Overview | Data Preparation (Simulated Example) | Method 1: sfaselectioncross + LP Metafrontier | Method 2: sfaselectioncross + QP Metafrontier | Method 3: sfaselectioncross + SFA (Huang) | Method 4: sfaselectioncross + SFA (O'Donnell) | Interpreting the Selection Correction | Extracting Efficiencies

Overview | Data Preparation | Method 1: LP Metafrontier | Method 2: QP Metafrontier | Method 3: Stochastic Metafrontier — Huang et al. (2014) | Method 4: Stochastic Envelope — O'Donnell et al. (2008) | Comparing Methods | Extracting Efficiencies | Other Extractors

Overview | Setup: Fit a Model | summary() — Full Model Summary | efficiencies() — Firm-Level Efficiency and MTR Scores | Column Reference | Subsetting by Group | Visualising the Distribution | coef() — Estimated Coefficients | vcov() — Variance-Covariance Matrix | logLik() — Log-Likelihood | ic() — Information Criteria | nobs() — Number of Observations | fitted() — Fitted Values | residuals() — Residuals | Comparing Multiple Models

What is smfa? | Conceptual Framework | Methods Supported | Installation | Quick-Start Example | Step 1: Load data and create groups | Step 2: Fit the metafrontier model | Step 3: Summarise results | Step 4: Extract firm-level efficiencies | What Next?

Overview | Data Preparation | Method 1: LCM + LP Metafrontier | Method 2: LCM + QP Metafrontier | Method 3: LCM + SFA (Huang) | Method 4: LCM + SFA (O'Donnell) | Choosing the Number of Classes | Extracting Efficiencies and Posterior Probabilities | Class membership summary | Key Difference from sfacross

Introduction | 1. Data Description and Preparation | 2. Cross-Sectional Fractional Models (fracreg) | 2.1 Empirical and Simulated Fractional Regressions | 2.1.2 Binary Logit Component of a Two-Part Model | 2.1.3 Fractional Component of a Two-Part Model | 2.1.4 Full Two-Part Model (Joint Estimation) | 2.1.5 Three-Part Double-Inflated Model | 2.2 Partial Effects (fracreg.pe) | 2.2.2 APEs for a Two-Part Model | 2.2.3 Conditional Partial Effects (CPE) | 2.2.4 APEs for a Three-Part Model | 3. Hypothesis Testing and Specification Diagnostics | 3.1 Generalised Goodness-Of-Functional-Form (fracreg.ggoff) | 3.1.2 GGOFF Test for the Binary Component | 3.2 RESET Test (fracreg.reset) | 3.2.2 RESET Test for the Binary Component | 3.3 P-Test for Non-Nested Models (fracreg.ptest) | 3.3.2 P-Test: 1P vs. 2P Model | 4. Endogeneity & Heteroscedasticity (fracreghet) | 4.1.2 GMMz Estimator | 4.1.3 GMMxv Estimator | 4.1.4 QMLxv Control Function Estimator | 4.1 Partial Effects for Endogenous Models (fracreghet.pe) | 4.1.2 Naive Estimator for CPEs | 4.2 RESET Test for Endogenous Models (fracreghet.reset) | 5. Panel Data Fractional Models (fracregpd) | 5.1.2 Standard Panel GMM (GMMbgw) | 5.1.3 Generalized Method of Moments (GMMww) | 5.1.4 GMMww with Lagged Covariates | 5.1.5 Endogenous Panel GMM (GMMpfe) | 6. Fractional Ridge Regression | 6.1 Empirical 401(k) Example | 6.2 Simulated Data Example | 7. Fractional Multinomial Logit | 7.1 Estimating Budget Shares | 7.2 Willingness to Pay (WTP) | Conclusion | Acknowledgements | References