Abstract
In empirical analysis of data consisting of repeated
observations on economic units (time series on a cross
section) it is often assumed that the coefficients of the
quantitative variables (slopes) are the same, whereas the
coefficients of the qualitative variables (intercepts or
effects) vary over units or periods.This is the
constant-slope variable-intercept framework. In such an
analysis an explicit account should be taken of the
statistical dependence that exists between the quantitative
variables and the effects. It is shown that when this is
done, the random effect approach and the fixed effect
approach yield the same estimate for the slopes, the
"within" estimate. Any matrix combination of the
"within" and "between" estimates is generally
biased. When the "within" estimate is subject to a
relatively large error a minimum mean square error can be
applied, as is generally done in regression analysis. Such
an estimator is developed here from a somewhat different
point of departure.
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