Syllabus

QMT 2026 — Introduction to Quantitative Methods

1 Course Overview

This unit introduces students to quantitative methods used in economic analysis. It covers linear regression, inference, model diagnostics, and an introduction to time series econometrics. All analysis is conducted in R, with an emphasis on reproducible, well-documented work.

2 Learning Outcomes

By the end of this unit, students will be able to:

Explain the role of regression analysis in empirical economics
Specify, estimate, and interpret linear regression models
Conduct hypothesis tests and construct confidence intervals for economic parameters
Diagnose and address violations of classical regression assumptions
Apply regression methods to time series data and test for non-stationarity
Produce professional, reproducible empirical analysis using R

3 Module Structure

3.1 Module 1: Foundations (Chapters 1–2)

Chapter 1 — Introduction to Quantitative Methods

The scope and purpose of quantitative methods in economics
Causality vs. correlation: why the distinction matters
Types of economic data: cross-section, time series, panel
Introduction to R and the tidyverse

Chapter 2 — Mathematical and Statistical Foundations

Probability and random variables
Key distributions: Normal, $t$, $F$, $\chi^2$
Hypothesis testing: logic, errors, and power
Confidence intervals and p-values

3.2 Module 2: The Linear Regression Model (Chapters 3–5)

Chapter 3 — Simple Linear Regression

The population regression function
OLS estimation: derivation and mechanics
Interpretation of slope and intercept
Goodness of fit: $R^2$ and the standard error of regression

Chapter 4 — Properties of OLS: BLUE and Gauss-Markov

The classical linear model assumptions
Unbiasedness of OLS
The Gauss-Markov theorem: OLS as BLUE
Consequences of assumption violations

Chapter 5 — Inference and Hypothesis Testing

Sampling distributions of OLS estimators
$t$-tests for individual coefficients
Confidence intervals for regression parameters
$F$-tests for joint hypotheses

3.3 Module 3: Model Building (Chapters 6–7)

Chapter 6 — Multiple Regression and Specification

The multiple regression model
Interpretation with controls: ceteris paribus
Omitted variable bias
Adjusted $R^2$, AIC, and BIC

Chapter 7 — Functional Form, Dummies, and Interactions

Log specifications: log-log, semi-log
Polynomial regression
Binary and categorical dummy variables
Interaction effects and their interpretation

3.4 Module 4: Diagnostics (Chapters 8–10)

Chapter 8 — Large Sample Properties

Consistency and the Law of Large Numbers
The Central Limit Theorem
Asymptotic normality of OLS
Why large-sample theory validates regression inference

Chapter 9 — Heteroskedasticity

Definition and sources of heteroskedasticity
Consequences for OLS inference
Detection: graphical and formal tests
Heteroskedasticity-robust standard errors

Chapter 10 — Serial Correlation

Autocorrelation in time series and residuals
Consequences for OLS
Detection: Durbin-Watson and Breusch-Godfrey tests
HAC (Newey-West) standard errors

3.5 Module 5: Time Series (Chapters 11–12)

Chapter 11 — Dynamic Models and Distributed Lags

Static vs. dynamic regression models
Finite distributed lag (FDL) models
Autoregressive distributed lag (ARDL) models
Long-run and short-run effects

Chapter 12 — Non-Stationarity and Unit Roots

Stationarity: definitions and implications
Random walks and spurious regression
The Augmented Dickey-Fuller (ADF) test
Introduction to cointegration

4 Assessment

Assessment	Weight	Topic Coverage
Assignment 0	Formative	R basics and data exploration
Assignment 1 — Cross-Sectional Analysis	30%	Modules 1–3
Assignment 2 — Time Series Analysis	40%	Modules 4–5
Final Examination	30%	All modules

5 Textbooks

The following textbooks are used throughout the course. All are available through the library.

Wooldridge, J.M. (2016). Introductory Econometrics: A Modern Approach (6th ed.). Cengage.
Stock, J.H. & Watson, M.W. (2019). Introduction to Econometrics (4th ed.). Pearson.
Hill, R.C., Griffiths, W.E. & Lim, G.C. (2011). Principles of Econometrics (4th ed.). Wiley.

6 Software

All analysis is conducted in R (version ≥ 4.3) with RStudio. See the Resources page for setup instructions.

