… mindmap

It would be great to show a modern roadmap in econometrics starting from mathematic foundations (linear algebra, calculus, probability) to econometrics to computationally intensive data processing tasks. I’ve seen this being approached as clusters of courses, Khan Academy has goals by subject, but I think there is more that can be done.

An (over)simplified view of econometrics curriculum

  • linear algebra, calculus, probability and statistical inference

  • OLS (assumptions, violations, fixes + estimatore quality)

  • limited depenedent variables + maximum likelihood

  • intrumental variables

  • time series, state space representation

  • panel data

  • classifications

  • systems of equations

Key areas

  • data structures (crosssection, time series, panel)

  • inference methods

    • model specification

    • estimation procedure

    • model evaluation

  • use cases

Additional topics

  • simulation (Monte Carlo, bootstrap)

  • transformations (PCA)

OLS Extensions:

  • GMM

  • 2,3 stage OLS

  • quantile regressions

  • lasso, rigde


  • maximum likelihood

  • bayesian estimation

  • mcmc (see reddit post)

Time series:

  • time series, stationarity, unit root

  • state space representation, Kalman filter

  • fractional integration

  • seasonal adjustment

  • (vector) error correction model, VECM

  • structural breaks

User profiles

  1. “Numerate biologists” - solve a domain problem in biology, psychology, social sciences

  2. “Want to hit a ‘Run’ button” - quick results without thinking, typical of students

  3. “I’m doing XYZ now!” - excited adopters, writing a piece on Medium full of acronyms

  4. “Sane econometrics” - appropriate methods with clear, accessible explaination, rare trait

  5. “Asymptotics” - publish evermore sophisticated articles to secure academic career