copulaDiscrete

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

formula

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

endoVar

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

Fits linear models with discrete, endogeneous regressors using the approach described in Park and Gupta (2012). Due to the variablility 
in pStar, a simulation was needed in order to obtain the coefficient estimates. Then, in order to get the Z-scores and p-values the sum of the Z method was used
as described in Zaykin, D V.(2011). "Optimally weighted Z-test is a powerful method for combining probabilities in meta-analysis". Journal of Evolutionary Biology, 24:1836-1841.

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:
- 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.

copulaDiscrete: Fitting Linear Models with Endogeneous Discrete Regressors using Internal Instrumental Variables

Description

Usage

Arguments

Value

References