What are the consequences of model specification error?

What are the consequences of model specification error?

In the context of a statistical model, specification error means that at least one of the key features or assumptions of the model is incorrect. In consequence, estimation of the model may yield results that are incorrect or misleading.

What are the common reasons for incorrect specification of the econometric models?

Since econometrics does not content itself with only making optimal predictions, but also aspires to explain things in terms of causes and effects, econometricians need loads of assumptions — most important of these are additivity and linearity.

What are the types of specification errors?

Inclusion of an unnecessary variable(s) Adopting the wrong functional form. Errors of measurement. Incorrect specification of the stochastic error term.

What is a regression specification?

Model specification refers to the determination of which independent variables should be included in or excluded from a regression equation. In general, the specification of a regression model should be based primarily on theoretical considerations rather than empirical or methodological ones.

What are the sources of specification error?

In this paper we generalize their test to cover all four common sources of errors in specification: functional form, autocorrelated disturbances, heteroscedasticity, and missing variables. The Savin-White test, as well as the standard tests for only one source of error, are special cases of the test developed.

What happens when you formulate a regression model?

There are four possible outcomes when formulating a regression model for a set of data: The regression model is ” correctly specified .” The regression model is ” underspecified .” The regression model contains one or more ” extraneous variables .” The regression model is ” overspecified .”

Which is a specification bias in a regression model?

In relation to the true model, (13.2.6) would also constitute a specification bias, the bias here being the use of the wrong functional form: In (13.2.1) Y appears linearly, whereas in (13.2.6) it appears log-linearly. Finally, consider the researcher who uses the following model:

When to use an overspecified regression model?

Regression models that are overspecified yield unbiased regression coefficients, unbiased predictions of the response, and an unbiased SSE. Such a regression model can be used, with caution, for prediction of the response, but should not be used to describe the effect of a predictor on the response.

What happens if the regression equation contains ” wrong “?

If the regression model is overspecified, then the regression equation contains one or more redundant predictor variables. That is, part of the model is correct, but we have gone overboard by adding predictors that are redundant. Redundant predictors lead to problems such as inflated standard errors for the regression coefficients.