Panel data are a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses. The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators. The purpose of this paper is to review dynamic panel data estimators that eliminate these problems. I first show how the problems with cross-sectional estimators arise in dynamic models for panel data. I then show how to correct for these problems using g...
It has become increasingly obvious that the estimation of dynamic panel data models has become one o...
Researchers face a tradeoff when applying latent variable models to time-series, cross-section*al da...
An exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non-...
Party identification has been studied extensively using both individual- and aggregate-level data. T...
The article discusses statistical inference in parametric models for panel data. The models feature ...
A computationally simple bias correction for linear dynamic panel data models is proposed and its as...
This article deals with a variety of dynamic issues in the analysis of time-series-cross-section (TS...
2015-06-18Dynamic panel models has very wide economic application in labor economics, health economi...
Panel data are repeated observations on the same cross section unit, typically of individuals or fir...
This paper proposes new moment estimators for autoregressive panels with cross-sectional dependence....
A major attraction of panel data is the ability to estimate dynamic models on an individual level. M...
textabstractAn important feature of panel data is that it allows the estimation of parameters charac...
Political scientists often argue that political processes move together in the long run. Examples in...
This paper analyzes the properties of the fixed-effects vector decomposition estimator, an emerging ...
This study develops a new bias-corrected estimator for the fixed-effects dynamic panel data model an...
It has become increasingly obvious that the estimation of dynamic panel data models has become one o...
Researchers face a tradeoff when applying latent variable models to time-series, cross-section*al da...
An exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non-...
Party identification has been studied extensively using both individual- and aggregate-level data. T...
The article discusses statistical inference in parametric models for panel data. The models feature ...
A computationally simple bias correction for linear dynamic panel data models is proposed and its as...
This article deals with a variety of dynamic issues in the analysis of time-series-cross-section (TS...
2015-06-18Dynamic panel models has very wide economic application in labor economics, health economi...
Panel data are repeated observations on the same cross section unit, typically of individuals or fir...
This paper proposes new moment estimators for autoregressive panels with cross-sectional dependence....
A major attraction of panel data is the ability to estimate dynamic models on an individual level. M...
textabstractAn important feature of panel data is that it allows the estimation of parameters charac...
Political scientists often argue that political processes move together in the long run. Examples in...
This paper analyzes the properties of the fixed-effects vector decomposition estimator, an emerging ...
This study develops a new bias-corrected estimator for the fixed-effects dynamic panel data model an...
It has become increasingly obvious that the estimation of dynamic panel data models has become one o...
Researchers face a tradeoff when applying latent variable models to time-series, cross-section*al da...
An exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non-...