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=1.1" 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)
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

dataCopDis function

A data frame with 2500 observations on the following 5 variables.
  <dl> </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: Simulated Dataset

Description

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