Augmented Dickey-Fuller test for LEU28 MOVEMENT QUANTITY_IN_100KG/1000 Is a value between upper bound or four minus upper bound and two. Hypothesis for the Durbin-Watson test are: Statistic was for testing autocorrelation in the sample. Since the test results is lower than the critical value, one must reject the null hypothesis and,Ĭoncluded that there is no unit-root in the sample and the variable is stationary. Augmented Dickey-Fuller test for Calendar ConstructionUpdate regression of DCalendarĬoefficient Std.Error t-value Calendar ConstructionUpdate_1 -0,73061 0,13458 -5,4289ĭCalendar ConstructionUpdate_1 0,3013 0,10797 2,7907 DCalendar ConstructionUpdate_2 -0,072707 0,10223 -0,71118Ĭritical values used in ADF test: -3,453 -4,049 ADF-Calendar ManufacturingUpdate -5,429 **įrom table 2 one can see that variable construction is stationary, the ADF-test result is -5.429, which Since null hypothesis is not rejected, one can conclude that series values are not correlated Indeed is between the critical value 1.78 and 2, one must conclude that the test fails to reject the null With the Durbin-Watson test, one must remember that test value that fails to reject null hypothesis, H0: Series values are not correlated with each other H1: Series values are correlated with each other

That there is no unit-root in the sample and the variable is stationary. Since the test results is lower than the critical value, one must reject the null hypothesis and conclude

H0: There is a unit-root in the sample H1: There is not a unit-root in the sample Which is smaller than the 1 % critical value. Augmented Dickey-Fuller test for Calendar ManufacturingUpdate regression ofĬoefficient Std.Error t-value Calendar ManufacturingUpdate_1 -1,3579 0,1736 -7,8236ĭCalendar ManufacturingUpdate_1 0,4185 0,1304 3,2083 DCalendar ManufacturingUpdate_2 0,1559 0,0992 1,5721Ĭritical values used in ADF test: -3,453 -4,048 ADF-Calendar ManufacturingUpdate -7,824 **įrom table 1 one can see that variable manufacturing is stationary, the ADF-test result is -7.824, Testing for stationarity with Dickey-Fuller From previous paragraphs, one knows that theseĪre tested with Dickey-Fuller and Durbin-Watson tests. Whether there is autocorrelation with-in the variable. To use variables in a regression model, one must first test whether the variables are stationary and Interpreting the Durbin-Watson test static (compiled by the author).įor example, when one has ~ sample size of 80 and 4 variables, the upper bound for Durbin-Watson Where 𝑑 𝑙 denotes the lower bound and 𝑑 𝑢 the upper bound of the area of uncertainty.įigure 4.

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