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This article deals with a variety of dynamic issues in the analysis of time-series-cross-section (TSCS) data. Although the issues raised are general, we focus on applications to comparative political economy which frequently uses TSCS data. We begin with a discussion of specification and lay out the theoretical differences implied by the various types of dynamic 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 ordinary least squares that is inconsistent in this situation There is a brief discussion of lagged dependent variables combined with fixed effects and issues related to non-stationarity. We then show bow our favored method of modeling dynamics combines nicely with methods for dealing with other TSCS issues, such a parameter heterogeneity and spatial dependence. We conclude with two examples. Adapted from the source document.

Misreporting is a problem that plagues researchers who use survey data. In this article, we develop a parametric model that corrects for misclassified binary responses using information on the misreporting patterns obtained from auxiliary data sources. The model is implemented within the Bayesian framework via Markov Chain Monte Carlo (MCMC) methods and can be easily extended to address other problems exhibited by survey data, such as missing response and/or covariate values. While the model is fully general, we illustrate its application in the context of estimating models of turnout using data from the American National Elections Studies.

This article considers random coefficient models (RCMs) for time-series—cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully pooled estimator, the unit by unit (unpooled) estimator, and the (maximum likelihood) RCM estimator. The maximum likelihood estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares estimator of the RCM models is always inferior to the maximum likelihood estimator, and in smaller samples dramatically so.

In this Top Twenty Commentaries section of the Centennial Issues of the journal, Nathaniel Beck & Jonathan N. Katz review their highly cited article What to Do (and Not to Do) with Time-Series Cross-Section Data(1995). The author's assert that their article contributed to the methodology for analyzing the principal type of data used in the study of quantitative data that is now being used in comparative politics & international relations. The analysis of Time Series Cross Section (TSCS) data as used by Cameron & Hibbs is argued to allow for rigorous analysis of both temporal & spatial dimensions of the data. The importance of these issues to political science research, & the growing interest in finite sample properties of estimators are related to the superior method of least squares combined with correction for standard errors over the use of an asymptotically superior estimator. The initial refusal of the article by the Review, & the current methodological debates are concluded to not increase concern that they author's missed many important issues in their seminal article. References. J. Harwell

Donald P. Green, Soo Yeon Kim, and David H. Yoon argue that many findings in quantitative international relations that use the dyad-year design are flawed. In particular, they argue that the effect of democracy on both trade and conflict has been vastly overstated, that researchers have ignored unobserved heterogeneity between the various dyads, and that heterogeneity can be best modeled by "fixed effects," that is, a model that includes a separate dummy for each dyad.We argue that the use of fixed effects is almost always a bad idea for dyad-year data with a binary dependent variable like conflict. This is because conflict is a rare event, and the inclusion of fixed effects requires us to not analyze dyads that never conflict. Thus while the 90 percent of dyads that never conflict are more likely to be democratic, the use of fixed effects gives democracy no credit for the lack of conflict in these dyads. Green, Kim, and Yoon's fixed-effects logit can tell us little, if anything, about the pacific effects of democracy.Their analysis of the impact of democracy on trade is also flawed. The inclusion of fixed effects almost always masks the impact of slowly changing independent variables; the democracy score is such a variable. Thus it is no surprise that the inclusion of dyadic dummy variables in their model completely masks the relationship between democracy and trade. We show that their preferred fixed-effects specification does not outperform a model with no effects (when that model is correctly specified in other ways). Thus there is no need to include the masking fixed effects, and so Green, Kim, and Yoon's findings do not overturn previous work that found that democracy enhanced trade.We agree with Green, Kim, and Yoon that modeling heterogeneity in time-series cross-section data is important. We mention a number of alternatives to their fixed-effects approach, none of which would have the pernicious consequences of using dyadic dummies in their two reanalyses.

We propose a comprehensive statistical model for analyzing multiparty, district-level elections. This model, which provides a tool for comparative politics research analogous to that which regression analysis provides in the American two-party context, can be used to explain or predict how geographic distributions of electoral results depend upon economic conditions, neighborhood ethnic compositions, campaign spending, and other features of the election campaign or aggregate areas. We also provide new graphical representations for data exploration, model evaluation, and substantive interpretation. We illustrate the use of this model by attempting to resolve a controversy over the size of and trend in the electoral advantage of incumbency in Britain. Contrary to previous analyses, all based on measures now known to be biased, we demonstrate that the advantage is small but meaningful, varies substantially across the parties, and is not growing. Finally, we show how to estimate the party from which each party's advantage is predominantly drawn.