Free online courseEconometrics

Course Description

Make better corporate finance and business decisions by learning how to extract credible insights from data. This econometrics course is designed to help you move beyond simple correlations and into evidence-based reasoning: when does a variable truly influence another, how confident can you be, and what can go wrong when models are built on imperfect real-world data?

You will build a strong foundation in the logic of econometric analysis, starting with samples versus populations, sampling error, and the role of estimators. You will learn what makes an estimator useful in practice—properties such as unbiasedness, consistency, and efficiency—and why those ideas matter when you rely on models to guide pricing, investment, risk, or performance decisions.

From there, the course develops the intuition and mathematics behind linear regression and least squares. You will see how lines of best fit are derived and interpreted, why the Gauss-Markov assumptions are central to ordinary least squares, and what happens when reality violates them. Common pitfalls are treated seriously: omitted variable bias, reverse causality, measurement error, functional form mistakes, multicollinearity, heteroskedasticity, and serial correlation. Instead of memorizing rules, you will learn to recognize symptoms, understand consequences, and think clearly about remedies.

Inference is a major focus. You will practice hypothesis tests, confidence intervals, and model comparison tools, while developing a practical understanding of standard errors, t tests, F tests, and when R-squared can mislead. As you progress, you will also explore strategies for better identification, including weighted least squares, generalized least squares, and instrumental variables with two stage least squares—tools that help when endogeneity threatens the validity of your conclusions.

Finally, the course introduces time series thinking: stationarity, spurious regression, autoregressive and moving average processes, unit roots, and cointegration. By the end, you will be equipped to read econometric results critically, build more defensible models, and communicate findings with the rigor expected in finance and analytics roles.