ADF statistic for residuals from a cointegrating regression
[rescadf]=cadf(p,l,namey,arg1,...,argn)
* p= order of time polynomial in the null-hypothesis
- p = -1, no deterministic part
- p = 0, for constant term
- p = 1, for constant plus time-trend
- p > 1 returns no critical values
* l= # of lagged changes of the residuals to include in regression
* namey= a time series, a real (nx1) vector or a string equal to the name of a time series or a (nx1) real vector between quotes
* argi= arguments which can be:
- a time series
- a real (nx1) vector
- a string equal to the name of a time series or a (nx1) real vector between quotes
- the string 'noprint' if the user doesn't want to print the results of the regression
- the string 'dropna' if the user wants to remove the NA values from the data
* rcadf = a tlist with
. all of the arguments of the second stage regression
and:
. rcadf('cointrel') = tlist with all the arguments of the first stage regression (see ols() for a description of all these arguments)
load(GROCERDIR+'/data/datajpl.dat') r = cadf(0,6,'illinos','indiana') // Example taken from function cadf_d. Illinos and indiania are the names of two variables taken from data base jpl.dat // and these names will be used for printings. First entry is set to 0, which means that no trend is imposed in the // cointegrating regression. second entry is set to 6, which means that there are 6 lags to the residuals in the second // stage regression. r = cadf(1,4,illinos,indiana) // Now, a trend is included in the cointegrating regression, 4 lags on residuals in the second stage // regression and the variables are named 'endogenous' and 'exogenous' respectively when the results are printed. | ![]() | ![]() |