What is specification bias
Specification bias, also known as model specification bias, refers to a potential problem in statistical analysis where the chosen model for a regression or other predictive model does not accurately capture the true relationship between the independent and dependent variables. In other words, the model's specifications are incorrect, leading to biased estimates of the model parameters.
Specification bias can occur in various ways, such as:
1. Omitted variables bias: When important variables are excluded from the model, leading to biased estimates of the coefficients of the included variables. This can happen when relevant variables are not measured or unidentified.
2. Functional form misspecification: When the chosen functional form of the relationship between the variables is not correct, such as assuming a linear relationship when it is actually nonlinear. This can lead to biased coefficients and incorrect inferences.
3. Measurement error bias: When the measurement of the variables used in the model is subject to error or imprecision. This can result in biased parameter estimates and incorrect model specifications.
4. Endogeneity bias: When there is a bidirectional relationship between the dependent and independent variables, violating the assumption of exogeneity. This can lead to biased estimates and incorrect model specifications unless appropriate techniques like instrumental variables are used.
To mitigate specification bias, researchers should carefully consider the theoretical foundation of the relationship, collect accurate and reliable data, examine different functional forms, and include relevant variables in the model. Additionally, sensitivity analysis and diagnostic tests can help identify and address specification bias.