Note: this package is for Python 2 and has not been updated in awhile.

nomics

Containing general utilities/functions often useful for economic analyses. More detailed documentation is found in the comments of the code itself

To install it run

git clone https://github.com/codyfcook/nomics.git && cd nomics && pip install -e . && cd ..

Utilities

There are various simple functions that may be of use. Often these functions just simplify the notation to reduce Googling/typing/typos

Utilities also imports from basil_utils.py, so running from nomics.utilities import * will get you the functions below plus a bunch of Basil's.

Sample of a few of the utility functions:

1. groups_apply_parallel(groups, func, cpu_count=4) Run applies in parallel. Make sure not to overwhelm the CPUs.
2. format_list_for_qr(list, quote=True) formats a list of items so that it can be used in a query (e.g. as 'name in ({list})')
3. dfs_in_mem() shows you the dataframes currently being held in memory
4. mem_usage(obj) prints how much memory the object is using

Statistics

Absorber

The absorber allows you to run absorbed FE regressions (the new linearmodels package is likely now a better choice than this, although performance is similar).

To run it, do:

from nomics.stats.absorber import OLSAbsorb
y = d['y_col']
dense = d['dense_cols']
absorb = np.array(d['absorb_cols'])
model = OLSAbsorb(y, dense, absorb)
results = model.fit()
print results.summary()

The absorbed cols should be integers from 1 to N denoting the group for which the effect size (coefficient) will be absorbed. You can turn any old column in to a series such integeres by running:

d[new_col] = d[old_col].astype('category').cat.codes.astype(int)

ZIP

Code to run a Zero Inflated Poisson.

from nomics.stats.zip import ZIPoisson
y = d['y_cols']
x = d['x_cols']
# The columns you want included in the logit part of a ZIP to predict 0/1 in the y col
inflateX = ['inflate_cols']

mod = ZIPoisson(y,x,inflateX).fit()
print mod.summary()

Also has code for a Vuong test.

Experiments

Has two functions to check whether treatment groups are balanced.

1. check_for_balance_with_regs(cat_vars, cont_vars, group_var, data) For continuous variables, get's the p-value from an F-test of var on treatment to see if treatment can predict variable. For categorical vars uses a chi squared test to see if treatment more likely to appear in some categories than others
2. pairwise_t_test(data, cont_vars, group_var): For each variable, compare pairwise across groups and do a t-test to see if different

Matching

Code to run (1) propensity score matching (2) distance matching (KNN; greedy matching; optimal matching) and (3) basic analytics on matched pairs (calculate ATT; some measures of balance)