ECON526 - Fall 2024

This is a MA-level course in quantitative economics, data science, and causal inference in economics.

This course will have a combination of coding, theory, and development of mathematical background. All coding is done in Python. Link to Jesse's Lecture Slides and Paul's HTML Slides, source

Course materials

• Get a GitHub ID and apply for the Student Developer Pack to get further free features
• Consider clicking Watch at the top of this repository to see file changes

All materials will be on github, and canvas will be used to submit assignments/communication.

There is no assigned physical textbook, but we will be using lecture notes from:

• Causal Inference for The Brave and True
• QuantEcon Python and Data Science Lectures

Computing Environment

While you can use the UBC JupyterOpen for this course, we strongly suggest installing Python on your local machine. The easiest way to do this is:

• Install Anaconda to install python and its packages for your operating system
• Install git for your operating system
• Optionally: install (a) Github Desktop; (b) VS Code to make it easier to manage downloaded notebooks.
• Then clone the following repositories onto your local machine using a terminal, using either git directly (e.g. in terminal go git clone https://github.com/ubcecon/ECON526.git), GitHub Desktop, or VS Code
  • https://github.com/ubcecon/ECON526
  • https://github.com/QuantEcon/lecture-python-intro.notebooks
  • https://github.com/QuantEcon/lecture-python.notebooks
  • https://github.com/QuantEcon/lecture-datascience.notebooks
  • In some cases you will need to manually install packages (by doing pip install -r requirements.txt within some of those repositories, or manually installing packages as required)

We recommend using VS Code to access repositories since you will likely begin using the VSCode editor as your primary Python (and latex) editor sooner than later.

Syllabus

See Syllabus for more details

Problem Sets and Exams

The course has one midterm, weekly to bi-weekly problem sets, and a final data project due the last day of class.

1. September 8th Midnight: Problem Set 1
2. September 18th Midnight: Problem Set 2
3. September 25th Midnight: Problem Set 3
4. October 6th Midnight: Problem Set 4
5. October 16th: Midterm Logistics Practice and Review Midterm Practice Problems
6. October 21st: IN CLASS MIDTERM
7. December 15th Midnight: Data Project Due

See the /problem_sets folder within this repository for the problem sets as jupyter notebooks. You should modify them directly as Jupyter notebooks, and the TA will explain how to submit them.

Lectures

This year the course will be taught in two parts where the later parts of the course will follow material in Causal Inference for The Brave and True.

This lecture begins assuming you have completed the math/programming bootcamp for our masters students, or had an existing python-based programming course. To refresh your knowledge, see basics in QuantEcon Data Science Lectures or QuantEcon Python Programming for Economics and Finance.

Slides for the lectures can be found here and after his section starts: Paul's HTML Slides, source

Jesse

• September 4: Introduction to Numerical Linear Algebra and its Applications in Data Science

  • Topics: Overview of computational complexity and numerical precision, solving systems of equations, geometric interpretations of linear algebra, matrix decompositions, linear least squares, and eigenvalues and eigenvectors. Preparation for applications.
  • Material:
    • Linear Algebra Foundations, Jupyter, PDF
  • Self-study:
    • Basics of linear algebra, matrices, norms, and linear independence: https://python.quantecon.org/linear_algebra.html
    • Numerical optimization: https://datascience.quantecon.org/scientific/optimization.html
    • Systems of Equations: https://python.quantecon.org/linear_algebra.html#solving-systems-of-equations
    • Eigenvectors and eigenvalues: https://python.quantecon.org/linear_algebra.html#eigenvalues-and-eigenvectors
    • Downloading and manipulating data in Python: https://intro.quantecon.org/long_run_growth.html and https://intro.quantecon.org/business_cycle.html
  • (Optional) Extra Material:
    • Introductory material on linear algebra: https://intro.quantecon.org/linear_equations.html and https://datascience.quantecon.org/scientific/applied_linalg.html
    • Matrix decompositions and other topics: https://python.quantecon.org/linear_algebra.html#further-topics

• September 9: Continuing on Introduction to Numerical Linear Algebra

  • Topics: Overview of computational complexity and numerical precision, solving systems of equations, geometric interpretations of linear algebra, matrix decompositions, linear least squares, and eigenvalues and eigenvectors. Preparation for applications.
  • Material:
    • Linear Algebra Foundations, Jupyter, PDF
  • Self-study:
    • Basics of linear algebra, matrices, norms, and linear independence: https://python.quantecon.org/linear_algebra.html
    • Numerical optimization: https://datascience.quantecon.org/scientific/optimization.html
    • Systems of Equations: https://python.quantecon.org/linear_algebra.html#solving-systems-of-equations
    • Eigenvectors and eigenvalues: https://python.quantecon.org/linear_algebra.html#eigenvalues-and-eigenvectors
    • Downloading and manipulating data in Python: https://intro.quantecon.org/long_run_growth.html and https://intro.quantecon.org/business_cycle.html
  • (Optional) Extra Material:
    • Introductory material on linear algebra: https://intro.quantecon.org/linear_equations.html and https://datascience.quantecon.org/scientific/applied_linalg.html
    • Matrix decompositions and other topics: https://python.quantecon.org/linear_algebra.html#further-topics

