mixedREndo-class

internal

Fits linear models with endogenous regressor using latent
instrumental variable approaches. The methods included in the package
are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as Lewbel's (2012) <doi:10.1080/07350015.2012.643126>
heteroskedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718>joint estimation method
that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
method of moment approach that deals with endogeneity in a multilevel setting.
These are statistical techniques to address the endogeneity problem where no
external instrumental variables are needed.
This version:
- solves an error occurring when using the multilevelIV() function with two levels, random intercept.
- returns the AIC and BIC for copulaCorrection() (method 1) and latentIV() methods.
- residuals and fitted values can be saved by users for latentIV() and copulaCorrection() methods.
- improves the summary methods for copulaCorrection() and multilevelIV() functions.

Raluca Gui

REndo

Fitting Linear Models with Endogenous Regressors using Latent
Instrumental Variables

mixedREndo-class function

 <dl class="dl-horizontal">
<dt><code>coefficients</code></dt><dd>- the estimated coefficients.</dd> <dt><code>coefSdErr</code></dt><dd>- stabdard errors of the estimates.</dd> <dt><code>formula</code></dt><dd>- the formula used for estimation.</dd> <dt><code>vcovMat</code></dt><dd>- variance-covariance matrix.</dd> <dt><code>weightMat</code></dt><dd>- the weight matrix used for estimation.</dd>
</dl>

mixedREndo-class: mixedREndo S4 Object

Description

Arguments

Slots

Examples