Part 1 | Chapters 1-8
Flagship course
Fundamentals of Python for Financial Econometrics
Economics, Data Analysis, Econometrics, Causal Inference, Forecasting, Machine Learning, and Artificial Intelligence with Python
Mohammad Safavi, Ph.D. | Ceteris Lab
A complete Python-first pathway from programming foundations to econometrics, causal inference, forecasting, financial risk, machine learning, and responsible AI.
53 chapters70 to 100 hours53 Python notebooks94 figures and 129 tables
Need help setting up Python?
Compare six beginner-friendly options, including browser Python with no installation. Completing a local installation is not required to use supported Ceteris Lab examples.
Beginner to advanced
Start with Python syntax and reproducibility, then progress to econometrics, causal inference, forecasting, risk, machine learning, and responsible AI.
Practice in every chapter
Run editable browser Python, inspect verified source output, download notebooks and scripts, and complete guided practice before each knowledge check.
Portfolio roadmap
Preserve reproducible evidence as you move from small exercises to economic prediction, causal analysis, forecasting, risk, and the final capstone portfolio.
Four learning paths
Complete 53-chapter sequence
8 chapters
Part 2 | Chapters 9-16
Data Analysis, Statistics, and Econometrics Foundations
- 9pandas Fundamentals
- 10Importing, Cleaning, Validating, and Exporting Data
- 11Manipulating, Grouping, Reshaping, and Joining Data
- 12Data Visualization with Python
- 13Descriptive Statistics and Exploratory Data Analysis
- 14Probability, Simulation, and Statistical Inference
- 15Regression and Econometrics with Python
- 16Obtaining Economic and Financial Data
Part 3 | Chapters 17-45
Econometrics, Causal Inference, Time Series, and Financial Econometrics
- 17Econometric Thinking and Research Design
- 18Simple Linear Regression
- 19Multiple Regression and the Ceteris Paribus Interpretation
- 20Functional Forms, Dummy Variables, and Interactions
- 21Inference, Robust Standard Errors, and Diagnostics
- 22Omitted Variables, Measurement Error, Simultaneity, and Endogeneity
- 23Instrumental Variables and Two-Stage Least Squares
- 24Panel Data and Fixed Effects
- 25Binary, Ordered, and Count Outcomes
- 26Censoring, Selection, Quantiles, and Robust Methods
- 27Potential Outcomes and Randomized Experiments
- 28Matching, Propensity Scores, Weighting, and Doubly Robust Estimation
- 29Difference-in-Differences and Event Studies
- 30Regression Discontinuity
- 31Synthetic Control and Comparative Case Studies
- 32Regularization and High-Dimensional Economic Models
- 33Decision Trees, Random Forests, and Boosting for Economic Prediction
- 34Causal Machine Learning and Double Machine Learning
- 35Explainability, Uncertainty, and Model Governance
- 36Introduction to Time-Series Data
- 37Dependence, Stationarity, and White Noise
- 38AR, MA, ARMA, ARIMA, and Forecasting
- 39Unit Roots, Seasonality, and Dynamic Regression
- 40ARCH and GARCH Models
- 41Asymmetric, Advanced, and Realized Volatility
- 42Nonlinear Models, Regimes, Market Microstructure, and Ordered Outcomes
- 43Stochastic Processes and Option Pricing
- 44Financial Risk Management
- 45Multiple Time Series
Part 4 | Chapters 46-53
Machine Learning and Artificial Intelligence
- 46Econometrics and Machine Learning: Different Questions, Shared Tools
- 47Machine-Learning Workflow and Gradient Descent for Economic Data
- 48Applied Economic Prediction and Classification Projects
- 49Neural Networks and Deep Learning for Economic Data
- 50Computer Vision and Spatial Economic Measurement
- 51Natural-Language Processing and Transformers for Economics
- 52Language Models, Retrieval, and AI Agents for Econometric Research
- 53Capstone Projects and Student Portfolio
Publication resources
Book and student package
Download the supplied Ceteris Lab web publication or the student package containing the book, requirements, original figures, and executable notebooks.
Course materials are original Ceteris Lab work, properly licensed or publicly sourced where metadata confirms it, or clearly labelled as simulations. Unverified external dataset references remain private and draft.