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    <p>I will give example using <code>urca</code> package in <code>R</code>. </p> <pre><code>library(urca) data(npext) # This is the data used by Nelson and Plosser (1982) sample.data&lt;-npext head(sample.data) year cpi employmt gnpdefl nomgnp interest indprod gnpperca realgnp wages realwag sp500 unemploy velocity M 1 1860 3.295837 NA NA NA NA -0.1053605 NA NA NA NA NA NA NA NA 2 1861 3.295837 NA NA NA NA -0.1053605 NA NA NA NA NA NA NA NA 3 1862 3.401197 NA NA NA NA -0.1053605 NA NA NA NA NA NA NA NA 4 1863 3.610918 NA NA NA NA 0.0000000 NA NA NA NA NA NA NA NA 5 1864 3.871201 NA NA NA NA 0.0000000 NA NA NA NA NA NA NA NA 6 1865 3.850148 NA NA NA NA 0.0000000 NA NA NA NA NA NA NA NA </code></pre> <p>I will use <code>ADF</code> to perform the unit root test on <code>industrial production index</code> as an illustration. The <code>lag</code> is selected based on the <code>SIC</code>. I use trend as there is trend in the date . </p> <pre><code> ############################################### # Augmented Dickey-Fuller Test Unit Root Test # ############################################### Test regression trend Call: lm(formula = z.diff ~ z.lag.1 + 1 + tt + z.diff.lag) Residuals: Min 1Q Median 3Q Max -0.31644 -0.04813 0.00965 0.05252 0.20504 Coefficients: Estimate Std. Error t value Pr(&gt;|t|) (Intercept) 0.052208 0.017273 3.022 0.003051 ** z.lag.1 -0.176575 0.049406 -3.574 0.000503 *** tt 0.007185 0.002061 3.486 0.000680 *** z.diff.lag 0.124320 0.089153 1.394 0.165695 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.09252 on 123 degrees of freedom Multiple R-squared: 0.09796, Adjusted R-squared: 0.07596 F-statistic: 4.452 on 3 and 123 DF, p-value: 0.005255 Value of test-statistic is: -3.574 11.1715 6.5748 Critical values for test statistics: 1pct 5pct 10pct tau3 -3.99 -3.43 -3.13 phi2 6.22 4.75 4.07 phi3 8.43 6.49 5.47 </code></pre> <p>#Interpretation: <code>BIC</code> selects the <code>lag 1</code> as optimal lag. The test statistics <code>-3.574</code> is less than the critical value <code>tau3</code> at 5 percent (-3.430). So, the null that there is an unit root is is rejected only at <code>5 percent</code>.</p> <p>Also, check the free forecasting book available <a href="http://otexts.com/fpp/8/1/" rel="nofollow">here</a></p>
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    1. POhow to identify time series is stationarity or not by package fUnitRoots in R language
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    1. COok, thank you Metrics so much. I am a beginner, let me check it by typing them into R terminal. thank you.
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    2. CO@ hylepo: My answer assumes that you are familiar with the theoretical stuff on unit root tests.
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    3. COthank you so much. i think i got some very useful information, based one which i could dig much more, thanks
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