Chapter 14
Introduction to panel data
Overview
Increasingly, researchers are now using panel data where possible in preference to cross-sectional data.
One
major reason is that dynamics may be explored with panel data in a way that is seldom possible with cross-
sectional data.
Another is that panel data offer the possibility of a solution to the pervasive problem of omitted
variable bias.
A further reason is that panel data sets often contain very large numbers of observations and the
quality of the data is high.
This chapter describes
Learning outcomes
After working through the corresponding chapter in the text, studying the corresponding slideshows, and doing
the starred exercises in the text and the additional exercises in this guide, you should be able to:
explain the differences between panel data, cross-sectional data, and time series data
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explain what the benefits that can be obtained using panel data
explain the differences between OLS pooled regressions, fixed effects regressions, and random effects
regressions
explain the potential advantages of the fixed effects model over pooled OLS
explain the differences between the within-groups, first differences, and least squares dummy variables
variants of the fixed effects model
explain the assumptions required for the use of the random effects model
explain the advantages of the random effects model over the fixed effects model when the assumptions are
valid
explain how to use a Durbin–Wu–Hausman test to determine whether the random effects model may be used
instead of the fixed effects model
Additional exercises
A14.1 The
NLSY2000
data set contains the following data for a sample of 2,427 males and 2,392 females for the
years 1980–2000: weight in pounds, years of schooling, age, marital status in the form of a dummy
variable
MARRIED
defined to be 1 if the respondent was married, 0 if single, and height in inches.
Hypothesizing that weight is influenced by schooling, age, marital status, and height, the following
regressions were performed for males and females separately:
(1)
an ordinary least squares (OLS) regression pooling the observations
(2)
a within-groups fixed effects regression
(3)
a random effects regression
The results of these regressions are shown in the table.
Standard errors are given in parentheses.
October 2007

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