dataCopDis

A dataset with an intercept, two exogenous regressors and one endogenous, discrete variable, used for exemplifying the use of <code><a rd-options="" href="/link/copulaCorrection?package=REndo&version=1.2" data-mini-rdoc="REndo::copulaCorrection">copulaCorrection</a></code> function. The true parameter values are: <code>b0 = 2</code>, <code>b1 = 1.5</code>, <code>b2 = -3</code>, and the coefficient of the endogenous variable is set to <code>a1 = -1</code>. The correlation between the endogenous regressor <code>P</code> and the error term is 0.33. <code>P</code> has a Poisson distribution with <code>lambda = 5</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

dataCopDis 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, discrete and endogenous.</dd>
  </dl>

Format

A dataset with an intercept, two exogenous regressors and one endogenous, discrete variable, used for exemplifying the use of <code><a rd-options='' href='copulaCorrection'>copulaCorrection</a></code> function. The true parameter values are: <code>b0 = 2</code>, <code>b1 = 1.5</code>, <code>b2 = -3</code>, and the coefficient of the endogenous variable is set to <code>a1 = -1</code>. The correlation between the endogenous regressor <code>P</code> and the error term is 0.33. <code>P</code> has a Poisson distribution with <code>lambda = 5</code>.


 — dataCopDis

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, discrete and endogenous.</dd>
  </dl>

dataCopDis:

Description

Usage

Arguments

Format