Hands-on Econometrics for Beginners and Advanced Users

Hands-on Econometrics for Beginners and Advanced Users (H.E.B.A.) is strictly about practical econometrics. A “Do-As-I-Do” approach is adopted to engage students, enrolees and users. This HEBA course will cover topics from beginner category to advanced econometrics with practical real-life applications. You will be able to finish your dissertations/theses and manuscripts and interpret your results with greater confidence. Topics will include but not limited to the following: Classical Linear Regression Model, Dummy Variables, Causality Models, Limited Dependent Variable Models, Cointegration and Error Correction Models, Time-Varying Coefficient Models, Dynamic Heterogeneous Panels, Threshold Analysis, Quantile and Inter-Quantile Analysis, Distributed Lag Models, Structural Equation Models, Time Series Modelling and Forecasting, Duration Models, Modelling Long-Run Relationships, Modelling Volatility and Correlation, Switching and State Space Models, Traditional Panel Data Models, Panel Cointegration, Difference-in-Difference Models, Cross-Sectional Analysis…and so much more!

At the end of my course, students will be able to proficiently use both Stata and EViews analytical packages. In addition, they will be able to apply the knowledge gained to specify econometric models that fit their research hypotheses, analyse data, interpret results and complete their theses/manuscripts (publish in reputable Journals) within the shortest possible time frame. Specifically, the learning outcomes are:

1) Students will be able to use the EViews and Stata econometric software with much confidence.

2) Students will be able to learn and apply different estimation techniques.

3) Enrollees will be able to construct and interpret econometric models.

3) Students will improve on their analytical and results interpretation skills.

5) Improved report writing skills

6) Self-testing assessments

7) Increased confidence to publish research outputs in reputable high-impact Journals.