Johan A. Elkink
teaching

POL 50050 PhD Quantitative Methods II

Tuesday, 11 am-1 pm, on Zoom.

Syllabus

While the statistical package R is the software used on slides, students can use any package of choice. A booklet with commands in R, Stata, and SPSS will be made available. R can be downloaded for free here. A handy overview of R regression commands can be found here: reference card for regression. A more general one for R is here: short reference card. Finally, there is an extensive overview of R packages relevant for econometrics.

A booklet is in production with all relevant commands for the course in a number of different statistical packages, with an early draft here: Statistical Software Guide for Introductory Econometrics. Since we are actively working on this booklet, any feedback would be greatly appreciated.

For R users, please make sure to install the following packages prior to the second lecture of the course:
faraway, arm, lmtest, tseries, ape, MASS, plm, and pcse.

Data can be found on the teaching data page, but for homeworks also original replication data sets might be used.

 videos: recoding & mergingresources: chapter on data management 1 19/1 Mathematics review & statistical estimators slides lab lecture: matrices | derivatives | asymptoticsnotes: math review | statistical estimatorsvideos: sampling distribution 2 26/1 Ordinary Least Squares slides lab lecture: linear model | deriving OLS | common distributions | p-values | t- and F-testsnotes: regression tablesvideos: lab talk-through (available 1 Feb 2021) 3 2/2 Regression diagnostics slides lab homework lecture: outliers | multicollinearity | measurement error | heteroskedasticity | heteroskedasticity: solutions | heteroskedasticity: diagnostics 4 9/2 Autocorrelation and time-series analysis slides lab lecture: autocorrelation | autocorrelation diagnostics | time-series processes | non-stationarity | dynamic models | cointegration 5 16/2 Multilevel data slides lab lecture: multilevel data | fixed effects | random effects 6 23/2 Panel data slides lab homework lecture: panel data and rewatch previous two weeks 7 2/3 Model specification & matching slides lab lecture: confounding | controls | model selection | matching notes: causal inference 8 23/3 Maximum Likelihood slides lab lecture: maximum likelihood | linear model | numerical optimization | typical tests 9 30/3 Limited dependent variables I slides lab homework lecture: introduction | binomial models | multinomial models | model fit 10 6/4 Limited dependent variables II (continued) lecture: count models | survival models 11 13/4 Bootstrap & simulation slides lab homework lectures: simulated parameters | bootstrapping | monte carlonotes: bootstrap & simulation 12 20/4 Spatial & network data slides (course paper) lectures: interdependence | spatial autocorrelation | spatial autoregression | exponential random graph models | stochastic actor-oriented models