The document discusses model specification error, noting that the initial model may be overspecified by including too many variables, underspecified by omitting important variables, or specify the wrong mathematical relationships. Correct specification means the model includes all core variables, excludes irrelevant ones, uses the right functional form, and has no errors in variables or incorrectly specified error terms. Reasons for errors include omitting relevant variables, including unnecessary ones, adopting the wrong functional form, or having errors of measurement.

1. Model specification error
We usually attempt to build a “correct” model starting with the initial one. The
initial model may not be optimal for different reason. First , the may contain
many variables and is said to be overspecified. Second the model may not
contain the right variables and is said to be as underspecified. Third we may
not have the correct mathmaical relationship

2. Model specification error
• CLRM assumes that our model is correctly specified. By correct specification we mean one or
more o the following
The model does not exclude any “core” variables.
The model does not include any “irrelevant” variables.
The functional form of the model is suitably chosen
There are no error of measurement in the indeoendent and dependent variable.
The probability distribution of error term is well specified.
What happens if the regressors are stochastic.

3. Reasons
Omission of a relavent variable
Inclusion of unnesseray variables
Adopting a wrong functional form
Errors of measurments