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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
Sample (adjusted): 1/06/2009 2/28/2014
Included observations: 1267 after adjustments

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

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  • $\begingroup$ I think I don't see some hypothesis testing in your result. There are some tests like the Dickey Fuller or KPSS test but you didn't give the results here. You probably have to state some where in EViews that you want to conduct such a test. Take a look at this youtube video. He explains how to do everything: youtube.com/watch?v=-ePFfyGxz4I $\endgroup$
    – random_guy
    Commented Nov 29, 2014 at 14:13
  • $\begingroup$ I posted an answer to your question. Did it help you. Could you please reply, accept, or vote for it? $\endgroup$
    – random_guy
    Commented Dec 1, 2014 at 19:57
  • $\begingroup$ Could you replace your written text by posting simply a screen shot of your outputP This will be highly beneficial for others who have the same question and possibly come to this page. $\endgroup$
    – random_guy
    Commented Dec 3, 2014 at 17:27
  • $\begingroup$ Sir can you help me with guiding how to vote for your answer? $\endgroup$
    – Faiza Sajjad
    Commented Dec 19, 2014 at 4:55
  • $\begingroup$ Hi Faiza, thank you for accepting my answer. However, for voting one needs at least 15 points and you have only 13 so far. I already up voted your question. So, I think I cannot do more. But maybe in the future when you have two points more you remember me and come back here to up vote. :) But you can also go to some questions or answers of other users and just edit them. I think an accepted edit gives 2 points. ;) Or you can just ask another good question and somebody will up vote you there. This gives 5 points. $\endgroup$
    – random_guy
    Commented Dec 19, 2014 at 10:05

2 Answers 2

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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 $H_0$.

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    $\begingroup$ Many Thanks for supporting me, this has let me clear my question up to some extent...:) $\endgroup$
    – Faiza Sajjad
    Commented Dec 19, 2014 at 4:53
  • $\begingroup$ alternatively you look at your p-value. if your p-value is less than 0.05, reject your null hypothesis at 5% significance level. $\endgroup$
    – user143159
    Commented Dec 22, 2016 at 4:29
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If t* > ADF crtitical value, ==> accept null hypothesis, i.e., unit root exists.. mean data is non stationary

If t* < ADF critical value, ==> reject null hypothesis, i.e., unit root does not exist. mean data is stationary

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