teacher value-added
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README.md
basic_va_alg.py
factor_model_mc_data.py
likelihood_check.py
mcmc_likelihood_check.py
mle.py
moment_matching_estimator.py
simulate_baseline.py
test_va_alg.py
tests.py
va_functions.py
vam.py

README.md

tva

basic_va_alg.py implements three different value-added estimators; see the 'method' parameter for more information. Parameters are

data: A Pandas DataFrame. It must contain the columns named in the parameters outcome, teacher, covariates, class_level_vars, and categorical_controls.

covariates: A list of strings containing the names of columns that contain covariate data. These should be scalars; to generate fixed effects from a vector of categorical_data, use the categorical_controls argument. Example: ['previous test score', 'age']

class_level_vars: A list of column names that, incombination, uniquely identify a classroom. For example, if a teacher, year, and time period uniquely identify a classroom (Mrs. Smith's 9 am class in 2015-2016), class_level_vars might be ['teacher', 'year', 'time'].

categorical_controls (optional): List of columns that contain categorical data, which will be expanded into fixed effects. For exmaple, categorical_controls = ['ethnicity', 'home language'].

jackknife (optional, default = False): Whether to use a jackknife estimator for each teacher's value-added.

moments_only (optional, default = True): If moments_only=True, this returns only structural parameters and does not estimate VA for individuals.

method:

if method is 'ks' (default), implements the estimator in Kane and Staiger (2008).

if method is 'cfr', implements the estimator in Kane and Staiger (2008) but with a tweak suggested by Chetty, Friedman, and Rockoff (2014): Coefficients on covariates are estimated in the presence of teacher fixed effects.

if method is 'fk', implements an estimator inspired by Fessler and Kasy (2016).