Quality research begins with reliable and comprehensive unit root testing.
Inadequate unit root testing jeopardizes research results.
Most time series and panel data estimation techniques depend on establishing whether data is stationary or results can be unreliable.
Standard unit root tests can’t capture the complexities of real-world data.
Failing to account for characteristics such as structural breaks or panel data relationships means losing important information.
Standard unit root tests can’t capture the complexities of real-world data.
Failing to account for characteristics such as structural breaks or panel data relationships means losing important information.
GAUSS offers the most comprehensive unit root testing tools for real-world applications.
✔ Time series unit root testing.
✔ Panel data unit root testing.
✔ Support for multiple structural breaks.
✔ Granger causality tests.
✔ Cointegration tests.
✔ Cointegration tests with regime and trend shifts.
✔ Fourier approximation tests for smooth breaks.
More than 50 tests developed from papers combining for over 165,000 citations.
Clear and Complete Results
- Test statistics and accompanying critical values.
- Break point locations (where relevant).
- Plain language results.
Two breaks ADF test (Narayan & Popp, 2010)
--------Model C: Break in level & trend-----
ADF-stat -5.1555
Break Date One 02/14/2012
Fraction One 0.4324
Break Date Two 07/23/2017
Fraction Two 0.7748
Lag 3
Critical Values:
1% 5% 10%
-5.58 -4.94 -4.60
Reject the null hypothesis
of a unit root at the 5% level.
Full running examples for every test.
Testimonials
Expert support makes it easy
I was intimidated by GAUSS at first, but the support team made it easy. They helped me get started and I was even able to run complicated models.
Esmaeil Ebadi, Lecturer UW-Whitewater
Simple and comprehensive
Having all these tests at my fingertips in a consistent and growing package makes my work much easier.
John van Horn, Researcher