Official Data
Official Data Observatory Data structures, trends, and evidence
Load official economic indicators from Ceteris Lab's server export endpoint, plot a time series, calculate growth rates, and discuss limitations.
official data time series plot growth rates limitations
Module 0
Loading CSV Data in Python Loading CSV data in Python
Load wage_sample.csv, inspect rows, and identify variables in a DataFrame.
pandas CSV loading DataFrame preview
Module 0
Descriptive Statistics in Python Descriptive statistics in Python
Calculate means, medians, standard deviations, and grouped wage summaries.
summary statistics groupby interpretation
Module 0
Creating Simple Graphs in Python Creating simple graphs in Python
Create a scatter plot that compares education and wages.
matplotlib scatter plots visual interpretation
Module 0
Mini Python Practice Lab Mini Python practice lab
Run a full beginner workflow: load, inspect, summarize, graph, and interpret.
workflow summaries graphs
Module 1
First Python Data Example First Python data example
Use Python to describe the wage and education relationship without overclaiming causality.
correlation grouped means careful interpretation
Module 2
Simple Regression in Python Regression in Python
Estimate wage on education, draw the fitted line, and read the coefficient output with a statsmodels-compatible fallback.
OLS statsmodels-ready fitted line
Module 2
Fitted Values and Residuals Fitted values and residuals
Calculate fitted wages, residuals, squared residuals, and residual plots from the wage regression.
fitted values residuals diagnostic plots
Module 2
OLS and R-squared by Formula Ordinary Least Squares intuition
Compute the OLS slope, intercept, residual sum of squares, and R-squared step by step.
OLS formulas SSR R-squared
Module 2
Module 2 Regression Project Template Practice regression project
A reusable project notebook students can edit to choose y and x, estimate the model, and write an interpretation.
project workflow student template interpretation
Module 3
WAGE1 Multiple Regression Multiple Regression in Python
Estimate log wage on education, experience, and tenure when the WAGE1 file is installed.
multiple regression WAGE1 statsmodels
Module 3
GPA1 Multiple Regression Holding Other Factors Fixed
Estimate college GPA on high-school GPA and ACT when the GPA1 file is installed.
controls GPA1 coefficient interpretation
Module 3
Simple vs Multiple Regression Simple Regression versus Multiple Regression
Compare the education coefficient before and after adding experience and tenure controls.
simple regression controls coefficient comparison
Module 3
Partialling-Out Demonstration The Partialling-Out Interpretation
Recover a multiple-regression coefficient by using the leftover part of education after controls.
partialling out residuals ceteris paribus
Module 3
Omitted-Variable Bias Example Omitted-Variable Bias
Compare a short wage model with a controlled model and discuss the direction of omitted-variable bias.
omitted-variable bias controls interpretation
Module 3
VIF Calculation Multicollinearity and VIF
Calculate variance inflation factors for education, experience, and tenure.
VIF multicollinearity precision
Module 3
Residuals and Fitted Values Fitted Values and Residuals
Create fitted values and residuals from a multiple-regression wage model.
fitted values residuals diagnostics
Module 3
R-squared Comparison Goodness of Fit in Multiple Regression
Compare R-squared across nested models and separate fit from causal interpretation.
R-squared model comparison fit
Module 4
T Tests with WAGE1 Testing a Single Coefficient Against Zero
Estimate a wage equation and test education, experience, and tenure with real t statistics.
t tests WAGE1 statsmodels
Module 4
One-Sided and Two-Sided Tests One-Sided and Two-Sided Tests
Compare tail choices and hypothesis wording using GPA1.
one-sided tests two-sided tests p-values
Module 4
p-Values and Critical Values p-Values for t Tests
Use scipy to compute p-values and critical values from the t distribution.
p-values critical values scipy
Module 4
Confidence Intervals Confidence Intervals for Regression Coefficients
Build confidence intervals from estimates, standard errors, and t critical values.
confidence intervals WAGE1 inference
Module 4
Linear Combinations Testing One Linear Combination of Parameters
Test whether two coefficients are equal using WAGE2.
linear combinations coefficient equality t_test
Module 4
F Tests for Exclusion Restrictions F Tests for Exclusion Restrictions
Compare restricted and unrestricted models and compute an F statistic.
