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Masters-level applied econometrics course—focusing on prediction—at the University of Oregon (EC424/524 during Winter quarter, 2020 Taught by Ed Rubin
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README.md

EC 524, Winter 2020

Welcome to Economics 524 (424): Prediction and machine-learning in econometrics, taught by Ed Rubin.

Schedule

Lecture Tuesday and Thursday, 10:00am–11:50am, 105 Peterson Hall

Lab Friday, 12:00pm–12:50pm, 102 Peterson Hall

Office hours

  • Ed Rubin (PLC 519): Thursday (2pm–3pm); Friday (1pm–2pm)
  • Connor Lennon (PLC 430): Monday (1pm-2pm)

Syllabus

Syllabus

Books

Required books

Suggested books

Lecture notes

000 - Overview (Why predict?)

  1. Why do we have a class on prediction?
  2. How is prediction (and how are its tools) different from causal inference?
  3. Motivating examples

Formats .html | .pdf | .Rmd

001 - Statistical learning foundations

  1. Why do we have a class on prediction?
  2. How is prediction (and how are its tools) different from causal inference?
  3. Motivating examples

Formats .html | .pdf | .Rmd

002 - Model accuracy

  1. Model accuracy
  2. Loss for regression and classification
  3. The variance bias-tradeoff
  4. The Bayes classifier
  5. KNN

Formats .html | .pdf | .Rmd

Projects

Intro Predicting sales price in housing data (Kaggle)

001 KNN and loss (Kaggle notebook)
You will need to sign into you Kaggle account and then hit "Copy and Edit" to add the notebook to your account.
Due 21 January 2020 before midnight.

Lab notes

000 - Workflow and cleaning

  1. General "best practices" for coding
  2. Working with RStudio
  3. The pipe (%>%)

Formats .html | .pdf | .Rmd

dplyr and Kaggle notebooks

  1. Finish previous lab on dplyr
  2. Working in (Kaggle) notebooks

Problem sets

Additional resources

R

Data Science

Spatial data

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