https://github.com/pablobarbera/quant3materials

PhD course: Quantitative Methods for Political Science III (NYU) -- Recitation Materials
https://github.com/pablobarbera/quant3materials

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PhD course: Quantitative Methods for Political Science III (NYU) -- Recitation Materials

• Host: GitHub
• URL: https://github.com/pablobarbera/quant3materials
• Owner: pablobarbera
• License: gpl-2.0
• Created: 2013-11-22T18:12:48.000Z (about 11 years ago)
• Default Branch: master
• Last Pushed: 2014-12-19T17:54:08.000Z (about 10 years ago)
• Last Synced: 2024-11-11T01:35:12.530Z (about 2 months ago)
• Language: R
• Size: 8.15 MB
• Stars: 53
• Watchers: 7
• Forks: 28
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
## Quantitative Methods for Political Science 3 ##

This repository contains a selection of recitation materials prepared by [Pablo Barberá](http://www.pablobarbera.com) for the PhD-level course "Quantitative Methods for Political Science 3", taught by [Prof. Nathaniel Beck](http://politics.as.nyu.edu/object/nathanielbeck) in the Fall of 2013 at New York University.

## Maximum likelihood ##

- [(Re-)introduction to R](maxlk/lab1_r_intro.R)

- [Recovering the parameters of a Poisson and a Beta distribution using maximum likelihood](maxlk/lab1_maxlk.R)

- [Recovering the parameters of a exponential distribution using maximum likelihood](maxlk/lab3_exponential_maxlk.R)

- [Probit regression and quantities of interest](maxlk/lab3_probit_qois.R)

- [Probit/Logit and marginal effects](maxlk/lab4_probit_logit_marginal.R)

- [Ordinal probit and marginal effects](maxlk/lab4_oprobit_marginals.R)

- [Identification in logit regression](maxlk/lab5_identification_logit.R)

- [OLS vs Poisson regression](maxlk/lab6_poisson.R)

## Duration models ##

- [Duration models with R](duration/lab6_duration_models.R)

## Time-series analysis ##

- [Introduction to time-series with R](timeseries/lab7_time_series.R)

- [Impulse/unit response functions for ADL model](timeseries/lab8_response_functions_ADL.R)

- [Time-series and stationarity](timeseries/lab8_stationary_time_series.R)

- [Cointegration and error-correction models](timeseries/lab9_cointegration_ecm.R)

## Bayesian statistics ##

- [Accept-reject sampling of a beta distribution](bayesian/lab12_accept_reject_sampler.R)

- [Bayesian samplers (grid sampling, Metropolis-Hastings, Gibbs, Hamiltonian Monte Carlo)](bayesian/lab12_samplers.R)

- [Bayesian Poisson Regression](bayesian/lab12_bayesian_poisson.R)

- [Bayesian Probit Regression](bayesian/lab13_bayesian_probit.R)

- [Bayesian Hierarchical Regression](bayesian/lab13_hierarchical_model.R)

- [Introduction to Multilevel Regression with Poststratification](bayesian/lab13_mrp_intro.R)

- [Introduction to Item-Response Theory models](bayesian/lab13_IRT_intro.R)

- [Introduction to High-Performance Computing](bayesian/lab14_HPC_cheatsheet.pdf)

- [More advanced examples of IRT models](bayesian/lab14_IRT_issues.R)