F tests restricted model unrestricted model
Module 4
R-Squared F Test R-Squared Form of the F Statistic
Use R-squared values to compute the F statistic for valid exclusion restrictions.
R-squared F statistic nested models
Module 4
General Linear Restrictions General Linear Restrictions
Use statsmodels f_test for restrictions beyond simple variable exclusion.
general restrictions f_test housing data
Module 4
Reporting Regression Results Reporting Regression Results Professionally
Assemble coefficient, uncertainty, p-value, fit, and interpretation into a report table.
reporting regression table professional interpretation
Module 4
Applied Inference Project Applied Inference Project
Run a complete inference workflow and draft a concise empirical report.
applied project inference workflow reporting
Module 5
Consistency Simulation What Consistency Means
Simulate OLS under exogeneity and watch estimates concentrate as n grows.
consistency simulation large samples
Module 5
OLS Inconsistency from Endogeneity Inconsistency and Asymptotic Bias
Show estimates converging to the wrong target when x and u are correlated.
inconsistency endogeneity simulation
Module 5
Omitted-Variable Inconsistency Omitted Variable Inconsistency
Compare true and omitted models as sample size grows.
omitted variables probability limits simulation
Module 5
Asymptotic Normality Large-Sample Inference without Normal Errors
Show coefficient distributions becoming approximately normal under several error distributions.
asymptotic normality CLT simulation
Module 5
Nonnormal Errors and Large-Sample Inference Large-Sample t and F Tests
Use 401K to discuss bounded, nonnormal outcomes and approximate inference.
nonnormality large-sample tests 401K
Module 5
Standard Errors Shrink with n Asymptotic Standard Errors
Estimate growing GPA2 subsamples and compare standard errors.
standard errors sample size GPA2
Module 5
Histograms, Skewness, and Transformations Histograms, Normality, and Transformations
Compare WAGE1 residual histograms for wage and log(wage).
histograms skewness log transformations
Module 5
LM Test with CRIME1 The Lagrange Multiplier Test
Compute the n-R-squared LM statistic and compare it with exclusion-test logic.
LM test auxiliary regression CRIME1
Module 5
Asymptotic Efficiency Simulation Asymptotic Efficiency of OLS
Compare OLS with an alternative consistent estimator in simulation.
asymptotic efficiency simulation estimator comparison
Module 5
Applied Asymptotics Project Module 5 Applied Project
Plan a complete large-sample inference workflow with simulations, diagnostics, and limitations.
applied project asymptotics reporting
Module 6
Scaling and Coefficient Interpretation Why Units Matter in Multiple Regression
Convert slopes across wage units and confirm that fitted relationships do not change when units are handled correctly.
scaling unit conversion coefficient interpretation
Module 6
Standardized Betas Standardized Coefficients
Compare raw and standardized coefficients while avoiding causal importance rankings.
standardized beta comparison interpretation
Module 6
Log Models and Percent Changes Level-Log and Log-Level Models
Practice level-log, log-level, and log-log interpretations with exact percentage conversions.
log models percent changes elasticity
Module 6
Quadratic Turning Points Quadratic Terms and Turning Points
Estimate a quadratic model and calculate turning points and marginal effects.
quadratics turning points marginal effects
Module 6
Interaction Effects Interactions between Continuous Variables
Calculate interaction-based marginal effects at meaningful values.
interactions dummy variables marginal effects
Module 6
Centering Interactions Centering Variables before Interactions
Show how centering changes coefficient meaning while preserving fitted values.
centering interactions collinearity
Module 6
Adjusted R-Squared and Model Comparison Adjusted R-Squared and Model Size
Compare candidate specifications using adjusted R-squared and modeling logic.
adjusted R-squared model comparison controls
Module 6
Bad Controls and Precision Controls Over-Control and Bad Controls
Distinguish harmful controls from safe precision controls.
bad controls precision controls design
Module 6
Prediction and Prediction Intervals Prediction with Multiple Regression
Create fitted predictions and compare mean and individual prediction intervals.
prediction prediction intervals uncertainty
Module 6
Log Prediction and Smearing Predictions when the Dependent Variable Is Logged
Compare naive and smearing-adjusted retransformation from log outcomes.
log prediction smearing retransformation
Module 6
Bootstrap Standard Errors Project Module 6 Applied Forecasting Project
Bootstrap a coefficient and write a careful interpretation with uncertainty.
