dataCopC2

A dataset with two exogenous, normally distributed regressors, <code>X1</code> and <code>X2</code>, two endogenous, continuous regressors, <code>P1</code> and <code>P2</code>, having a T-distribution with 3 and 5 degrees of freedom respectively, with a correlation of 0.25. The correlation between <code>P1</code> and the error was set at 0.33, while between <code>P2</code> and the error, at 0.15. The dataset contains an intercept and the dependent variable, <code>y</code>. The true parameter value for the model: <code>y = b0 + b1 * X1+ b2 * X2 + a1 * P1 + a2 * P2 + eps</code>, are: <code>b0 = 2, b1 = 1.5, b2 = -3, a1 = -1, a2 = 0.8</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:
- 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.

Raluca Gui

REndo

Fitting Linear Models with Endogenous Regressors using Latent
Instrumental Variables

dataCopC2 function

A data frame with 2500 observations on the following 6 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>P1</code></dt><dd>a numeric vector, continuous and endogenous having T-distribution with 3 degrees of freedom.</dd>
    <dt><code>P2</code></dt><dd>a numeric vector, continuous and endogenous having T-distribution with 5 degrees of freedom.</dd>
  </dl>

Format


 — dataCopC2

A data frame with 2500 observations on the following 6 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>P1</code></dt><dd>a numeric vector, continuous and endogenous having T-distribution with 3 degrees of freedom.</dd>
    <dt><code>P2</code></dt><dd>a numeric vector, continuous and endogenous having T-distribution with 5 degrees of freedom.</dd>
  </dl>

dataCopC2:

Description

Usage

Arguments

Format