Year 2 · Core Finance · Macro & Economics
Econometrics Foundations
Build the statistical toolkit used to study financial and economic relationships: regression, OLS, coefficients, statistical significance, confidence intervals, R², model assumptions, common diagnostic problems and the limits of causal interpretation.
LEARNING OBJECTIVES
What you should be able to explain after this lesson
01
Distinguish descriptive statistics from econometric modeling.
02
Interpret simple and multiple linear regressions and OLS coefficients.
03
Understand residuals, standard errors, t-statistics, p-values and confidence intervals.
04
Interpret R² and adjusted R² without confusing fit with causality.
05
Recognize omitted-variable bias, multicollinearity, heteroskedasticity and autocorrelation.
06
Apply econometric reasoning to finance and market questions while respecting model limitations.
CONCEPT MAPFrom economic question to defensible empirical conclusion
Econometrics combines economic questions with statistical evidence
CORE FACTS · SAME IN EVERY MODE
Econometrics uses statistical methods to quantify relationships in economic and financial data.
A useful empirical model begins with a clear question and a theory-driven choice of variables.
Regression can reveal association, prediction and conditional relationships; causality requires stronger identification.
A statistically significant coefficient is not automatically economically important.
BeginnerTeaching style changes, knowledge does not.
Econometrics is the part of economics that asks: can we use real data to measure whether two things move together, by how much, and how confident we are in that relationship?
WORKED EXAMPLE
A researcher asks whether monthly stock returns are related to market returns. A regression can estimate the conditional sensitivity, but it does not by itself prove a causal mechanism.
PROFESSIONAL VOCABULARYDependent variable / variable dépendante Explanatory variable / variable explicative Identification / identification
The outcome the model attempts to explain.
A variable used to explain variation in the dependent variable.
The logic that allows a parameter or causal effect to be distinguished from alternatives.