Specification (regression)

In regression analysis specification is the process of developing a regression model. This process consists of selecting an appropriate functional form for the model and choosing which variables to include. For instance, one may specify the functional relationship y = f(s,x) between personal income y and human capital in terms of schooling s and on-the-job experience x as:[1]


\ln y = \ln y_0 + \rho s + \beta_1 x + \beta_2 x^2 + \varepsilon

where \varepsilon is the unexplained error term that is supposed to be independent and identically distributed. If assumptions of the regression model are correct, the least squares estimates of the parameters \rho and \beta will be efficient and unbiased. Hence specification diagnostics usually involve testing the first to fourth moment of the residuals.[2]

Specification error and bias

Specification error occurs when an independent variable is correlated with the error term. There are several different causes of specification error:

Detection

The Ramsey RESET test can help test for specification error.

See also

References

  1. This particular example is known as Mincer earnings function.
  2. Long, J. Scott; Trivedi, Pravin K. (1993). "Some Specification Tests for the Linear Regression Model". In Bollen, Kenneth A.; Long, J. Scott. Testing Structural Equation Models. London: Sage. pp. 66–110. ISBN 0-8039-4506-X.
  3. Untitled

Further reading

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