Module Reference

Instrumental Variable Estimation

IV2SLS(dependent, exog, endog, instruments, *)	Estimation of IV models using two-stage least squares
IVLIML(dependent, exog, endog, instruments, *)	Limited information ML and k-class estimation of IV models
IVGMM(dependent, exog, endog, instruments, *)	Estimation of IV models using the generalized method of moments (GMM)
IVGMMCUE(dependent, exog, endog, instruments, *)	Estimation of IV models using continuously updating GMM

Absorbing Least Squares

OLS and WLS with high-dimensional effects.

AbsorbingLS(dependent[, exog, absorb, …])	Linear regression with high-dimensional effects
AbsorbingLSResults(results, model)	Results from IV estimation
Interaction([cat, cont, nobs])	Class that simplifies specifying interactions

Estimation Results

IVResults(results, model)	Results from IV estimation
IVGMMResults(results, model)	Results from GMM estimation of IV models
OLSResults(results, model)	Results from OLS model estimation
IVModelComparison(results, *[, precision])	Comparison of multiple models
FirstStageResults(dep, exog, endog, instr, …)	First stage estimation results and diagnostics
compare(results, …)	Compare the results of multiple models

Instrumental Variable Covariance Estimation

HomoskedasticCovariance(x, y, z, params[, …])	Covariance estimation for homoskedastic data
HeteroskedasticCovariance(x, y, z, params[, …])	Covariance estimation for heteroskedastic data
ClusteredCovariance(x, y, z, params[, …])	Covariance estimation for clustered data
KernelCovariance(x, y, z, params[, kernel, …])	Kernel weighted (HAC) covariance estimation

Kernel Weight Generators

kernel_weight_bartlett(bw, \*args)	Kernel weights from a Bartlett kernel
kernel_weight_parzen(bw, \*args)	Kernel weights from a Parzen kernel
kernel_weight_quadratic_spectral(bw, n)	Kernel weights from a quadratic-spectral kernel

GMM Weight and Covariance Estimation

IVGMMCovariance(x, y, z, params, w[, …])	Covariance estimation for GMM models
HomoskedasticWeightMatrix([center, debiased])	Homoskedastic (unadjusted) weight estimation
HeteroskedasticWeightMatrix([center, debiased])	Heteroskedasticity robust weight estimation
KernelWeightMatrix([kernel, bandwidth, …])	Heteroskedasticity, autocorrelation robust weight estimation
OneWayClusteredWeightMatrix(clusters[, …])	Clustered (one-way) weight estimation

IV Data Structures

IVData(x[, var_name, nobs, convert_dummies, …])

Type abstraction for use in univariate models.