• September 11: Applications of Linear Algebra (Eigenvalues and Discounting)

  • Topics: Geometric series and present values, difference equations, steady states, and convergence, unemployment dynamics, present discounted values
  • Material:
    • Finishing off Linear Algebra Foundations
    • Eigenvalues and Stability, Jupyter, PDF
  • Self-study:
    • Geometric Series and Present Values: https://intro.quantecon.org/geom_series.html#example-interest-rates-and-present-values
    • Portfolio example: https://datascience.quantecon.org/scientific/applied_linalg.html#portfolios
    • Unemployment Dynamics example: https://datascience.quantecon.org/scientific/applied_linalg.html#unemployment-dynamics
  • (Optional) Extra Material:
    • Supply and Demand: https://intro.quantecon.org/intro_supply_demand.html
    • More on Competitive Equilibrium: https://intro.quantecon.org/supply_demand_multiple_goods.html

• September 16: Latent Variables and Intro to Unsupervised Learning

  • Topics: Review eigenvalues and dynamics, principle components, and present discounted values
  • Material:
    • Latent Variables and Unsupervised Learning, Jupyter, PDF
  • Self-study:
    • scikit-learn PCA docs
    • seaborn tutorials

• September 18: More on Latent Variables and Clustering

  • Topics: Finish off continuous latent variables, PCA, auto-encoders, clustering, and started dynamics
  • Material:
    • Latent Variables and Unsupervised Learning
  • Self-study:
    • scikit-learn k-means docs

• September 23: Dynamics

  • Topics: Dynamical systems, stability, fixed points, linearization, intro to the Solow-Swan growth model
  • Material:
    • Linear and Nonlinear Dynamics, Jupyter, PDF
  • Self-study:
    • Solow-Swan Growth Model Derivation: https://intro.quantecon.org/solow.html (skip 20.3)
    • Nonlinear Dynamics and Stability: https://intro.quantecon.org/scalar_dynam.html
    • Review taylor series, just to first order
  • (Optional) Extra Material:
    • More on the Solow Model and Python: https://python-programming.quantecon.org/python_oop.html#example-the-solow-growth-model

• September 25: Finished Dynamics and Started Probability, Randomness, and Independence

  • Topics: Axioms of probability, LLN and CLT, and Conditional Independence
  • Material:
    • Probability, Conditioning, and Independence, Jupyter, PDF
  • Self-study:
    • https://datascience.quantecon.org/scientific/randomness.html
    • https://intro.quantecon.org/prob_dist.html
  • (Optional) Extra Material:
    • https://python.quantecon.org/lln_clt.html
    • https://python.quantecon.org/prob_meaning.html
    • https://python.quantecon.org/prob_matrix.html

• September 30 (Statutory holiday)

• October 2nd: Continue Probability, Randomness, and Independence

  • Topics: Axioms of probability, LLN and CLT, and Conditional Independence
  • Material:
    • Probability, Conditioning, and Independence, Jupyter, PDF
  • Self-study:
    • https://datascience.quantecon.org/scientific/randomness.html
    • https://intro.quantecon.org/prob_dist.html
  • (Optional) Extra Material:
    • https://python.quantecon.org/lln_clt.html
    • https://python.quantecon.org/prob_meaning.html
    • https://python.quantecon.org/prob_matrix.html

• October 7: Stochastic Processes and Forecasts

  • Topics: Conditional expectations, Bayes' rule, Law of Iterated Expectations, stochastic processes
  • Material:
    • Finish Probability, Conditioning, and Independence
    • Stochastic Processes, Jupyter, PDF
  • (Optional) Extra Material:
    • https://python.quantecon.org/finite_markov.html for more on Markov Chains
    • https://python.quantecon.org/ar1_processes.html for more on AR(1) processes
    • https://datascience.quantecon.org/scientific/randomness.html#loan-states for a simple Markov Chain example

• October 9: Markov Chains and Maybe Start Causality

  • Topics: Finish stochastic processes and Markov Chains and briefly setup causality and counterfactuals if time permits
  • Material:
    • Finish Stochastic Processes
    • Introduction to Causality and Randomized Experiments
  • Self-Study:
    • https://matheusfacure.github.io/python-causality-handbook/01-Introduction-To-Causality.html

• October 14 (Statutory holiday)

• October 16 Introduction to Causality and Counterfactuals + Midterm Logistics Review

  • Material:
    • Introduction to Causality and Randomized Experiments
  • Self-study:
    • https://matheusfacure.github.io/python-causality-handbook/01-Introduction-To-Causality.html
    • https://matheusfacure.github.io/python-causality-handbook/02-Randomised-Experiments.html

• October 21 (IN CLASS MIDTERM)

Paul

Go here for a list of topics, reading, and slides.

Here is the source for my slides.

See "Sources and Futher Reading" (2nd last slide) on each set of slides for additional reading.

Important Dates

• November 11 (Midterm Break)
• November 13 (Midterm Break)

• December 15
  • PROJECT DUE