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)
higher moments approach as well as Lewbel's (2012) heteroskedasticity approach, Park and Gupta's (2012) joint estimation method that uses Gaussian
copula and Kim and Frees's (2007) 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.

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> </dl> 

dataCopC2: Simulated Dataset

Description

Usage

Arguments

Format