Hi

i have some stock exchange time series data and i want the following analysis be done

The Dickey Fuller Test (sometimes called a Dickey Pantula test), which is based on linear regression. Serial correlation can be an issue, in which case the Augmented Dickey-Fuller (ADF) test can be used. The ADF handles bigger, more complex models. It does have the downside of a fairly high Type I error rate.

The Elliott-Rothenberg-Stock Test, which has two subtypes:

The P-test takes the error term's serial correlation into account,

The DF-GLS test can be applied to detrended data without intercept.

The Schmidt-Phillips Test includes the coefficients of the deterministic variables in the null and alternate hypotheses. Subtypes are the rho-test and the tau-test.

The Phillips-Perron (PP) Test is a modification of the Dickey Fuller test, and corrects for autocorrelation and heteroscedasticity in the errors.

The Zivot-Andrews test allows a break at an unknown point in the intercept or linear trend.

Kwiatkowski-Phillips-Schmidt-Shin (KPSS) for level or trend stationarity

I(d) test results

i) white noise disturbances

ii) AR(1) disturbances

iii) Bloomfield (m = 1) disturbances

In bold the selected models according to the deterministic terms. In parenthesis, the 95% confidence intervals of the non-rejection values of d.