estimates a VAR model
rvar=VAR(p,arg1,...argn)
* p = the lag length of the VAR
* argi = an argument which can be:
- 'endo=[var1;var2; ... ;varn]' or 'endo=var1;var2; ... ;varn' with vari the name of an admissible variable for explox (a real vector, a ts, a real matrix or a list of such variables)
- 'exo=[var1;var2; ... ;varn]' or 'exo=var1;var2; ... ;varn' with vari the name of an admissible variable for explox (a real vector, a ts, a real matrix or a list of such variables)
- the string 'noprint' if the user doesn't want to print the results of the regression
- the string 'nocte' if the user doesn't want a constant in the regression
- the string 'dropna' if the user wants to remove the NA values from the data
* rvar = a results tlist with:
. rvar('meth') = 'var'
. rvar('yall') = y data vector agged data included
. rvar('y') = y data vector
. rvar('x') = x data matrix
. rvar('nobs') = # observations
. rvar('nvar') = # exogenous variables
. rvar('neqs') = # endogenous variables
. rvar('resid') = residuals, with rvar('resid')(:,i): residuals for equation # i
. rvar('beta') = bhat, with rvar('beta')(:,i): coefficients for equation # i
. rvar('rsqr') = rsquared, with rvar('rsqr')(i) : rsquared for equation # i
. rvar('overallf') = F-stat for the nullity of coefficients other than the constant
with: rvar('overallf')(i): F-stat for equation # i
. rvar('pvaluef') = their significance level with: rvar('pvaluef')(i): significance level for equation # i
. rvar('rbar') = rbar-squared
. rvar('sigu') = sums of squared residuals with rvar('sigu')(:,i): sum of squared residuals for equation # i
. rvar('ser') = standard errors of the regression with rvar('ser')(i): standard error for equation # i
. rvar('tstat') = t-stats, with rvar('tstat')(:,i): t-stat for equation # i
. rvar('pvalue')= pvalue of the betas, with rvar('pvalue')(:,i): p-value for equation # i
. rvar('dw') = Durbin-Watson Statistic, with: rvar('dw')(i): DW for equation # i
. rvar('condindex') = multicolinearity cond index, with rvar('condindex')(i): cond index for equation # i
. rvar('boxq') = Box Q-stat, with rvar('boxq')(i): Box Q-stat for equation # i
. rvar('sigma') = (neqs x neqs) var-covar matrix of the regression
. rvar('aic') = Akaïke information criterion
. rvar('bic') = Schwartz information criterion
. rvar('hq') = Hannan-Quinn information criterion
. rvar('prests') = boolean indicating the presence or absence of a time series in the regression
. rvar('namey') = name of the y variable
. rvar('nx') = # of x variables
. rvar('namex') = name of the x variables (if any)
. rvar('dropna') = boolean indicating if NAs have been dropped
. rvar('bounds') = if there is a timeseries in the forecast, the bounds of the regression
. rvar('nonna') = vector indicating position of non-NAs
load(GROCERDIR+'/data/lutk1.dat') bounds('1960q4','1978q4') results=VAR(2,'endo=delts(log(rfa_inv));delts(log(rfa_inc));delts(log(rfa_cons))') // Example taken from function var_d1() estimate a var model, taken from Lütkepohl's book "Introduction to multiple time series analysis", // with three endogenous variables (the differentiated logarithm of investment, GDP and consumption) with 2 lags and no exogenous variables. | ![]() | ![]() |