copulaMethod2

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/s should contain the endogeneous variable/s.

a matrix containing the endogeneous variables. They should be continuous and non-normally distributed.

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

intercept

It fits linear models with several endogeneous regressors using Gaussian Copula proposed by Park and Gupta (2012).

internal

Instrumental

Variables

copula

endogenous

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.

copulaMethod2: Fitting Linear Models with Endogeneous Regressors using Gaussian Copula - Method 2

Description

Usage

Arguments

Value