Introduction to Quantitative Methods

QMT 2026

Introduction to Quantitative Methods

A modern, applied approach to econometrics and statistical reasoning — integrating theory, data, and computation in R.

1 About This Course

This course introduces quantitative methods for economic analysis. You will learn how to frame economic questions as statistical models, estimate those models using real data, interpret results correctly, and diagnose violations of key assumptions.

The course is built around R — a free, open-source statistical computing environment. Every concept is illustrated with real data and live code. By the end you will be able to conduct a full regression analysis from raw data to polished, publication-ready output.

What You Will Learn

Specify and estimate linear regression models for cross-sectional and time-series data
Conduct and interpret hypothesis tests for economic hypotheses
Detect and correct violations of classical assumptions (heteroskedasticity, serial correlation)
Produce professional regression tables and visualisations in R
Apply critical thinking to causal claims in economics

2 Course Structure

The course is organised into five modules across twelve chapters.

Module 1 — Chapters 1–2

Foundations

What econometrics does, the data we use, and the statistical machinery behind it. Causality, correlation, and why the distinction matters.

Module 2 — Chapters 3–5

The Linear Regression Model

From simple linear regression through to inference. How OLS works, why it works, and what we can test with it.

Module 3 — Chapters 6–7

Model Building

Multiple regression, functional form, dummy variables, and interaction effects. Making models more realistic and interpreting richer output.

Module 4 — Chapters 8–10

Diagnostics

Large-sample theory, heteroskedasticity, and serial correlation. What goes wrong when classical assumptions fail and how to fix it.

Module 5 — Chapters 11–12

Time Series

Dynamic models, distributed lags, non-stationarity, and unit root tests. The special challenges of economic time series data.

3 Weekly Schedule

Chapter	Topic	Module
1	Introduction to Quantitative Methods	Foundations
2	Mathematical & Statistical Foundations	Foundations
3	Simple Linear Regression	Linear Regression
4	Properties of OLS — BLUE & Gauss-Markov	Linear Regression
5	Inference & Hypothesis Testing	Linear Regression
6	Multiple Regression & Specification	Model Building
7	Functional Form, Dummies & Interactions	Model Building
8	Large Sample Properties	Diagnostics
9	Heteroskedasticity	Diagnostics
10	Serial Correlation	Diagnostics
11	Dynamic Models & Distributed Lags	Time Series
12	Non-Stationarity & Unit Roots	Time Series

4 Software

This course uses R and RStudio. See the Resources page for installation instructions and the complete list of packages used.

--- title: "Introduction to Quantitative Methods" subtitle: "QMT 2026" page-layout: full toc: false --- ```{=html} <div class="hero"> <h1>Introduction to Quantitative Methods</h1> <p>A modern, applied approach to econometrics and statistical reasoning — integrating theory, data, and computation in R.</p> </div> ``` ## About This Course This course introduces quantitative methods for economic analysis. You will learn how to frame economic questions as statistical models, estimate those models using real data, interpret results correctly, and diagnose violations of key assumptions. The course is built around **R** — a free, open-source statistical computing environment. Every concept is illustrated with real data and live code. By the end you will be able to conduct a full regression analysis from raw data to polished, publication-ready output. ::: {.callout-note} ## What You Will Learn - Specify and estimate linear regression models for cross-sectional and time-series data - Conduct and interpret hypothesis tests for economic hypotheses - Detect and correct violations of classical assumptions (heteroskedasticity, serial correlation) - Produce professional regression tables and visualisations in R - Apply critical thinking to causal claims in economics ::: --- ## Course Structure The course is organised into five modules across twelve chapters. ::: {.grid} ::: {.g-col-12 .g-col-md-6} <div class="module-card"> <div class="module-number">Module 1 — Chapters 1–2</div> **Foundations** What econometrics does, the data we use, and the statistical machinery behind it. Causality, correlation, and why the distinction matters. </div> ::: ::: {.g-col-12 .g-col-md-6} <div class="module-card"> <div class="module-number">Module 2 — Chapters 3–5</div> **The Linear Regression Model** From simple linear regression through to inference. How OLS works, why it works, and what we can test with it. </div> ::: ::: {.g-col-12 .g-col-md-6} <div class="module-card"> <div class="module-number">Module 3 — Chapters 6–7</div> **Model Building** Multiple regression, functional form, dummy variables, and interaction effects. Making models more realistic and interpreting richer output. </div> ::: ::: {.g-col-12 .g-col-md-6} <div class="module-card"> <div class="module-number">Module 4 — Chapters 8–10</div> **Diagnostics** Large-sample theory, heteroskedasticity, and serial correlation. What goes wrong when classical assumptions fail and how to fix it. </div> ::: ::: {.g-col-12 .g-col-md-12} <div class="module-card"> <div class="module-number">Module 5 — Chapters 11–12</div> **Time Series** Dynamic models, distributed lags, non-stationarity, and unit root tests. The special challenges of economic time series data. </div> ::: ::: --- ## Weekly Schedule | Chapter | Topic | Module | |------|-------|--------| | [1](weeks/week01.qmd) | Introduction to Quantitative Methods | Foundations | | [2](weeks/week02.qmd) | Mathematical & Statistical Foundations | Foundations | | [3](weeks/week03.qmd) | Simple Linear Regression | Linear Regression | | [4](weeks/week04.qmd) | Properties of OLS — BLUE & Gauss-Markov | Linear Regression | | [5](weeks/week05.qmd) | Inference & Hypothesis Testing | Linear Regression | | [6](weeks/week06.qmd) | Multiple Regression & Specification | Model Building | | [7](weeks/week07.qmd) | Functional Form, Dummies & Interactions | Model Building | | [8](weeks/week08.qmd) | Large Sample Properties | Diagnostics | | [9](weeks/week09.qmd) | Heteroskedasticity | Diagnostics | | [10](weeks/week10.qmd) | Serial Correlation | Diagnostics | | [11](weeks/week11.qmd) | Dynamic Models & Distributed Lags | Time Series | | [12](weeks/week12.qmd) | Non-Stationarity & Unit Roots | Time Series | --- ## Software This course uses **R** and **RStudio**. See the [Resources](resources.qmd) page for installation instructions and the complete list of packages used.