#  Outline 

 



After the foundational material is presented (roughly the first third of the class), I will introduce a large variety of statistical models and methods. I will choose these based on what makes sense from a pedagogical perspective at first, but as the semester goes on I will choose more and more material based on students interest and class projects.

For more information on the content of the class, see the detailed lecture notes online, which gives a general outline. Here's another version of some of the material:

### Foundations

- What is statistics?
- What is political methodology?
- Models and a language of inference
- The role of simulation
    - To solve probability problems
    - to evaluate estimators
    - to compute features of probability distributions
    - to transform statistical results into quantities of interest
- Stochastic components (normal, log-normal, Bernoulli, Poisson, etc)
- The relationship between stochastic and systematic components and data generation processes
- Systematic components (linear, logit, etc.)
- Uncertainty and Inference
    - Probability as a model of uncertainty
    - Probability distributions, theory, discrete, continuous, examples
- Inference
    - Inverse probability problems
    - The likelihood theory of inference
    - The Bayesian theory of inference
    - Detailed example: Forecasting presidential elections
- Properties of maximum likelihood estimation (finite sample, asymptotic, etc.)
- Precision of likelihood estimates

### Specific Topics

We will not get to all these topics, and the list of topics we do cover will likely include others than those listed here, depending on student interest.

- Discrete regression models
    - Binary variables
    - Interpreting functional forms
    - Ordinal variables
    - Grouped uncorrelated binary variables
    - Event count models --- Correlated and uncorrelated events; over and under dispersion.
- Basic time series models
- Basic multiple equation models, including identification
- Multinomial choice models
- Models for selection bias, censoring, and truncation
- Models for duration
- Hurdle models
- Case-control designs
- Model dependence
- Matching as nonparametric preprocessing
- Rare events
- Neural network models
- An overview of MCMC methods
- Compositional data
- Missing data (item and unit nonresponse) problems
- Ecological inference (avoiding aggregation bias)
- Models for reciprocal causation and endogenity
- Empirical and hierarchical Bayesian analysis
- Time series cross-sectional data
- Models for interpersonal incomparability in surveys
- Text Analysis



 

###  Gov2001 Dataverse 

 



  

 



 

 

 

###  Class Outings 

[Gov 2001 Party, 2012](/galleries/gov-2001-party-2012 "Gov 2001 Party, 2012")

   ![img_6776](/sites/g/files/omnuum7421/files/styles/hwp_1_1__360x360_scale/public/2025-02/img_6776.jpg?itok=Vkf3-ZGu) 

 

[Gov 2001 Class Party and Mountain Biking 2011](/galleries/gov-2001-class-party-2011 "Gov 2001 Class Party and Mountain Biking 2011")

   ![1](/sites/g/files/omnuum7421/files/styles/hwp_1_1__360x360_scale/public/2025-02/1.jpg?itok=8_unjKzY)