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I have several short (n=12) time series on which I am trying to conduct Augmented Dickey-Fuller tests, but I am getting very different/unexpected results:

import numpy as np
from statsmodels.tsa.stattools import adfuller
import matplotlib.pyplot as plt

series1 = [0.41625778, 0.40488883, 0.43044564, 0.44369687, 0.46613348, 0.44420775, 0.45091315, 0.48484614, 0.40252088, 0.43944978, 0.51613973, 0.49302274]
series2 = [0.35257984, 0.38692909, 0.39669828, 0.36296244,0.42035612,0.39374964, 0.41100085, 0.43182076, 0.40815853, 0.45394297, 0.41584767, 0.47399517]
series3 = [0.43749208, 0.45737407, 0.55615576, 0.48133939, 0.53273292, 0.49173426, 0.52919659, 0.56866738,0.53430795, 0.55901341, 0.54038404, 0.59699887]
series4 = [0.42294083, 0.45716565, 0.4782621, 0.48460774, 0.44784565, 0.41837626, 0.42410506, 0.47462612, 0.53232291, 0.48118058, 0.45748453, 0.54685356]

fig, ax = plt.subplots()
ax.plot(series1, label='series1')
ax.plot(series2, label='series2')
ax.plot(series3, label='series3')
ax.plot(series4, label='series4')
plt.legend()

print('#1 p-value: ' + str(adfuller(series1)[1]))
print('#2 p-value: ' + str(adfuller(series2)[1]))
print('#3 p-value: ' + str(adfuller(series3)[1]))
print('#4 p-value: ' + str(adfuller(series4)[1]))

which outputs:

series

#1 p-value: 0.17255361114708972
#2 p-value: 2.1591294791118934e-11
#3 p-value: 0.974897811933493
#4 p-value: 0.9655183938140642

While in 3 of these 4 cases the test results in p-values above reasonable significance levels (e.g. 0.05), in #2 the p-value is very low, therefore we can reject the existence of a unit root and assume the series is stationary. However, from the plot above all these series are somewhat similar to each other, and in fact #2 has a noticeable increasing trend (therefore suggested non-stationary). What can explain such a discrepancy in the obtained p-values? Is it caused by the limited number of samples/are ADF tests less 'robust' with short time series?

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1 Answer 1

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This answer would likely be better as a comment but I do not have the reputation.

You say,

in fact #2 has a noticeable increasing trend (therefore suggested non-stationary)

however a time series can have a trend and be stationary (trend-stationary) around that trend. There are three versions of the Dickey-Fuller test, one of which is a test of unit root in the presence of a deterministic trend.

Broadly speaking, 12 data points is likely not sufficient for a reliable ADF test. If you think about what is happening in the Dickey-Fuller test, we are testing for a significant relationship between the value of $$y(t-1)$$ and the change $$ \Delta y(t) = y(t) - y(t-1) .$$ In the ADF test, we also incorporate lagged values of the process $$\Delta y(t) $$ which means that we are using very few data points to test for a unit root.

With regards to the specific series #2, I agree it does appear trend-stationary by sight.

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  • $\begingroup$ I thought about trend stationarity, but python's adfuller accounts for that by default (i.e. trend stationary series should still fail the test). $\endgroup$
    – joaocandre
    Jun 6, 2023 at 0:23
  • $\begingroup$ Are there alternative stationarity (and/or serial correlation) tests able to deal with short time series? $\endgroup$
    – joaocandre
    Jun 6, 2023 at 0:25
  • $\begingroup$ > Are there alternative stationarity (and/or serial correlation) tests able to deal with short time series? The difficulty is stationarity is defined by the series statistical properties not depending on the time $t$ but you have so few time points to assess stationarity. $\endgroup$
    – pmxpp88
    Jun 8, 2023 at 5:41
  • $\begingroup$ Is there any rule-of-thumb regarding the minimum number of samples under which these tests aren't reliable? $\endgroup$
    – joaocandre
    Jun 9, 2023 at 18:00

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