copulaCont1

the model formula, e.g. <code>y ~ X1 + X2 + X3</code>.

formula

a string with the name of the endogenous variable/s, in quotation marks.

endoVar

initial values for the parameters to be optimized over.

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 "FALSE", otherwise the value "TRUE".

intercept

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

data

Estimates a linear model with one endogenous variable using Gaussian copula. The optimization is done via maximum likelihood, using the BFLG algorithm.

internal

Instrumental

Variables

copula

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:
- includes an omitted variable test in the multilevel estimation. It is reported in the summary() function of the
multilevelIV() function.
- resolves the error "Error in listIDs[, 1] : incorrect number of dimensions" when using the multilevelIV() function.
- a new simulated dataset is provided, dataMultilevelIV, on which to exemplify the multilevelIV() function.

copulaCont1: Fitting Linear Models with One Enedogenous Regressor using Gaussian Copula

Description

Usage

Arguments

Value