bootstrap standard errors project
Module 7
Binary Variables Creating Binary Variables
Create and audit binary indicators from transparent rules.
dummy variables qualitative information Python statsmodels
Module 7
Group Mean Comparisons Comparing Two Means with Regression
Show how a dummy-only regression reproduces a two-group mean difference.
dummy variables qualitative information Python statsmodels
Module 7
Dummy Variables in Log Models Dummy Variables in Log-Dependent Models
Convert log-dummy coefficients using approximate and exact percentages.
dummy variables qualitative information Python statsmodels
Module 7
Multiple Categories Multiple Categories
Encode categories with a base group and compare coefficient meanings.
dummy variables qualitative information Python statsmodels
Module 7
Dummy Interactions Binary by Binary Interactions
Interpret binary-by-binary interactions as conditional group differences.
dummy variables qualitative information Python statsmodels
Module 7
Different Slopes and Centering Binary by Continuous Interactions
Estimate group-specific slopes and show how centering changes the reference point.
dummy variables qualitative information Python statsmodels
Module 7
Chow-Style Group Difference Tests Full Regression Differences Across Groups
Use interaction restrictions to test full group differences.
dummy variables qualitative information Python statsmodels
Module 7
Linear Probability Model Linear Probability Model
Estimate an LPM and interpret coefficients as probability-point changes.
dummy variables qualitative information Python statsmodels
Module 7
LPM Robust Standard Errors Limitations of the Linear Probability Model
Inspect fitted probabilities and use HC1 robust standard errors.
dummy variables qualitative information Python statsmodels
Module 7
Policy Evaluation and Self-Selection Policy Evaluation and Self-Selection
Compare randomized and self-selected treatment comparisons.
dummy variables qualitative information Python statsmodels
Module 7
Discrete Dependent Variables Discrete Dependent Variables
Describe count outcomes and explain why discrete models may be needed later.
dummy variables qualitative information Python statsmodels
Module 7
Module 7 Capstone Module 7 Capstone
Complete a full qualitative-variable modeling checklist.
dummy variables qualitative information Python statsmodels
Module 8
Simulating Heteroskedasticity What Is Heteroskedasticity?
Generate and visualize changing variance.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Conventional versus Robust Standard Errors Robust Standard Errors
Compare conventional, HC0, HC1, HC2, and HC3 standard errors.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Breusch-Pagan Test The Breusch-Pagan Test
Run and manually audit the Breusch-Pagan workflow.
heteroskedasticity robust standard errors Python statsmodels
Module 8
White Test The White Test
Use full and fitted-value White diagnostics.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Robust Joint Tests Robust Joint Tests
Test multiple restrictions with robust covariance.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Weighted Least Squares Weighted Least Squares Intuition
Choose inverse-variance weights and compare OLS with WLS.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Population and Group-Size Weights Group Means, Population Weights, and Aggregated Data
Use group size as a precision weight.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Feasible GLS Feasible GLS
Estimate a variance function and fit FGLS.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Robust Standard Errors after WLS What If the WLS Variance Model Is Wrong?
Compare WLS conventional and robust standard errors.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Prediction Intervals with Changing Variance Prediction under Heteroskedasticity
Plot prediction intervals under changing variance.
heteroskedasticity robust standard errors Python statsmodels
Module 8
LPM Robust Inference The Linear Probability Model Revisited
Estimate a linear probability model with HC1 robust standard errors.
heteroskedasticity robust standard errors Python statsmodels
Module 8
Module 8 Capstone Module 8 Capstone
Complete a full diagnostic and correction workflow.
heteroskedasticity robust standard errors Python statsmodels
Fundamentals of Python for Financial Econometrics
Chapter 1: Welcome to Python and Ceteris LAB Welcome to Python and Ceteris LAB
How can one language carry an economic question from raw observations to a reproducible conclusion?
why Python is useful in economics, finance, econometrics, and AI scripts, notebooks, packages, and environments Run a small end-to-end analysis Recognize the difference between computation and interpretation
Fundamentals of Python for Financial Econometrics
Chapter 2: Installing and Running Python Installing and Running Python
What is the least fragile way to move from “I have a computer” to a working, reproducible Python environment?
