PhD Candidate, Department of Government, Harvard University

To be Assistant Professor of Political Science, Yale University (2022-)

My research focuses on democratic representation in American Politics. In an ongoing project, I study the structure of voter's party choices across levels of national, state, and local government, using cast vote records and surveys. My other projects include public opinion and Congress, election administration, and the intersection of survey statistics and causal inference. Prior to and during graduate school, I worked at the Analyst Institute in Washington D.C.

kuriwaki@g.harvard.edu

1737 Cambridge Street

Room 428, CGIS Knafel Building

Cambridge, MA 02138

The following screencasts were designed as *short* guided introductions for particular statistical concepts. They are probably best used as links in problem sets that students can refer to at their own pace, before they set out to tackle harder, open-ended questions.

All code uses R conforms to the tidyverse style and often uses tidyverse syntax, and uses real dataset that can be loaded on any R environment quickly (e.g. through a package built-in dataset). It is geared towards advanced undergraduates or a masters class where students already have some familiarity with probability and inference.

*Contents*: Package Setup, lm, LASSO, Fixed Effects, Instrumental Variables, Regression Discontinuity, Diff-in-Diff, Creating Functions, Maps in ggplot

Installing vs. loading scripts, basic structure and sections of a script, function masking.

Running linear regression, formulas, options to the `lm`

function:

- Basic syntax of
`lm`

- Summary of lm objects (extracting stats from summary)
- Different types of formula specifications (logs, squared terms, factors)
- Coefficient plots using
`ggplot2`

(putting 1-3 together)

Using `cv.glmnet`

to

- Setup and pre-processing: testing and training data, formula with
`"."`

, model matrix creation. - Fitting and understanding cv.glmnet: penalty terms, cross validation, LASSO algorithm, picking a penalty.
- Making predictions with
`predict`

with the testing dataset, generic functions.

Fixed effects syntax with `lfe::felm`

, adjusting for clustered errors.

Instrumental variables as an omitted variable problem, using both `AER::ivreg`

and `lfe::felm`

packages. Uses the proximity to college dataset by Card (1994).

Visualizing regression discontinuity, estimating coefficients with interactions, polynomials, and local linear regression.

- Visualizing RD
- Estimating RD coefficients

Time series data, long form, plotting time trends, interactions, 2 by 2 difference-in-differences, DID with fixed effects.

- Creating and understanding variables for DID
- Implementing DID in a two-way FE regression

Thanks to Oscar Torres-Reyna for the data (http://princeton.edu/~otorres/DID101R.pdf).

Arguments, body, and return statement. Also see the function basics tutorial for background.

Choropleth maps using `sf`

objects in `ggplot2`

, merging in other variables into sf dataframes.