copulaCont1

a vector or matrix containing the dependent variable.

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

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

Initial values for the parameters to be optimized over.

param

It should be specified whether the model should be estimated with or without 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;FALSE&quot;, otherwise the value &quot;TRUE&quot;.

intercept

optional 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:
- 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.

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

Description

Usage

Arguments

Value