Package index • hetid

Data

Datasets included with the package

lewbel_sim: Simulated Lewbel Test Data

Data Generation

Functions for generating data that satisfies Lewbel’s assumptions

generate_lewbel_data(): Generate Data for Lewbel (2012) Triangular Model

generate_hetid_test_data(): Generate consistent test data for all comparisons

verify_lewbel_assumptions(): Verify Lewbel's Key Identifying Assumptions

generate_seed_matrix(): Generate Seed Matrix for Reproducible Parallel Simulations

generate_all_seeds(): Generate All Seeds for Lewbel Simulation

Estimation

Core estimation functions for the Lewbel method

calculate_lewbel_bounds(): Calculate Set Identification Bounds for Lewbel Estimator

run_single_lewbel_simulation(): Run Single Lewbel Simulation

GMM (Generalized Method of Moments)

GMM estimation for heteroskedasticity-based identification methods

lewbel_gmm(): Estimate Lewbel Model using GMM

lewbel_triangular_moments(): Define GMM Moment Conditions for Lewbel Triangular System

lewbel_simultaneous_moments(): Define GMM Moment Conditions for Lewbel Simultaneous System

rigobon_gmm(): GMM Estimation for Rigobon (2003) Regime-Based Identification

rigobon_moment_conditions(): Define GMM Moment Conditions for Rigobon Triangular System

prono_gmm(): GMM Estimation for Prono (2014) GARCH-Based Identification

prono_triangular_moments(): Define GMM Moment Conditions for Prono Triangular System

compare_gmm_2sls(): Compare GMM and 2SLS Estimates for Lewbel Model

print(<lewbel_gmm>): Print Method for Lewbel GMM

summary(<lewbel_gmm>): Summary Method for Lewbel GMM

Monte Carlo Simulations

Functions for running Monte Carlo experiments

run_lewbel_monte_carlo(): Run Complete Lewbel (2012) Monte Carlo Simulation

run_lewbel_demo(): Run Quick Lewbel Monte Carlo Demo

run_main_simulation(): Run Main Lewbel Monte Carlo Simulation

run_sample_size_analysis(): Run Sample Size Analysis

run_sensitivity_analysis(): Run Sensitivity Analysis

run_bootstrap_demonstration(): Run Bootstrap Demonstration

create_default_config(): Create Default Configuration for Lewbel Monte Carlo Simulations

Rigobon (2003) Method

Functions for regime-based heteroskedasticity identification

generate_rigobon_data(): Generate Data for Rigobon (2003) Regime-Based Model

run_rigobon_analysis(): Rigobon (2003) Regime-Based Heteroskedasticity Identification

run_rigobon_estimation(): Run Rigobon (2003) Regime-Based Estimation

run_rigobon_demo(): Run Rigobon (2003) Identification Demo

validate_rigobon_assumptions(): Validate Rigobon Assumptions

compare_rigobon_methods(): Compare Rigobon with Other Methods

Prono (2014) Method

Functions for GARCH-based heteroskedasticity identification

generate_prono_data(): Prono (2014) Heteroskedasticity-Based Identification with GARCH

run_single_prono_simulation(): Run single Prono simulation with GARCH-based instruments

run_prono_monte_carlo(): Run Prono Monte Carlo simulation

run_prono_demo(): Run Prono demonstration

create_prono_config(): Create default configuration for Prono simulations

fit_diagonal_garch_prono(): Diagonal GARCH Implementation for Prono (2014) Method

prono_diagonal_garch(): Run Prono Estimation with Diagonal GARCH

replicate_prono_table2(): Replicate Prono's Table II Results

Klein & Vella (2010) Method

Functions for semiparametric control function approach

generate_klein_vella_data(): Generate data following Klein & Vella (2010) assumptions

verify_klein_vella_assumptions(): Verify Klein & Vella assumptions in data

create_klein_vella_config(): Create configuration for Klein & Vella data generation

klein_vella_parametric(): Parametric Klein & Vella estimation

klein_vella_semiparametric(): Semiparametric Klein & Vella estimation

run_klein_vella_demo(): Run Klein & Vella demonstration

run_klein_vella_monte_carlo(): Run Klein & Vella Monte Carlo simulation

compare_klein_vella_methods(): Compare Klein & Vella methods

summary(<klein_vella_fit>): Summary method for Klein & Vella estimation results

print(<klein_vella_fit>): Print method for Klein & Vella estimation results

print(<klein_vella_semipar>): Print method for semiparametric Klein & Vella results

print(<summary.klein_vella_fit>): Print summary of Klein & Vella estimation results

Analysis

Functions for analyzing simulation results

analyze_main_results(): Analyze Main Simulation Results

analyze_sample_size_results(): Analyze Sample Size Results

analyze_sensitivity_results(): Analyze Sensitivity Results

analyze_bootstrap_results(): Analyze Bootstrap Results

print_simulation_summary(): Print Simulation Summary

Visualization

Functions for creating plots and visualizations

generate_all_plots(): Generate All Simulation Plots

plot_estimator_distributions(): Create Distribution Plot of Estimators

plot_first_stage_f_dist(): Create First-Stage F Distribution Plot

plot_sample_size_consistency(): Create Sample Size Consistency Plot

plot_het_sensitivity(): Create Heteroscedasticity Sensitivity Plot

plot_bootstrap_ci(): Create Bootstrap Confidence Intervals Plot

Degrees of Freedom

Functions for handling degrees of freedom adjustments

adjust_se_for_df(): Adjust standard errors for degrees of freedom

get_critical_value(): Get critical value for confidence intervals

extract_se_lm(): Extract adjusted standard errors from lm model

extract_se_ivreg(): Extract adjusted standard errors from ivreg model

Utilities

Helper functions for checking optional dependencies and Stata integration

has_curl(): Check if curl is available

has_haven(): Check if haven is available

has_rendo(): Check if REndo is available

has_rstata(): Check if RStata is available

has_stata(): Check if Stata is available via RStata

ensure_stata_packages(): Helper to ensure Stata packages are installed

get_stata_path(): Get the actual Stata executable path

hetid_opt(): Get hetid package options with fallback to constants