--- title: "Syllabus" subtitle: "QMT 2026 — Introduction to Quantitative Methods" --- ## Course Overview This unit introduces students to quantitative methods used in economic analysis. It covers linear regression, inference, model diagnostics, and an introduction to time series econometrics. All analysis is conducted in R, with an emphasis on reproducible, well-documented work. ## Learning Outcomes By the end of this unit, students will be able to: 1. Explain the role of regression analysis in empirical economics 2. Specify, estimate, and interpret linear regression models 3. Conduct hypothesis tests and construct confidence intervals for economic parameters 4. Diagnose and address violations of classical regression assumptions 5. Apply regression methods to time series data and test for non-stationarity 6. Produce professional, reproducible empirical analysis using R ## Module Structure ### Module 1: Foundations (Chapters 1–2) **Chapter 1 — Introduction to Quantitative Methods** - The scope and purpose of quantitative methods in economics - Causality vs. correlation: why the distinction matters - Types of economic data: cross-section, time series, panel - Introduction to R and the tidyverse **Chapter 2 — Mathematical and Statistical Foundations** - Probability and random variables - Key distributions: Normal, $t$, $F$, $\chi^2$ - Hypothesis testing: logic, errors, and power - Confidence intervals and p-values --- ### Module 2: The Linear Regression Model (Chapters 3–5) **Chapter 3 — Simple Linear Regression** - The population regression function - OLS estimation: derivation and mechanics - Interpretation of slope and intercept - Goodness of fit: $R^2$ and the standard error of regression **Chapter 4 — Properties of OLS: BLUE and Gauss-Markov** - The classical linear model assumptions - Unbiasedness of OLS - The Gauss-Markov theorem: OLS as BLUE - Consequences of assumption violations **Chapter 5 — Inference and Hypothesis Testing** - Sampling distributions of OLS estimators - $t$-tests for individual coefficients - Confidence intervals for regression parameters - $F$-tests for joint hypotheses --- ### Module 3: Model Building (Chapters 6–7) **Chapter 6 — Multiple Regression and Specification** - The multiple regression model - Interpretation with controls: ceteris paribus - Omitted variable bias - Adjusted $R^2$, AIC, and BIC **Chapter 7 — Functional Form, Dummies, and Interactions** - Log specifications: log-log, semi-log - Polynomial regression - Binary and categorical dummy variables - Interaction effects and their interpretation --- ### Module 4: Diagnostics (Chapters 8–10) **Chapter 8 — Large Sample Properties** - Consistency and the Law of Large Numbers - The Central Limit Theorem - Asymptotic normality of OLS - Why large-sample theory validates regression inference **Chapter 9 — Heteroskedasticity** - Definition and sources of heteroskedasticity - Consequences for OLS inference - Detection: graphical and formal tests - Heteroskedasticity-robust standard errors **Chapter 10 — Serial Correlation** - Autocorrelation in time series and residuals - Consequences for OLS - Detection: Durbin-Watson and Breusch-Godfrey tests - HAC (Newey-West) standard errors --- ### Module 5: Time Series (Chapters 11–12) **Chapter 11 — Dynamic Models and Distributed Lags** - Static vs. dynamic regression models - Finite distributed lag (FDL) models - Autoregressive distributed lag (ARDL) models - Long-run and short-run effects **Chapter 12 — Non-Stationarity and Unit Roots** - Stationarity: definitions and implications - Random walks and spurious regression - The Augmented Dickey-Fuller (ADF) test - Introduction to cointegration --- ## Assessment | Assessment | Weight | Topic Coverage | |------------|--------|----------------| | Assignment 0 | Formative | R basics and data exploration | | Assignment 1 — Cross-Sectional Analysis | 30% | Modules 1–3 | | Assignment 2 — Time Series Analysis | 40% | Modules 4–5 | | Final Examination | 30% | All modules | ## Textbooks The following textbooks are used throughout the course. All are available through the library. - Wooldridge, J.M. (2016). *Introductory Econometrics: A Modern Approach* (6th ed.). Cengage. - Stock, J.H. & Watson, M.W. (2019). *Introduction to Econometrics* (4th ed.). Pearson. - Hill, R.C., Griffiths, W.E. & Lim, G.C. (2011). *Principles of Econometrics* (4th ed.). Wiley. ## Software All analysis is conducted in **R** (version ≥ 4.3) with **RStudio**. See the [Resources](resources.qmd) page for setup instructions.