Quantitative Methods for Political Science 3
This repository contains a selection of recitation materials prepared by Pablo Barberá for the PhD-level course "Quantitative Methods for Political Science 3", taught by Prof. Nathaniel Beck in the Fall of 2013 at New York University.
Maximum likelihood
- (Re-)introduction to R
- Recovering the parameters of a Poisson and a Beta distribution using maximum likelihood
- Recovering the parameters of a exponential distribution using maximum likelihood
- Probit regression and quantities of interest
- Probit/Logit and marginal effects
- Ordinal probit and marginal effects
- Identification in logit regression
- OLS vs Poisson regression
Duration models
Time-series analysis
- Introduction to time-series with R
- Impulse/unit response functions for ADL model
- Time-series and stationarity
- Cointegration and error-correction models
Bayesian statistics
- Accept-reject sampling of a beta distribution
- Bayesian samplers (grid sampling, Metropolis-Hastings, Gibbs, Hamiltonian Monte Carlo)
- Bayesian Poisson Regression
- Bayesian Probit Regression
- Bayesian Hierarchical Regression
- Introduction to Multilevel Regression with Poststratification
- Introduction to Item-Response Theory models
- Introduction to High-Performance Computing
- More advanced examples of IRT models