Thomas Plümper

Professor of Government, University of Essex

Director, Essex Summer School in Social Science Data Analysis

 

Curriculum Vitae

Publications

Working Papers

Statistical Software Components

Projects

Placements

ECPR SG Political Economy

Courses

Replication Data

 

 

 

 

 

 

 

 

 

 

 
 

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.