Fundamentals of Python for Financial Econometrics

Chapter 26: Censoring, Selection, Quantiles, and Robust Methods

What if the mean is not the whole story, or part of the outcome is unobserved?

Loading notebook...censoring and truncationsample selectionmissing-data mechanismssurvey weights

Notebook lab guide

Related dataset

Ceteris Lab teaching sample

Estimated time

25 to 40 min

Packages

pandas, numpy

Expected output

Printed Python results that can be compared with the lesson explanation.

Learning goals

Load and inspect Ceteris Lab teaching sample.
Run the Python cells connected to Censoring, Selection, Quantiles, and Robust Methods.
Interpret the output using censoring and truncation and sample selection.

Common errors

File not found: check that wage_sample.csv is installed or use the course data folder.
Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
Column name error: compare your variable names with the dataset variables listed for this notebook.

Dataset path helper

import pandas as pd

df = pd.read_csv("/data/wage_sample.csv")
df.head()

Loading notebook runner...