Stationarity tests
Augmented Dickey–Fuller and KPSS tests on one numeric series.
When to use it
Use these tests when you need to know whether a series looks stationary before ARIMA. ADF’s null is a unit root. KPSS’s null is stationarity around a constant. The two can disagree; that disagreement is the result to read. Autocorrelation is the other diagnostic.
Assumptions
The target is numeric, with at least eight non-missing values. ADF uses AIC lag selection. KPSS uses a constant (level stationarity) and automatic lags. There is no trend-stationarity option and no chart.
Running it in Tensr
Options
Prop
Type
Reading the output
The same sales series, which was built with a drift and a seasonal wiggle, not as white noise. ADF’s null is a unit root. KPSS’s null is level stationarity.
| Value | |
|---|---|
| Statistic | -1.7502 |
| p-value | p = .405 |
| Critical 1% | -3.6155 |
| Critical 5% | -2.9413 |
| Critical 10% | -2.6092 |
| Value | |
|---|---|
| Statistic | 0.3558 |
| p-value | p = .096 |
| Critical 10% | 0.3470 |
| Critical 5% | 0.4630 |
| Critical 2.5% | 0.5740 |
| Critical 1% | 0.7390 |
ADF p = .405 (null: unit root). KPSS p = .096 (null: level stationarity). Neither test rejects its null on this short, mildly drifting series, so the pair is inconclusive.
Reporting (APA 7)
ADF p = .405, KPSS p = .096 on 48 months of sales. Report both tests; they do not share a null.
Coming from SPSS
SPSS does not ship ADF and KPSS as a pair. Tensr’s item is Time series → Decomposition → Stationarity Tests. The path string stored for this item says Time series → Diagnostics → Stationarity. The item itself sits under Decomposition with STL.
Related
ARIMA / SARIMA is the forecast that usually follows. Autocorrelation shows leftover lag structure.