ECON526: Graduate Quantitative Economics and Datascience
Getting Started
- Python setup: install Python, Jupyter, and VS Code
- Canvas submissions: problem sets are submitted as
ipynband exportedhtmlfiles - Notebooks repository: every deck and problem set as a runnable notebook
Slides
| Lecture | Formats |
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Foundations of Numerical Linear Algebra Vectors, matrices, and linear systems in NumPy and SciPy, with symmetric, positive definite, and orthogonal matrices, eigendecompositions, and conditioning. |
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Least Squares, Uniqueness, and Regularization Definiteness and quadratic forms, least squares through the normal equations, uniqueness of solutions, and regularization. |
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Applications of Linear Algebra and Eigenvalues Eigenvalues and eigenvectors applied to linear difference equations, unemployment dynamics, and present discounted values. |
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Classification Logistic and multinomial regression as classifiers, decision rules and their evaluation, coding occupations from job titles, and the measurement error a classifier introduces into a regression. |
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Latent Variables and Introduction to Unsupervised Learning Latent variables and unsupervised learning, with principal components, auto-encoders, and clustering with discrete latent variables. |
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Linear and Nonlinear Dynamics Fixed points and stability of linear and nonlinear dynamics, with the Solow-Swan growth model and PageRank as applications. |
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Probability and Uncertainty Probability spaces, discrete distributions, the law of large numbers and central limit theorem, and joint and conditional distributions. |
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Stochastic Processes, Markov Chains, and Expectations Stochastic processes, Markov chains, stationary distributions, and conditional expectations. |
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Natural Language Processing and Large Language Models Tokens, embeddings, and sequential token prediction as an introduction to natural language processing and large language models. |
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Text Classification Text as features, sentiment and topic classifiers, a century of wire-service news, coverage regressions with classifier output as the outcome, and measurement error in a generated outcome. |
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Problem Sets
| Problem Set | Formats |
|---|---|
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ECON526: Problem Set 0 Minimize the function \(x^2 + y^2 +z^2\) subject to |
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ECON526: Problem Set 1 Install Python (with Jupyter and VS Code) |
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ECON526: Problem Set 2 Use the following packages and imports |
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ECON526: Problem Set 3 Feel free to use the following packages (and we have added a few convenience imports) |
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ECON526: Problem Set 4 Feel free to use the following packages (and we have added a few convenience imports) |
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