Econometrics
About the Course
Learn to test economic relationships using data and build an econometric model whose results can be interpreted and evaluated.
This course covers hypothesis formulation, data preparation, regression, significance testing, and model diagnostics.
Practical applications help you distinguish correlation from causation, understand the limitations of estimates and forecasts, and communicate findings clearly for research and decision-making.
Discussions look beyond the output of statistical software to the choices behind it, starting with a fundamental question: has the variable been clearly defined?
Who Is This Course For?
- Researchers and postgraduate students in economics and finance.
- Data analysts and economic and financial policy analysts.
- Research, planning, and decision support personnel.
Topics covered:
- Formulating economic questions and hypotheses and selecting variables and data sources, explaining expected relationships and the rationale for including each variable.
- Preparing and describing data and addressing missing values and unusual observations, documenting treatment decisions and their possible effects on the sample and results.
- Simple and multiple linear regression, parameter estimation, and interpretation, distinguishing the size and direction of an effect and explaining its economic meaning.
- Model assumptions, significance tests, confidence intervals, and goodness-of-fit measures, recognizing that statistical fit alone does not establish a sound economic explanation.
- Diagnosing multicollinearity, heteroskedasticity, and autocorrelation, understanding how each issue affects estimates or statistical inference.
- Time series analysis fundamentals, stationarity, and forecasting limitations, considering temporal order, structural changes, and the distinction between estimation and prediction.
- Correlation, causation, bias, and omitted variables, examining alternative explanations and keeping conclusions within the limits of the methodology.
- A practical exercise estimating a model and reporting findings and methodological limitations, examining sensitivity to data selection, preparation, and model specification.
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