1144 The Methods of Growth Econometrics
time series regressions for each country. The use of panel data is likely to increase
efficiency and allow richer models to be estimated, but at the expense of poten-
tially serious biases if the parameter homogeneity assumptions are incorrect. This
trade-off between robustness and efficiency is another running theme of our sur-
vey. The scientific solution would be to base the choice of estimation method on
a context-specific loss function, but this is clearly a difficult task, and in practice
more subjective decisions are involved.
Section 24.5.1 examines the econometric issues that arise in the use of time
series data to study growth, emphasizing some of the drawbacks of this approach.
Section 24.5.2 discusses the many issues that arise when panel data are employed,
an increasingly popular approach to growth questions. We consider the estimation
of dynamic models in the presence of fixed effects, and alternatives to standard pro-
cedures. Section 24.5.3 describes another increasingly popular approach, namely
the use of “event studies” to analyze growth behavior, based on studying responses
to major events such as political reform or changes in trade policy.
24.5.1 Time series approaches
At first glance, the most natural way to understand growth would be to examine
time series data for each country in isolation. In practice, however, a time series
approach runs into substantial difficulties. One key constraint is the available data.
For many developing countries, some of the most important data are only available
on an annual basis, with limited coverage before the 1960s. Moreover, the listing
of annual data in widely used sources and online databases can be misleading.
For example, population figures are often based primarily on census data, while
measures of average educational attainment are often constructed by interpolating
between census observations using school enrollments. The true extent of infor-
mation in the time series variation may be less than appears at first glance, and
conventional standard errors will be misleading.
Some key growth determinants display relatively little time variation. Even where
a variable appears to show significant variation, this may not correspond to the con-
cept the researcher originally had in mind. An example would be political stability.
Since Barro (1991), researchers have sometimes used the incidence of political rev-
olutions and coups as a measure of political instability. The interpretation of such
an index clearly varies depending on the length of the time period used to con-
struct it. If the hypothesis of interest relates to underlying political uncertainty
(say, theex anteprobability of a transfer of power) then time series observations on
political instability would need to be averaged over a long time period. The varia-
tion in political instability at shorter horizons casts light on a different hypothesis,
namely the direct impact of revolutions and coups, rather than on the effects ofex
antepolitical uncertainty.
There are other significant problems with the time series approach. The hypothe-
ses of most interest to growth theorists are mainly about the evolution of potential
output, not deviations from potential output, whether business cycles or larger-
scale output collapses. Since measured output is a noisy indicator of potential
output, it is easy for the econometric modeling of a growth process to be