2010
DOI: 10.1016/j.econlet.2009.11.007
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A simple estimator for the correlated random coefficient model

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Cited by 3 publications
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“…Failing to account for endogeneity may lead to biased parameter estimates, which undermines the validity of the findings obtained from regression-type analysis of observed data (Sande and Ghosh, 2018). There are a number of approaches used to treat endogeneity problems such as the control variables approach (Germann et al, 2015) and the control function approach (De Blander, 2010). We tested for endogeneity on the latent variable SCRE because it had both direct (BDAC, OMIN) and an indirect predictor (BDAC) using the instrumental variable approach.…”
Section: Structural Model Evaluationmentioning
confidence: 99%
“…Failing to account for endogeneity may lead to biased parameter estimates, which undermines the validity of the findings obtained from regression-type analysis of observed data (Sande and Ghosh, 2018). There are a number of approaches used to treat endogeneity problems such as the control variables approach (Germann et al, 2015) and the control function approach (De Blander, 2010). We tested for endogeneity on the latent variable SCRE because it had both direct (BDAC, OMIN) and an indirect predictor (BDAC) using the instrumental variable approach.…”
Section: Structural Model Evaluationmentioning
confidence: 99%