# 6.) Specification: Choosing the Independent Variables Flashcards

Specifying an econometric equation consists of three parts…

- ) Choosing the correct independent variables
- ) Choosing the correct functional form
- ) Choosing the correct form of the stochastic error term

A specification error results when…

any one of three parts of specifying an econometric equation made incorrectly

The strength of researchers being able to pick the independent variables for regression equations is…

the equations can be formulated to fit individual needs

The weakness of researchers being able to pick which independent variables to include is…

that researchers can estimate many different specifications until they find the one that “proves” their point

The primary consideration in deciding whether an independent variable belongs in an equation is whether

the variable is essential the regression on the basis of theory

The major consequence of omitting a relevant independent variable from..

an equation is to cause bias in the regression coefficients that remain in the equation

Omitted Variable is defined as..

an important explanatory variable that has been left out of a regression equation

The bias caused by leaving a variable out of an is called…

omitted variable bias

In an equation with more than one independent variable, the coefficient beta K represents…

the change I the dependent variable Y caused by a one-unit increase in the independent variable Xk, holding constant the other independent variables in the equation

If a variable is omitted, then …

it is not included as an independent variable, and it is not held constant for the calculation and interpretation of beta hat K.

The major consequence of omitting a relevant independent variable from an equation is…

to cause bias in the regression coefficients that remain in the equation

The term Bomf(rin,om) is …

the amount of specification bias introduced into the estimate of the coefficient of the included variable by leaving out the omitted variable.

When a relevant variable is omitted than r squared barred is likely to…

drop

What two factors are key to understand if a relevant variable is left out of a regression equation?

- ) There is no longer an estimate of the coefficient of that variable in the equation.
- ) The coefficients of the remaining variables are likely to be biased.

Reasons that including omitted variable is easier said than done

- ) Omitted variable bias is hard to detect
- ) The problem of choosing which variable to add to an equation once you decide that it is suffering from omitted variable bias.