Title:      Modeling Dynamics in Time-Series-Cross-Section
Political Economy Data

Authors:    Nathaniel Beck, Jonathan Katz

Entrydate:  2009-06-04 13:15:19

Keywords:   dynamics, TSCS, political economy, lagged dependent
variable, non-stationary

Abstract:   This paper deals with a variety of dynamic issues in
the analysis of time-series-cross-section (TSCS) data. While the
issues raised are more general, we focus on applications to
political economy. We begin with a discussion of specification
and lay out the theoretical differences implied by the various
types of time series models that can be estimated. It is shown
that there is nothing pernicious in using a lagged dependent
variable and that all dynamic models either implicitly or
explicitly have such a variable; the differences between the
models relate to assumptions about the speeds of adjustment of
measured and unmeasured variables. When adjustment is quick it
is hard to differentiate between the various models; with slower
speeds of adjustment the various models make sufficiently
different predictions that they can be tested against each
other. As the speed of adjustment gets slower and slower,
specification (and estimation) gets more and more tricky. We
then turn to a discussion of estimation. It is noted that models
with both a lagged dependent variable and serially correlated
errors can easily be estimated; it is only OLS that is
inconsistent in this situation. We then show, via Monte Carlo
analysis shows that for typical TSCS data that fixed effects
with a lagged dependent variable performs about as well as the
much more complicated Kiviet estimator, and better than the
Anderson-Hsiao estimator (both designed for panels).

http://polmeth.wustl.edu/retrieve.php?id=906

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