ARDL Model Course
Master the ARDL modelling framework from unit root testing through nonlinear extensions and applied research projects. This course gives economists, researchers, and graduate learners the technical skills to estimate long-run relationships, interpret error correction dynamics, and produce publication-quality empirical analyses with confidence.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
You will build a complete ARDL modelling skill set, starting with time series fundamentals such as stationarity, unit root testing, and cointegration theory. You will learn to specify and estimate ARDL models using information criteria, apply the Pesaran, Shin, and Smith bounds testing procedure, and extract both long-run multipliers and short-run dynamics. The course extends into nonlinear ARDL for asymmetric analysis and covers panel ARDL estimators including PMG and MG. You will also practise Granger and Toda-Yamamoto causality testing, forecasting, and diagnostic validation. By the final projects, you will execute the full ARDL workflow on real datasets and present findings in professional research reports.
Elevify advantages
Develop skills
- Bounds Testing Execution: Apply the PSS bounds test to confirm long-run cointegration between variables.
- Lag Order Selection: Use AIC, SIC, and HQC criteria to specify a well-identified ARDL model.
- Error Correction Estimation: Derive and interpret short-run dynamics and speed-of-adjustment coefficients accurately.
- Asymmetric ARDL Modelling: Decompose regressors into partial sums and test asymmetry using the NARDL framework.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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