In discussing
the advantages of panel data over pure cross-sectional data or pure time
series data,
Baltagi lists the following factors[1]:
1 Since panel data
deals with individuals, firms, states, countries and so on over time, there is
bound to be heterogeneity in these units, which may be often unobservable.
The panel data estimation techniques can take such heterogeneity explicitly
into account by allowing for subject-specific variables, as we shall
show shortly. We use the term subject generically to include microunits
such individuals, firms or states.
2 By combining
time series of cross-sectional observations, panel data gives “more informative
data, more variability, less collinearity among variables, more degrees of
freedom and more efficiency”.
3 By studying the
repeated cross-sections of observations, panel data are better suited to study
the dynamics of change. Spells of unemployment, job turnover, duration of
unemployment, and labor mobility are better studied with panel data.
4 Panel data can
better detect and measure effects that cannot be observed in pure cross-sectional
or time series data. Thus the effects of minimum wage laws on employment and
earnings can be better studied if we follow successive waves of increases in
federal and/or state minimum wages.
5 Phenomena such
as economies of scale and technological change can be better studied by panel
data than by pure cross-sectional or pure time series data.
[1] Badi H. Baltagi, Econometric
Analysis of Panel Data, John Wiley & Sons, New York, 1995, pp. 3–6.
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