# Solved – How to read UNIT ROOT TEST results obtained from EVIEWS? I mean what values do we study to interpret our result

eviewshypothesis testingunit root

Null Hypothesis: D(OIL_PRICES) has a unit root


Exogenous: Constant
Lag Length: 0 (Automatic – based on SIC, maxlag=22)

        t-Statistic   Prob.*


Augmented Dickey-Fuller test statistic -37.22113 0.0000
Test critical values: 1% level -3.435299
5% level -2.863613
10% level -2.567923

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation
Dependent Variable: D(OIL_PRICES,2)
Method: Least Squares
Date: 11/29/14 Time: 18:57

Variable Coefficient Std. Error t-Statistic Prob.

D(OIL_PRICES(-1)) -1.042433 0.028006 -37.22113 0.0000
C 0.050065 0.043335 1.155289 0.2482

R-squared 0.522716 Mean dependent var -0.003875
Adjusted R-squared 0.522339 S.D. dependent var 2.230622
S.E. of regression 1.541651 Akaike info criterion 3.705162
Sum squared resid 3006.510 Schwarz criterion 3.713283
Log likelihood -2345.220 Hannan-Quinn criter. 3.708213
F-statistic 1385.413 Durbin-Watson stat 2.002267
Prob(F-statistic) 0.000000

You conducted a Augmented Dickey Fuller test. The hypothesis of this test are $H_0$: "Process has unit root" vs. $H_1$: "Process has no unit root". The test statistic is $-37.22113$. Now you need to compare this with the critical values under $H_0$. The critical values are given with:
$1\%: -3.435299 \\ 5\%: -2.863613 \\ 10\%: -2.567923.$
Since your test statistic is much lower than all of the critical values you can reject $H_0$ at a significance level $\ \ <1\%$. So you can conclude with a very low probability of making an error that your time series has no unit root. So, you can reject $H_0$.