boots

the vector or matrix containing the dependent variable.

the data frame or matrix containing the regressors of the model, both exogeneous and endogeneous. The last column/s should contain the endogeneous variable/s.

the vector containing the continuous, non-normally distributed endogeneous variable.

initial values for the parameters to be optimized over. See <code><a rd-options="" href="/link/copulaCorrection?package=REndo&version=1.2" data-mini-rdoc="REndo::copulaCorrection">copulaCorrection</a></code> for more details.

param

an optional parameter. The model is estimated by default with 
intercept. If no intercept is desired or the regressors matrix <code>X</code> contains already
a column of ones, intercept should be given the value &quot;no&quot;.

intercept

optional data frame or matrix containing the variables of the model.

data

Performs bootstrapping to obtain the standard errors of the estimates of the model with one continuous endogenous regressor estimated via maximum likelihood
using the <code><a rd-options="" href="/link/copulaCorrection?package=REndo&version=1.2" data-mini-rdoc="REndo::copulaCorrection">copulaCorrection</a></code> function.

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

boots function

initial values for the parameters to be optimized over. See <code><a rd-options='' href='copulaCorrection'>copulaCorrection</a></code> for more details.

Performs bootstrapping to obtain the standard errors of the estimates of the model with one continuous endogenous regressor estimated via maximum likelihood
using the <code><a rd-options='' href='copulaCorrection'>copulaCorrection</a></code> function.

boots: Bootstrapping Standard Errors

Description

Usage

Arguments

Value

Details

See Also