Endogeneity refers to a situation in statistical models where an explanatory variable is correlated with the error term, leading to biased and inconsistent parameter estimates. This issue often arises due to omitted variable bias, measurement error, or simultaneous causality, and can be addressed using methods like instrumental variables or fixed effects models.

Endogeneity

Statistical models are mathematical representations of observed data that help in understanding and predicting the underlying processes generating the data. They are essential tools in data analysis, allowing researchers to quantify relationships, test hypotheses, and make informed decisions based on empirical evidence.

statistical models

explanatory variable

Correlated error term

Biased parameter estimates

inconsistent parameter estimates

Omitted Variable Bias occurs when a model leaves out one or more relevant variables, leading to biased and inconsistent parameter estimates. This bias arises because the omitted variable is correlated with both the dependent variable and one or more included independent variables, distorting the true relationship being studied.

omitted variable bias

Measurement error refers to the difference between the true value and the observed value due to inaccuracies in data collection, which can lead to biased results and incorrect conclusions. Understanding and minimizing measurement error is crucial for ensuring the validity and reliability of research findings.

measurement error

simultaneous causality

Instrumental variables are used in statistical analysis to estimate causal relationships when controlled experiments are not feasible, addressing the issue of endogeneity by providing a source of variation that is correlated with the explanatory variable but uncorrelated with the error term. This method helps to isolate the causal impact of a variable by using a third variable, the instrument, which allows for consistent estimation of the parameter of interest.

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