Choose between local Python, JupyterLab, VS Code, and Google Colab Create and activate a virtual environment Install packages once in a controlled setup Diagnose common kernel and path problems
Fundamentals of Python for Financial Econometrics
Chapter 3: Variables, Values, Types, Strings, and Dates Variables, Values, Types, Strings, and Dates
How does Python know whether a value is a price, a label, a date, or a logical condition?
Create variables with meaningful names Work with integers, floats, strings, booleans, and None Format text and numeric results Parse and compare dates safely
Fundamentals of Python for Financial Econometrics
Chapter 4: Collections, Indexing, and Slicing Collections, Indexing, and Slicing
How should related values be organized so that the structure communicates what operations are legitimate?
lists, tuples, dictionaries, and sets Index and slice ordered collections Build nested structures Choose a collection based on meaning rather than habit
Fundamentals of Python for Financial Econometrics
Chapter 5: Decisions, Loops, and Iteration Decisions, Loops, and Iteration
How can a script apply a rule repeatedly while remaining easy to inspect?
Write conditional branches Iterate with for and while loops enumerate and zip Recognize when vectorization is clearer
Fundamentals of Python for Financial Econometrics
Chapter 6: Functions, Modules, Errors, and Testing Functions, Modules, Errors, and Testing
How can a calculation be trusted when it is reused in several chapters or projects?
Define reusable functions Validate arguments and raise meaningful errors Import modules without hidden state assertions and tests for expected behaviour
Fundamentals of Python for Financial Econometrics
Chapter 7: Files, Paths, Projects, and Reproducibility Files, Paths, Projects, and Reproducibility
How can a project find its data tomorrow, on another computer, and after being uploaded to a website?
Build a portable project tree pathlib and relative paths Separate raw, frozen, processed, and generated files Record versions, sources, and random seeds
Fundamentals of Python for Financial Econometrics
Chapter 8: NumPy and Numerical Computing NumPy and Numerical Computing
Why are numerical arrays faster and more expressive than repeatedly updating Python lists?
Create NumPy arrays Understand shape, dtype, broadcasting, and vectorization Generate reproducible random samples Perform matrix and linear-algebra operations
Fundamentals of Python for Financial Econometrics
Chapter 9: pandas Fundamentals pandas Fundamentals
How can a rectangular dataset preserve labels, dates, and missing values while supporting fast analysis?
Create Series and DataFrames Inspect rows, columns, indexes, and dtypes Select, filter, sort, and assign variables method chains without hiding intermediate meaning
Fundamentals of Python for Financial Econometrics
Chapter 10: Importing, Cleaning, Validating, and Exporting Data Importing, Cleaning, Validating, and Exporting Data
How can messy input be transformed without erasing the evidence of what was changed?
Read common tabular formats Parse dates and numeric columns explicitly Handle missing, duplicate, invalid, and extreme values Write validation checks and export clean data
Fundamentals of Python for Financial Econometrics
Chapter 11: Manipulating, Grouping, Reshaping, and Joining Data Manipulating, Grouping, Reshaping, and Joining Data
How can information be reorganized without accidentally changing the number or meaning of observations?
Aggregate with groupby Move between wide and long forms Merge tables using keys Diagnose duplicate keys and many-to-many joins
Fundamentals of Python for Financial Econometrics
Chapter 12: Data Visualization with Python Data Visualization with Python
What should a figure reveal that a table or coefficient cannot show as clearly?
Construct line, scatter, distribution, and categorical charts Label units, sources, and transformations Design for accessibility and grayscale printing Recognize misleading scales and overplotting
Fundamentals of Python for Financial Econometrics
Chapter 13: Descriptive Statistics and Exploratory Data Analysis Descriptive Statistics and Exploratory Data Analysis
How can a dataset be summarized without letting one number erase its shape?
Calculate location, spread, shape, and dependence statistics Compare conventional and robust summaries skewness and kurtosis plots and tables together
Fundamentals of Python for Financial Econometrics
Chapter 14: Probability, Simulation, and Statistical Inference Probability, Simulation, and Statistical Inference
How can uncertainty be represented, simulated, and summarized without pretending that one sample is the population?
Work with random variables and common distributions Monte Carlo simulation sampling distributions and standard errors confidence intervals, tests, and power
Fundamentals of Python for Financial Econometrics
Chapter 15: Regression and Econometrics with Python Regression and Econometrics with Python
What does a regression coefficient mean, and which assumptions are needed before it can support an economic claim?
