Questions tagged [augmented-dickey-fuller]

A test of the [null] hypothesis that a time series has a unit root (ie, that it is non-stationary).

== Testing procedure == From Wikipedia's ADF testing procedure. This, the ADF-test is often used in conjunction with the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for stationarity and detrending (ADF/KPSS) of time series as in the KPSS-test the [null] and alternate hypotheses are switched or opposite those of the ADF test, i.e., one the two tests will be significant. The testing procedure for the ADF test is the same as for the Dickey–Fuller test but it is applied to the model

$$\Delta y_t = \alpha + \beta t + \gamma y_{t-1} + \delta_1 \Delta y_{t-1} + \cdots + \delta_{p-1} \Delta y_{t-p+1} + \varepsilon_t, $$

where $\alpha$ is a constant, $\beta$ the coefficient on a time trend and $p$ the lag order of the autoregressive process. Imposing the constraints $\alpha = 0$ and $\beta = 0$ corresponds to modelling a random walk and using the constraint $\beta = 0$ corresponds to modeling a random walk with a drift. Consequently, there are three main versions of the test, analogous to Dickey–Fuller test.

By including lags of the order ''p'' the ADF formulation allows for higher-order autoregressive processes. This means that the lag length ''p'' has to be determined when applying the test. One possible approach is to test down from high orders and examine the t-values on coefficients. An alternative approach is to examine information criteria such as the Akaike information criterion, Bayesian information criterion or the Hannan–Quinn information criterion.

The unit root test is then carried out under the null hypothesis $\gamma = 0$ against the alternative hypothesis of $\gamma < 0.$ Once a value for the test statistic

$$DF_\tau = \frac{\hat{\gamma}}{SE(\hat{\gamma})}$$

is computed it can be compared to the relevant critical value for the Dickey–Fuller Test. If the test statistic is less (this test is non symmetrical so we do not consider an absolute value) than the (larger negative) critical value, then the null hypothesis of $\gamma = 0$ is rejected and no unit root is present.

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What is the difference between a stationary test and a unit root test?

What is the difference between the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test and the augmented Dickey-Fuller (ADF) test? Are they testing the same thing? Or do we need to use them in different situations?
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How to know if a time series is stationary or non-stationary?

I am using R, I searched on Google and learnt that kpss.test(), PP.test(), and adf.test() are used to know about stationarity of time series. But I am not a statistician, who can interpret their results > PP.test(x) Phillips-Perron Unit Root…
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Which Dickey-Fuller test for a time series modelled with an intercept/drift and a linear trend?

Short version: I have a time series of climate data that I'm testing for stationarity. Based on previous research, I expect the model underlying (or "generating", so to speak) the data to have an intercept term and a positive linear time trend. To…
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Nice example where a series without a unit root is non stationary?

I've seen several times people reject the null in an augmented Dickey-Fuller test, and then claim that it shows their series is stationary (unfortunately, I cannot show the sources of these claims, but I imagine similar claims exist here and there…
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Test for cointegration between two time series using Engle–Granger two-step method

I am seeking to test for cointegration between two time series. Both series have weekly data spanning ~3 years. I am trying to do the Engle-Granger Two Step Method. My order of operations follows. Test each time series for unit root via Augmented…
d0rmLife
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R: Augmented Dickey Fuller (ADF) test

I'm having a problem with the Dickey-Fuller p-values and test statistic for unit root test in R. I tried using functions: urca::ur.df() fUnitRoots::adfTest() tseries::adf.test() All of them showed different results for the same test settings (lag,…
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ADF test suggesting incorrectly that series is stationary

The code below generates series y, which by design is clearly non-stationary. The ADF test below was run with 12 lags to yield (what visually appear to be) uncorrelated residuals and it would have us conclude that y is stationary. What went wrong…
ColorStatistics
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Contradictory results of ADF and KPSS unit root tests

To check whether the data is stationary or not, I computed KPSS and ADF test and got the following results adf.test(td,alternative = "stationary") Augmented Dickey-Fuller Test data: td Dickey-Fuller = -3.7212, Lag order = 3, p-value =…
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Explosive processes, non-stationarity and unit roots, how to distinguish?

I understand that if we have a simple model such as: $$Y_t=\rho Y_{t-1}+\epsilon_t$$ where $\rho$ is less than one in absolute value then we have a stationary process. If $\rho$ equals one then we have a unit-root and we can use the augmented Dickey…
Goose
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Unit root tests and stationarity

Two common methods of testing whether a time series is stationary are the KPSS and ADF tests. If my understanding is correct, these tests essentially work by measuring the residuals of fitting the time-series to an autoregressive model which is…
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How is the augmented Dickey–Fuller test (ADF) table of critical values calculated?

Could you please explain in simple terms how the table of critical values for the augmented Dickey–Fuller (ADF) test is created?
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Best practice for ADF/KPSS unit root testing sequence?

I've been quite confused by the various unit root testing strategies recommended in the literature, so I was hoping others may have some advice on the best way to proceed using ADF and KPSS tests. Pfaff (2008) describes the following procedure using…
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What are the assumptions for checking the stationarity of a time series?

I am checking stationarity or non-stationarity of a time series with R and I am using adf.test and kpss.test in tseries package. What are the assumptions for these tests? Is following a Gaussian distribution by the data set one of the…
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Why is the dickey fuller test different from a simple t-test

I am trying to understand why should there be different distribution for t-statistic, in case of AR model, Dickey-Fuller test For e.g. Say, the model is $Y_t = \beta_lY_{t-1} + \varepsilon_{t}$. Why should I not use Simple linear regression model…
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Why is this time-series stationary?

I am using python for time-series analysis of count data and came across a problem where I have a time-series that to me looks non-stationary but the Augmented Dickey-Fuller test (implemented in statsmodels) rejects the null hypothesis quite…
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