manus-skills/.kortix/opencode/skills/data/statsmodels/references/modeling_capabilities.md

Core Statistical Modeling Capabilities

Linear models, generalized linear models, discrete choice models, time series analysis, and statistical tests and diagnostics — what statsmodels offers in each area and which class to use.

Core Statistical Modeling Capabilities

1. Linear Regression Models

Comprehensive suite of linear models for continuous outcomes with various error structures.

Available models:

• OLS: Standard linear regression with i.i.d. errors
• WLS: Weighted least squares for heteroskedastic errors
• GLS: Generalized least squares for arbitrary covariance structure
• GLSAR: GLS with autoregressive errors for time series
• Quantile Regression: Conditional quantiles (robust to outliers)
• Mixed Effects: Hierarchical/multilevel models with random effects
• Recursive/Rolling: Time-varying parameter estimation

Key features:

• Comprehensive diagnostic tests
• Robust standard errors (HC, HAC, cluster-robust)
• Influence statistics (Cook's distance, leverage, DFFITS)
• Hypothesis testing (F-tests, Wald tests)
• Model comparison (AIC, BIC, likelihood ratio tests)
• Prediction with confidence and prediction intervals

When to use: Continuous outcome variable, want inference on coefficients, need diagnostics

Reference: See references/linear_models.md for detailed guidance on model selection, diagnostics, and best practices.

2. Generalized Linear Models (GLM)

Flexible framework extending linear models to non-normal distributions.

Distribution families:

• Binomial: Binary outcomes or proportions (logistic regression)
• Poisson: Count data
• Negative Binomial: Overdispersed counts
• Gamma: Positive continuous, right-skewed data
• Inverse Gaussian: Positive continuous with specific variance structure
• Gaussian: Equivalent to OLS
• Tweedie: Flexible family for semi-continuous data

Link functions:

• Logit, Probit, Log, Identity, Inverse, Sqrt, CLogLog, Power
• Choose based on interpretation needs and model fit

Key features:

• Maximum likelihood estimation via IRLS
• Deviance and Pearson residuals
• Goodness-of-fit statistics
• Pseudo R-squared measures
• Robust standard errors

When to use: Non-normal outcomes, need flexible variance and link specifications

Reference: See references/glm.md for family selection, link functions, interpretation, and diagnostics.

3. Discrete Choice Models

Models for categorical and count outcomes.

Binary models:

• Logit: Logistic regression (odds ratios)
• Probit: Probit regression (normal distribution)

Multinomial models:

• MNLogit: Unordered categories (3+ levels)
• Conditional Logit: Choice models with alternative-specific variables
• Ordered Model: Ordinal outcomes (ordered categories)

Count models:

• Poisson: Standard count model
• Negative Binomial: Overdispersed counts
• Zero-Inflated: Excess zeros (ZIP, ZINB)
• Hurdle Models: Two-stage models for zero-heavy data

Key features:

• Maximum likelihood estimation
• Marginal effects at means or average marginal effects
• Model comparison via AIC/BIC
• Predicted probabilities and classification
• Goodness-of-fit tests

When to use: Binary, categorical, or count outcomes

Reference: See references/discrete_choice.md for model selection, interpretation, and evaluation.

4. Time Series Analysis

Comprehensive time series modeling and forecasting capabilities.

Univariate models:

• AutoReg (AR): Autoregressive models
• ARIMA: Autoregressive integrated moving average
• SARIMAX: Seasonal ARIMA with exogenous variables
• Exponential Smoothing: Simple, Holt, Holt-Winters
• ETS: Innovations state space models

Multivariate models:

• VAR: Vector autoregression
• VARMAX: VAR with MA and exogenous variables
• Dynamic Factor Models: Extract common factors
• VECM: Vector error correction models (cointegration)

Advanced models:

• State Space: Kalman filtering, custom specifications
• Regime Switching: Markov switching models
• ARDL: Autoregressive distributed lag

Key features:

• ACF/PACF analysis for model identification
• Stationarity tests (ADF, KPSS)
• Forecasting with prediction intervals
• Residual diagnostics (Ljung-Box, heteroskedasticity)
• Granger causality testing
• Impulse response functions (IRF)
• Forecast error variance decomposition (FEVD)

When to use: Time-ordered data, forecasting, understanding temporal dynamics

Reference: See references/time_series.md for model selection, diagnostics, and forecasting methods.

5. Statistical Tests and Diagnostics

Extensive testing and diagnostic capabilities for model validation.

Residual diagnostics:

• Autocorrelation tests (Ljung-Box, Durbin-Watson, Breusch-Godfrey)
• Heteroskedasticity tests (Breusch-Pagan, White, ARCH)
• Normality tests (Jarque-Bera, Omnibus, Anderson-Darling, Lilliefors)
• Specification tests (RESET, Harvey-Collier)

Influence and outliers:

• Leverage (hat values)
• Cook's distance
• DFFITS and DFBETAs
• Studentized residuals
• Influence plots

Hypothesis testing:

• t-tests (one-sample, two-sample, paired)
• Proportion tests
• Chi-square tests
• Non-parametric tests (Mann-Whitney, Wilcoxon, Kruskal-Wallis)
• ANOVA (one-way, two-way, repeated measures)

Multiple comparisons:

• Tukey's HSD
• Bonferroni correction
• False Discovery Rate (FDR)

Effect sizes and power:

• Cohen's d, eta-squared
• Power analysis for t-tests, proportions
• Sample size calculations

Robust inference:

• Heteroskedasticity-consistent SEs (HC0-HC3)
• HAC standard errors (Newey-West)
• Cluster-robust standard errors

When to use: Validating assumptions, detecting problems, ensuring robust inference

Reference: See references/stats_diagnostics.md for comprehensive testing and diagnostic procedures.