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TODO_FIRST: determine whether this section should be
about modelling per se (in which case, show many models),
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…
TODO_FIRST: determine whether this section should be
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.
OrdinaryLeast Squares (Legendre published the method of least squares in 1805.)texreg
margins
)Note: Section 8.3. really should be a collection of examples.
References:
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