Dramatic world change has stimulated interest in research questions about the dynamics of politics. We have seen increases in the number of time series data sets and the length of typical time series. But three shortcomings are prevalent in published time series analysis. First, analysts often estimate models without testing restrictions implied by their specification. Second, researchers link the theoretical concept of equilibrium with cointegration and error correction models. Third, analysts often do a poor job of interpreting results. The consequences include weak connections between theory and tests, biased estimates, and incorrect inferences. We outline techniques for estimating linear dynamic regressions with weakly exogenous regress...
A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effec...
Panel data are a very valuable resource for finding empirical solutions to political science puzzles...
Data and code to replicate findings from "How to Make Causal Inferences with Time-Series Cross-Secti...
This article deals with a variety of dynamic issues in the analysis of time-series-cross-section (TS...
This paper deals with a variety of dynamic issues in the analysis of time-series–cross-section (TSCS...
Political scientists often argue that political processes move together in the long run. Examples in...
This paper deals with a variety of dynamic issues in the analysis of time-series– cross-section (TSC...
Researchers face a tradeoff when applying latent variable models to time-series, cross-section*al da...
Time-varying relationships and volatility are two methodological challenges that are particular to t...
While traditionally considered for non-stationary and cointegrated data, De Boef and Keele (2008) su...
Testing theories about political change requires analysts to make assumptions about the memory of th...
This paper deals with a variety of dynamic issues in the analysis of time- series–cross-section (TSC...
Duration analyses in political science often model non-proportional hazards through interactions wit...
Political relationships often vary over time, but standard models ignore temporal variation in regre...
Provides tools for the critical appraisal of empirical evidence in time-series econometrics as well ...
A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effec...
Panel data are a very valuable resource for finding empirical solutions to political science puzzles...
Data and code to replicate findings from "How to Make Causal Inferences with Time-Series Cross-Secti...
This article deals with a variety of dynamic issues in the analysis of time-series-cross-section (TS...
This paper deals with a variety of dynamic issues in the analysis of time-series–cross-section (TSCS...
Political scientists often argue that political processes move together in the long run. Examples in...
This paper deals with a variety of dynamic issues in the analysis of time-series– cross-section (TSC...
Researchers face a tradeoff when applying latent variable models to time-series, cross-section*al da...
Time-varying relationships and volatility are two methodological challenges that are particular to t...
While traditionally considered for non-stationary and cointegrated data, De Boef and Keele (2008) su...
Testing theories about political change requires analysts to make assumptions about the memory of th...
This paper deals with a variety of dynamic issues in the analysis of time- series–cross-section (TSC...
Duration analyses in political science often model non-proportional hazards through interactions wit...
Political relationships often vary over time, but standard models ignore temporal variation in regre...
Provides tools for the critical appraisal of empirical evidence in time-series econometrics as well ...
A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effec...
Panel data are a very valuable resource for finding empirical solutions to political science puzzles...
Data and code to replicate findings from "How to Make Causal Inferences with Time-Series Cross-Secti...