adf unit root test
[result]=adf(namey,p,l,arg1,...,argn)
* 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
* p = order of time polynomial in the null-hypothesis
- p = -1, for no added term
- p = 0, for constant term
- p = 1, for constant plus time-trend
- p > 1, for higher order polynomial
* l = # of lags of the ADF test
* arg1,...,argn = optional arguments which can be:
- 'noprint' if the user doesn't want to print the results of the regression
- 'dropna' if the user wants to remove the NA values from the data
* resadf = results tlist with:
- resadf('meth') = 'adf'
- resadf('y') = y data vector of the auxiliary regression
- resadf('x') = x data matrix of the auxiliary regression
- resadf('nobs') = # observations
- resadf('nvar') = # variables
- resadf('beta') = bhat
- resadf('yhat') = yhat
- resadf('resid') = residuals of the auxiliary regression
- resadf('vcovar') = estimated variance-covariance matrix of beta
- resadf('sige') = estimated variance of the residuals
- resadf('sigu') = sum of squared residuals
- resadf('ser') = standard error of the regression
- resadf('tstat') = t-stats
- resadf('pvalue') = pvalue of the betas
- resadf('dw') = Durbin-Watson Statistic
- resadf('condindex') = multicolinearity cond index
- resadf('prescte') = boolean indicating the presence or absence of a constant in the regression
- resadf('rsqr') = rsquared
- resadf('rbar') = rbar-squared
- resadf('f') = F-stat for the nullity of coefficients other than the constant
- resadf('pvaluef') = its significance level
- resadf('prests') = boolean indicating the presence or absence of a time series in the regression
- resadf('namey') = name of the y variable of the auxiliary regression
- resadf('namex') = name of the x variables of the auxiliary regression
- resadf('bounds') = if there is a timeseries in the regression, the bounds of the regression
- resadf('like') = log-likelihood of the regression
- resadf('1% level') = 1% critical level
- resadf('5% level') = 5% critical level
- resadf('10% level') = 10% critical level
- resadf('dropna') = boolean indicating if NAs have been droped
- resadf('nonna') = vector indicating position of non-NA values (if the option 'dropna' was active)
bounds() load(GROCERDIR+'/data/cousa.dat') adf('log(inc)',1,4) // test if the log of income is I(1) with trend adf('log(inc)',0,4) // test if the log of income is I(1) with constant // Examples taken from function adf_d. | ![]() | ![]() |