The ARDL model can be transformed into an error correction form to separate short-term effects from long-term equilibrium discrepancies. When dealing with nonstationary variables, regression can lead to spurious results. However, nonstationary time series can cointegrate, where a combination of processes becomes stationary. Cointegration simplifies empirical modeling and makes it easier to interpret. Tests for cointegration are crucial, along with methods for estimating cointegration regressions.