Specification Errors and Functional Form Misspecification A specification error arises when the economic model fails to conform to the true underlying e...
Specification Errors and Functional Form Misspecification
A specification error arises when the economic model fails to conform to the true underlying economic relationship between the variables. This can occur due to:
Measurement error: Using inaccurate or incomplete data to represent the variables.
Model misspecification: Choosing an inappropriate model that does not capture the essential relationships between the variables.
Omitted variable: Not including relevant variables that influence the dependent variable.
Multicollinearity: Multiple variables with high correlations that can cause spurious relationships.
Functional form misspecification occurs when the model fails to represent the true relationship between the variables in a non-linear fashion. This can lead to:
U-shaped relationships: A function that is increasing at first and then decreasing, instead of a constant or decreasing function.
Sign-changing relationships: A function that changes direction at some point, instead of being constant.
Non-monotonic relationships: A function that increases at first and then decreases, or vice versa.
Consequences of specification and functional form misspecification:
Inaccurate estimates: The model may not accurately predict the dependent variable, leading to biased and inefficient estimates.
Invalid inferences: The model may lead to incorrect conclusions about the economic relationships between the variables.
Difficulty in model interpretation: The model may be difficult to interpret, as the relationship between the variables may be obscured.
Examples:
Using incorrect data on consumer income and expenditure to estimate a model of consumer demand.
Choosing a linear model when there is a non-linear relationship between consumption and income.
Omitting relevant control variables, such as education and price level, leading to a specification error.
Having multiple highly correlated independent variables, which can create multicollinearity and spurious relationships