR
-res('dy') = the matrix of the differentiated endogenous variables
-res('exo') = the matrix of the variables in the short run dynamics (lagged differentiated endogenous variables + short run exogenous variables)
-res('lagy') = the matrix of the variables in the cointegrating relations (lagged endogenous variables + long run exogenous variables)
-res('nobs') = # of observations
-res('nvar') = # of variables
-res('nlags') = # of lags of the VAR
-res('eig') = eigenvalues (m x 1)
-res('evec') = eigenvectors (m x m)
-res('pi') = coefficients of the short run dynamics
-res('lr1') = likelihood ratio trace statistic for r=0 to m-1 (m x 1) vector
-res('lr2') = maximum eigenvalue statistic for r=0 to m-1 (m x 1) vector
-res('dropna') = boolean indicating if NAs have been dropped
-res('nonna') = vector indicating position of non-NA values (if the option 'dropna' was active)
-res('max non zeros') = maximum number of zeros a variable had to be considered as a dummy
-res('NBoot') = # of bootstrap draws
-res('alpha') = value of the error correction coefficients
-res('cvt') = critical values for trace statistic (m x 3) vector [90% 95% 99%]
-res('cvm') = critical values for max eigen value statistic (3 x m) vector [90% 95% 99%]
-res('p trace') = p-value for the trace statistic calculated with the standard bootstrap method
-res('p lmax') = p-value for the lambda-max statistic calculated with the standard bootstrap method
-res('p double trace') = p-value for the trace statistic calculated with the double bootstrap method
-res('p double lmax') = p-value for the lambda-max statistic calculated with the double bootstrap method
-res('prests') = boolean indicating the presence or absence of a time series in the regression
-res('bounds') = if there is a time series in the regression, the bounds of the regression
- res('nb of cointegration relations') = # of cointegration relations used for the test
- res('test type') = 'some ec vectors imposed'
- res('test stat') = value of the statistical test
- res('bootstrap test pvalue') = the p-value of the test statistic calculated with the standard bootstrap method
- res('fast double bootstrap test pvalue')= the p-value of the test statistic calculated with the double bootstrap method
- res('b') = matrix of the known cointegrating parameters
- res('test nb of draws') = NBoot
load(GROCERDIR+'\data\juselius.dat') // create dummies used for regression p.111-112 post83q1=dummy(['1973q1';'2003q1'],['1983q1';'2003q1']); dum75q4=dummy(['1973q1';'2003q1'],'1975q4')-0.5*dummy(['1973q1';'2003q1'],['1976q1';'1976q2']); dum76q4=dummy(['1973q1';'2003q1'],'1976q4'); dum83q2=dummy(['1973q1';'2003q1'],'1983q2'); dum83q1=dummy(['1973q1';'2003q1'],'1983q1'); rj2=johansen(1,'dnk_Lm3rC','dnk_Lyr','dnk_DLpy','dnk_Rm','dnk_Rb',... 'exo_lt=post(1983q1)','exo_st=const;dum75q4;dum76q4;dum83q1') rjtest11=johansen_known_beta(rj2,3,[0;0;1;0;-1;0]) // Example taken from function johansen_d(). // The first argument is the name of the result tlist stemming from a johansen estimation on Danish data. // The number of cointegration relations is set to 3. The known cointegration vector is [0;0;1;0;-1;0]. | ![]() | ![]() |