dataCopDisCont

A dataset with two exogenous regressors,
 <code>X1</code>,<code>X2</code>, and two endogenous regressors,
 <code>P1</code>, having a Poisson distribution with lambda parameter equal to 3, and <code>P2</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 <code>P1</code> is set to <code>a1 = -1</code> and of <code>P2</code> is set to <code>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> heteroscedasticity 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.
See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>.
Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.

Raluca Gui

REndo

Fitting Linear Models with Endogenous Regressors using Latent
Instrumental Variables

Markus Meierer

Rene Algesheimer

Patrik Schilter

dataCopDisCont function

A data frame with 2500 observations on 5 variables:<dl>
<dt><code>y</code></dt>
<dd>a numeric vector representing the dependent variable.</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 Poisson distribution with parameter lambda equal to 3.</dd><dt><code>P2</code></dt>
<dd>a numeric vector, continuous and endogenous having T-distribution with 3 degrees of freedom.</dd>
</dl>

Format

Raluca Gui <a href="/link/raluca.gui%40business.uzh.ch?package=REndo&version=2.4.10" data-mini-rdoc="REndo::raluca.gui@business.uzh.ch">raluca.gui@business.uzh.ch</a>

Author

Simulated Dataset with Two Endogenous Regressors — dataCopDisCont

A data frame with 2500 observations on 5 variables:<dl>
<dt><code>y</code></dt>
<dd>a numeric vector representing the dependent variable.</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 Poisson distribution with parameter lambda equal to 3.</dd>

<dt><code>P2</code></dt>
<dd>a numeric vector, continuous and endogenous having T-distribution with 3 degrees of freedom.</dd>


</dl>

Raluca Gui <a href='mailto:raluca.gui@business.uzh.ch'>raluca.gui@business.uzh.ch</a>

dataCopDisCont: Simulated Dataset with Two Endogenous Regressors

Description

Usage

Arguments

Format

Author