dataCopC1

A dataset with two exogenous regressors, <code>X1</code>,<code>X2</code>, and one endogenous, continuous regressor, <code>P</code>, having a T-distribution with 3 degrees of freedom. An intercept and a dependent variable, <code>y</code>, are also included. The true parameter values for the coefficients are: <code>b0 = 2</code>, <code>b1 = 1.5</code>, <code>b2 = -3</code> and the coefficient of the endogenous regressor is set to <code>a1 = -1</code>.

datasets

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.

Raluca Gui

REndo

Fitting Linear Models with Endogenous Regressors using Latent
Instrumental Variables

dataCopC1 function

A data frame with 2500 observations on the following 5 variables.<dl class="dl-horizontal">
 <dt><code>y</code></dt><dd>a numeric vector representing the dependent variable.</dd>
 <dt><code>I</code></dt><dd>a numeric vector representing the intercept.</dd>
 <dt><code>X1</code></dt><dd>a numeric vector, normally distributed and exogenous.</dd>
 <dt><code>X2</code></dt><dd>a numeric vector, normally distributed and exogenous.</dd>
 <dt><code>P</code></dt><dd>a numeric vector, continuous and endogenous having T-distribution with 3 degrees of freedom.</dd>
 </dl>

Format

Simulated Dataset — dataCopC1

A data frame with 2500 observations on the following 5 variables.<dl class='dl-horizontal'>
 <dt><code>y</code></dt><dd>a numeric vector representing the dependent variable.</dd>
 <dt><code>I</code></dt><dd>a numeric vector representing the intercept.</dd>
 <dt><code>X1</code></dt><dd>a numeric vector, normally distributed and exogenous.</dd>
 <dt><code>X2</code></dt><dd>a numeric vector, normally distributed and exogenous.</dd>
 <dt><code>P</code></dt><dd>a numeric vector, continuous and endogenous having T-distribution with 3 degrees of freedom.</dd>
 </dl>

dataCopC1: Simulated Dataset

Description

Usage

Arguments

Format