08. Models

[briatte]

TODO_FIRST: determine whether this section should be

1. about modelling per se (in which case, show many models),
2. or about general model classes (linear, nonlinear, hierarchical/multilevel, temporal/spatial effects and SE clustering, bootstrapped, Bayesian), plus tips and tricks (e.g. ggfortify, Zelig)

I'm slowly drifting towards Option 2, covering only the basic modelling stuff, and citing examples of text models (topic models), network models (ERGM, SOAM), etc.

• 8.0. Linear models
  • Current example: Markus Gesmann's prediction of London Olympics 100m men's sprint results
• 8.1. Linear correlation
  • Visualizing linear relationships
  • Measuring linear correlations
  • Correlation matrixes
  • Scatterplot matrixes
• 8.2. Linear equations (changed title; also, not yet sub-sectioned)
  • Ordinary Least Squares (Legendre published the method of least squares in 1805.)
  • Results:
    • residuals
    • fitted values
  • Generalization, e.g.
    • to add dummies (show that)
    • or lagged values (leave it to Section on 'Time Series')
  • Presenting results:
    • Tables: texreg
    • Marginal FX plots (margins)
• 8.3. Advanced Modelling (leave anything to do with 'Times Series' or 'Networks')
  • Nonlinear equations
  • Corrected standard errors
    • Robust SEs (jackknife, sandwich), FE, RE
    • Bootstrapped SEs
  • Quick word on a few model 'classes'
    • Spatial / Gravity
    • Econometrics: 2SLS, DiD, Oaxaca decomposition
    • Lasso, regularization
    • Machine Learning, random forests, neural networks…
  • Bayesian models with Stan
    • Gelman's blog, e.g.
    • A few links from Pinboard bookmarks, e.g. http://metrumrg.com/opencourses.html

Note: Section 8.3. really should be a collection of examples.

References:

• Hastie, Tibshirani, Friedman, The Elements of Statistical Learning
• Shalizi, ADAEPoV