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Thomas Plümper |
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Professor of Government, University of Essex |
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Director, Essex Summer School in Social Science Data Analysis |
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Fixed Effects Vector
Decomposition
We have updatet the xtfevd ado based on Stata's Mata technology. Point estimates are identical
to the previous version, but we changed the computation of the
variance-covariance matrix.
The ado does not deal with long variable names.
Note
that we do NOT follow the proposal of Breusch, Ward, Nguyen and Kompas (2010),
which gives
underconfident standard errors and is inconsistent with our procedure.
Note that we have not yet updated the hlp file.
xtfevd.ado
(version 4.0 beta)
xtfevd.hlp
Stata module to estimate fixed effects vector decomposition models
Efficient
Estimation of Time-Invariant and Rarely Changing Variables
in Finite Sample Panel Analyses with Unit Fixed Effects
(with Vera E. Troeger)
Political Analysis Vol. 15:2, 124-139.
This paper suggests a three-stage procedure for the estimation of time-invariant and rarely changing variables in panel data models with unit effects. The first stage of the proposed estimator runs a fixed-effects model to obtain the unit effects, the second stage breaks down the unit effects into a part explained by the time-invariant and/or rarely changing variables and an error term, and the third stage re-estimates the first stage by pooled OLS (with or without autocorrelation correction and with or without panel-corrected SEs) including the time-invariant variables plus the error term of stage 2, which then accounts for the unexplained part of the unit effects. We use Monte Carlo simulations to compare the finite sample properties of our estimator to the finite sample properties of competing estimators. In doing so, we demonstrate that our proposed technique provides the most reliable estimates under a wide variety of specifications common to real world data.
Spatial Lags
spmon -
Stata module to create spatial effect variable for
monadic data
spagg -
Stata module to create aggregate source or target
contagion spatial effect variable for directed dyadic data
spspc-
Stata module to create specific source or target
contagion spatial effect variable for directed dyadic data
spdir -
Stata module to create directed dyad contagion spatial
effect variable
Spatial Lags in Dyadic Data
(with Eric Neumayer)
International Organization, Vol. 64:1
Political units often spatially depend in their policy choices on other units.
This also holds in dyadic settings where, as in much of international
relations research, the focus of the analysis is the pair or dyad of two
political units. Yet, with few exceptions, social scientists have analyzed
contagion only in monadic datasets, consisting of individual political units.
This article categorizes all possible forms of modeling spatial lags in both
undirected and directed dyadic data. This enables scholars to formulate and
test novel mechanisms of contagion, thus ideally paving the way for studies
analyzing spatial dependence between dyads of political units. We illustrate
the modeling flexibility gained from an understanding of the full set of
specification options for spatial effects in dyadic data by an application to
the diffusion of bilateral investment treaties between developed and
developing countries, building and extending on Elkins et al. (2006, Competing
for Capital: The Diffusion of Bilateral Investment Treaties, 1960 2000.
International Organization 60: 811-846). We come to different conclusions
about the channels through which bilateral investment treaties diffuse. We
find that rather than a capital importing country being influenced by the
total number of BITs signed by other capital importers, as modeled in the
original article, a capital importing country is only more likely to sign a
BIT with a capital exporter if other competing capital importers have signed
BITs with this very same capital exporter. Similarly, other capital exporters'
BITs with a specific capital importer influence an exporter's incentive to
agree on a BIT with the very same capital importer.