Estimate simple and multiple linear regressions coefficients, interactions, and uncertainty Diagnose residual patterns and heteroskedasticity explanatory inference from predictive evaluation
Fundamentals of Python for Financial Econometrics
Chapter 16: Obtaining Economic and Financial Data Obtaining Economic and Financial Data
How can a live data source be used without making the analysis disappear when the network or provider changes?
Retrieve data from authoritative APIs Record series IDs, units, frequencies, and retrieval dates Cache legally redistributable snapshots Build graceful fallbacks and error handling
Fundamentals of Python for Financial Econometrics
Chapter 17: Econometric Thinking and Research Design Econometric Thinking and Research Design
What exactly are we trying to learn from economic data?
economic model versus econometric model population, sample, parameter, estimate, and estimand data-generating processes exogeneity and endogeneity
Fundamentals of Python for Financial Econometrics
Chapter 18: Simple Linear Regression Simple Linear Regression
How does consumption change with income in a simple model?
population regression function ordinary least squares intercept and slope fitted values and residuals
Fundamentals of Python for Financial Econometrics
Chapter 19: Multiple Regression and the Ceteris Paribus Interpretation Multiple Regression and the Ceteris Paribus Interpretation
How can we compare two people while holding other observed characteristics constant?
multiple explanatory variables partial effects ceteris paribus interpretation confounding
Fundamentals of Python for Financial Econometrics
Chapter 20: Functional Forms, Dummy Variables, and Interactions Functional Forms, Dummy Variables, and Interactions
When is a one-unit change not the right way to describe an economic relationship?
log-level, level-log, and log-log models elasticities quadratic terms dummy variables
Fundamentals of Python for Financial Econometrics
Chapter 21: Inference, Robust Standard Errors, and Diagnostics Inference, Robust Standard Errors, and Diagnostics
How certain should we be about an estimated coefficient?
sampling uncertainty standard errors t tests and confidence intervals joint F tests
Fundamentals of Python for Financial Econometrics
Chapter 22: Omitted Variables, Measurement Error, Simultaneity, and Endogeneity Omitted Variables, Measurement Error, Simultaneity, and Endogeneity
Why can a beautifully estimated regression still answer the wrong question?
omitted-variable bias bad controls measurement error reverse causality
Fundamentals of Python for Financial Econometrics
Chapter 23: Instrumental Variables and Two-Stage Least Squares Instrumental Variables and Two-Stage Least Squares
Can we isolate useful variation in an endogenous explanatory variable?
instrument relevance exclusion restriction first stage reduced form
Fundamentals of Python for Financial Econometrics
Chapter 24: Panel Data and Fixed Effects Panel Data and Fixed Effects
What can repeated observations teach us that a single cross-section cannot?
entity and time effects pooled OLS first differences fixed effects
Fundamentals of Python for Financial Econometrics
Chapter 25: Binary, Ordered, and Count Outcomes Binary, Ordered, and Count Outcomes
How should we model outcomes that are probabilities, categories, or event counts?
linear probability model logit and probit odds and marginal effects ordered logit and probit
Fundamentals of Python for Financial Econometrics
Chapter 26: Censoring, Selection, Quantiles, and Robust Methods Censoring, Selection, Quantiles, and Robust Methods
What if the mean is not the whole story, or part of the outcome is unobserved?
censoring and truncation sample selection missing-data mechanisms survey weights
Fundamentals of Python for Financial Econometrics
Chapter 27: Potential Outcomes and Randomized Experiments Potential Outcomes and Randomized Experiments
What does a causal effect mean for one unit, and why can we never observe both potential outcomes?
potential outcomes individual and average treatment effects fundamental problem of causal inference random assignment
Fundamentals of Python for Financial Econometrics
Chapter 28: Matching, Propensity Scores, Weighting, and Doubly Robust Estimation Matching, Propensity Scores, Weighting, and Doubly Robust Estimation
How can observational studies improve comparability when treatment is not randomized?
selection on observables propensity score overlap common support
Fundamentals of Python for Financial Econometrics
Chapter 29: Difference-in-Differences and Event Studies Difference-in-Differences and Event Studies
How can policy evaluation use changes over time when randomized assignment is unavailable?
