Fundamentals of Python for Financial Econometrics

Chapter 42: Nonlinear Models, Regimes, Market Microstructure, and Ordered Outcomes

What if the same lag has a different effect in calm and stressed regimes, or the observed price change is an ordered category?

Download .ipynb Download .py

Loading notebook...Simulate and interpret threshold autoregressionMarkov-switching regimes and expected durationmarket-microstructure effectsEstimate and interpret ordered probit probabilities

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 Nonlinear Models, Regimes, Market Microstructure, and Ordered Outcomes.
Interpret the output using Simulate and interpret threshold autoregression and Markov-switching regimes and expected duration.

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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