Estimation methods

Standard estimation methods

These functions perform parameter estimation, diagnostics and print reports.

fgls	Estimate parameters using feasible generalized least squares and provides model evaluation statistics.
glm	Solves the generalized linear model problem.
gmmFit	Estimate parameters using generalized method of moments.
gmmFitIV	Estimate instrumental variables model using the generalized method of moments.
kernelDensity	Computes the kernel density estimate of a sample and plots the distribution.
olsmt	Computes a least squares regression.
qfitSlopeTest	Performs post-estimation slope equality test after quantile regression.
quantileFit	Perform linear quantile regression.
quantileFitLoc	Perform local linear or quadratic quantile regression.
../waltest	Performs post-estimation tests of hypotheses.

Standard error methods

clusterSE	Computes the White cluster-robust standard errors.
hacSE	Computes the Newey-West HAC robust standard errors. The procedure uses the “sandwich” variance-covariance estimator with a small sample correction of \((n)/(n−1)\).
robustSE	Computes the Huber-White heteroscedastic robust standard errors. The procedure uses the “sandwich” variance-covariance estimator with a small sample correction of \((n)/(n−1)\).

Lower level estimation

Note

For most cases, the slash operator b_hat = y / X or olsqr() are the preferred methods to compute least-squares estimates.

ldlsol	Computes the solution to a system of linear equations given a factorized matrix returned by the function ldlp and one or more right hand sides.
lusol	Computes the solution of \(LUx=b\) where \(L\) and \(U\) are matrix factors returned by lu.
olsqr	Computes OLS coefficients using \(QR\) decomposition.
olsqr2	Computes OLS coefficients, residuals, and predicted values using the \(QR\) decomposition.
solpd	Solves a set of positive definite linear equations.