treated and comparison groups parallel trends two-period DID regression implementation
Fundamentals of Python for Financial Econometrics
Chapter 30: Regression Discontinuity Regression Discontinuity
What can a policy cutoff reveal about causal effects near the threshold?
running variable cutoff sharp and fuzzy designs local polynomial regression
Fundamentals of Python for Financial Econometrics
Chapter 31: Synthetic Control and Comparative Case Studies Synthetic Control and Comparative Case Studies
How can one treated region be compared with a data-driven combination of untreated regions?
treated unit and donor pool pre-treatment fit synthetic weights predictor balance
Fundamentals of Python for Financial Econometrics
Chapter 32: Regularization and High-Dimensional Economic Models Regularization and High-Dimensional Economic Models
How do we build stable predictions when the number of candidate predictors becomes large?
ridge regression lasso elastic net standardization
Fundamentals of Python for Financial Econometrics
Chapter 33: Decision Trees, Random Forests, and Boosting for Economic Prediction Decision Trees, Random Forests, and Boosting for Economic Prediction
Can flexible nonlinear models improve prediction without pretending to identify causal effects?
recursive partitioning tree depth and pruning bagging random forests
Fundamentals of Python for Financial Econometrics
Chapter 34: Causal Machine Learning and Double Machine Learning Causal Machine Learning and Double Machine Learning
How can flexible prediction tools help estimate causal parameters without replacing identification assumptions?
nuisance functions orthogonal scores residualization cross-fitting
Fundamentals of Python for Financial Econometrics
Chapter 35: Explainability, Uncertainty, and Model Governance Explainability, Uncertainty, and Model Governance
How do we decide whether a powerful prediction model is trustworthy enough to use?
coefficients versus feature importance permutation importance partial dependence SHAP as an optional tool
Fundamentals of Python for Financial Econometrics
Chapter 36: Introduction to Time-Series Data Introduction to Time-Series Data
What changes when observations are ordered in time and yesterday can influence today?
Create and validate time indexes levels, changes, growth rates, and returns Resample data with appropriate aggregation Handle missing dates and trading calendars
Fundamentals of Python for Financial Econometrics
Chapter 37: Dependence, Stationarity, and White Noise Dependence, Stationarity, and White Noise
How can we tell whether a time series contains predictable linear structure rather than random fluctuation?
Define weak stationarity and white noise Compute and interpret ACF and PACF Apply Ljung-Box and unit-root diagnostics Recognize volatility clustering and nonlinear dependence
Fundamentals of Python for Financial Econometrics
Chapter 38: AR, MA, ARMA, ARIMA, and Forecasting AR, MA, ARMA, ARIMA, and Forecasting
How can past values and past shocks be organized into a model that produces honest forecasts and uncertainty?
AR and MA dynamics ACF, PACF, AIC, and BIC for model guidance Estimate ARIMA models with statsmodels Evaluate point and interval forecasts out of sample
Fundamentals of Python for Financial Econometrics
Chapter 39: Unit Roots, Seasonality, and Dynamic Regression Unit Roots, Seasonality, and Dynamic Regression
How can a model distinguish persistent trend, recurring seasonality, and serially correlated errors?
Diagnose unit roots and differencing needs Apply seasonal differencing and decomposition Estimate SARIMA and SARIMAX models regression with time-series errors cautiously
Fundamentals of Python for Financial Econometrics
Chapter 40: ARCH and GARCH Models ARCH and GARCH Models
How can returns be difficult to predict while their volatility remains persistent and forecastable?
conditional variance and volatility clustering Test for ARCH effects Estimate ARCH and GARCH models Diagnose standardized residuals and forecast volatility
Fundamentals of Python for Financial Econometrics
Chapter 41: Asymmetric, Advanced, and Realized Volatility Asymmetric, Advanced, and Realized Volatility
Why can equally large negative and positive shocks have different volatility consequences, and what can intraday or OHLC data add?
Compare EGARCH, GJR-GARCH, APARCH, and GARCH-M concepts leverage and news-impact curves Calculate rolling, EWMA, realized, and range-based volatility Discuss microstructure noise and overnight information
Fundamentals of Python for Financial Econometrics
Chapter 42: Nonlinear Models, Regimes, Market Microstructure, and Ordered Outcomes 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?
