Fundamentals of Python for Financial Econometrics

Chapter 46: Econometrics and Machine Learning: Different Questions, Shared Tools

Which tasks deserve the label AI, and which claims collapse when we ask what data, objective, and evaluation produced the result?

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Loading notebook...AI, machine learning, deep learning, and generative AISeparate rule-based systems from learned modelsregression, classification, generation, and retrievalIdentify hallucination, bias, privacy, and deepfake risks

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 Econometrics and Machine Learning: Different Questions, Shared Tools.
Interpret the output using AI, machine learning, deep learning, and generative AI and Separate rule-based systems from learned models.

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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