Fundamentals of Python for Financial Econometrics

Chapter 32: Regularization and High-Dimensional Economic Models

How do we build stable predictions when the number of candidate predictors becomes large?

Loading notebook...ridge regressionlassoelastic netstandardization

Notebook lab guide

Related dataset

Ceteris Lab teaching sample

Estimated time

25 to 40 min

Packages

pandas, numpy, statsmodels, patsy

Expected output

A regression or inference table with coefficients, uncertainty, and short interpretation notes.

Learning goals

Load and inspect Ceteris Lab teaching sample.
Run the Python cells connected to Regularization and High-Dimensional Economic Models.
Interpret the output using ridge regression and lasso.

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()

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