Simulate and interpret threshold autoregression Markov-switching regimes and expected duration market-microstructure effects Estimate and interpret ordered probit probabilities
Fundamentals of Python for Financial Econometrics
Chapter 43: Stochastic Processes and Option Pricing Stochastic Processes and Option Pricing
How can continuous-time uncertainty be simulated and translated into a no-arbitrage option value?
Simulate Brownian motion and geometric Brownian motion Ito’s lemma intuitively Calculate European option payoffs and Black-Scholes prices Monte Carlo and sensitivity analysis
Fundamentals of Python for Financial Econometrics
Chapter 44: Financial Risk Management Financial Risk Management
How much could be lost, how often should that threshold be exceeded, and what happens beyond it?
Define a loss variable and coherent risk principles Calculate VaR and Expected Shortfall Compare historical, parametric, GARCH, and EVT methods Backtest exceedances and discuss stress testing
Fundamentals of Python for Financial Econometrics
Chapter 45: Multiple Time Series Multiple Time Series
When several series move together, which relationships are short-run predictive and which are long-run equilibrating?
Compute cross-correlation structures Estimate and diagnose VAR models Granger predictability and impulse responses cautiously Test cointegration and describe VECM adjustment
Fundamentals of Python for Financial Econometrics
Chapter 46: Econometrics and Machine Learning: Different Questions, Shared Tools 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?
AI, machine learning, deep learning, and generative AI Separate rule-based systems from learned models regression, classification, generation, and retrieval Identify hallucination, bias, privacy, and deepfake risks
Fundamentals of Python for Financial Econometrics
Chapter 47: Machine-Learning Workflow and Gradient Descent for Economic Data Machine-Learning Workflow and Gradient Descent for Economic Data
How can a model be trained without letting information from the future or test set leak into the learning process?
Define features, labels, train, validation, and test sets Recognize overfitting, underfitting, and leakage loss functions and gradient descent Build reproducible preprocessing pipelines
Fundamentals of Python for Financial Econometrics
Chapter 48: Applied Economic Prediction and Classification Projects Applied Economic Prediction and Classification Projects
How can an end-to-end workflow turn housing and passenger data into models that are evaluated honestly and interpreted carefully?
Build a regression workflow with the Tehran housing data Build a classification workflow with Titanic data pipelines, cross-validation, and multiple metrics Analyze residuals, confusion matrices, and subgroup performance
Fundamentals of Python for Financial Econometrics
Chapter 49: Neural Networks and Deep Learning for Economic Data Neural Networks and Deep Learning for Economic Data
How does a stack of simple differentiable units learn a nonlinear mapping, and how do we know it has not merely memorized the training sample?
neurons, activations, layers, and forward passes backpropagation and optimization Train a small PyTorch network learning curves, regularization, and validation
Fundamentals of Python for Financial Econometrics
Chapter 50: Computer Vision and Spatial Economic Measurement Computer Vision and Spatial Economic Measurement
How does a model transform pixels into useful local patterns without being told the exact edge or texture to search for?
Represent images as arrays convolution, stride, padding, and channels CNNs, augmentation, and transfer learning Evaluate errors, bias, and privacy
Fundamentals of Python for Financial Econometrics
Chapter 51: Natural-Language Processing and Transformers for Economics Natural-Language Processing and Transformers for Economics
How can text be converted into numerical representations while preserving enough context to classify or retrieve meaning?
Tokenize and vectorize text TF-IDF and word embeddings cosine similarity, attention, and transformers Evaluate text classifiers and language limitations
Fundamentals of Python for Financial Econometrics
Chapter 52: Language Models, Retrieval, and AI Agents for Econometric Research Language Models, Retrieval, and AI Agents for Econometric Research
How can a language model answer from a controlled evidence collection, use tools, and still remain subject to verification?
next-token prediction and context windows Build a simple retrieval-augmented workflow agent loops, tools, memory, and stopping conditions Evaluate grounding, privacy, cost, and safety
Fundamentals of Python for Financial Econometrics
Chapter 53: Capstone Projects and Student Portfolio Capstone Projects and Student Portfolio
How can a student transform code fragments into a defensible, reproducible analytical product for the Ceteris LAB website or a professional portfolio?
Plan a complete project from question to communication authoritative data and a data dictionary Compare baseline and advanced models out of sample Package code, figures, limitations, and reproducibility files