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Time-Series--Cross-Section Issues: Dynamics, 2004 Beck, Nathaniel Katz, Jonathan Uploaded 07-24-2004 Keywords Time-series--cross-section data lagged dependent variables Nickell bias specification integration Abstract This paper deals with a variety of dynamic issues in the analysis of time-series--cross-section (TSCS) data raised by recent papers; it also more briefly treats some cross-sectional issues. Monte Carlo analysis shows that for typical TSCS data that fixed effects with a lagged dependent variable performs about as well as the much more complicated Kiviet estimator, and better than the Anderson-Hsiao estimator (both designed for panels). It is also shown that there is nothing pernicious in using a lagged dependent variable, and 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 models, and analysts may choose on grounds of convenience (assuming that the model passes standard econometric tests). When adjustment is slow it may be the case that the data are integrated, which means that no method developed for the stationary case is appropriate. At the cross-sectional level, it is argued that the critical issue is assessing heterogeneity; a variety of strategies for this assessment are discussed.

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Lagging the Dog?: The Robustness of Panel Corrected Standard Errors in the Presence of Serial Correlation and Observation Specific Effects Kristensen, Ida Wawro, Gregory Uploaded 07-13-2003 Keywords time-series cross-section data serial correlation fixed effects panel data lag models Monte Carlo experiments Abstract This paper examines the performance of the method of panel corrected standard errors (PCSEs) for time-series cross-section data when a lag of the dependent variable is included as a regressor. The lag specification can be problematic if observation-specific effects are not properly accounted for, leading to biased and inconsistent estimates of coefficients and standard errors. We conduct Monte Carlo studies to assess how problematic the lag specification is, and find that, although the method of PCSEs is robust when there is little to no correlation between unit effects and explanatory variables, the method's performance declines as that correlation increases. A fixed effects estimator with robust standard errors appears to do better in these situations.

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Paper

Space Is more than Geography Beck, Nathaniel Gleditsch, Kristian Uploaded 07-11-2003 Keywords spatial econometrics time-series--cross-section data Abstract Most spatial models use some measure of distance in the spatial weighting matrix. But this is not required: any measure of "similarity" that has the mathematical properties of distance will work well. Here we use spatial methods to allow for dyads which share a common partner to be similar (and a directed dyad and its reverse to be especially similar). While we find evidence of spatial effects in a model with a spatially lagged error, we note that the substantive conseequences of taking this into account are not great. We then use various measures of "community" to assess the impact of similarity in models of democracy and development; the three similarity measures are physical distance, cultural (religious) similarity and trade. In a simple cross-sectional model the spatial lag has large consequences; however, when we move to time-series--cross-section data the impact of the spatial lag is very small. We also argue that one can simplify estimation in many time-series--cross-sectional data sets with temporally independent errors by using the first temporal lag of the spatial lag, which makes for simple estimation.

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Modelling Space and Time: The Event History Approach Beck, Nathaniel Uploaded 08-22-1996 Keywords duration analysis event history analysis time-series--cross-section data discrete duration data duration dependence Abstract This is an elementary exposition of duration modelling prepared for a volume in celebration of the 30th anniversary of the Essex Summer School (Research Strategies in the Social Sciences, Elinor Scarbrough and Eric Tanenbaum, editors). The approach is non-mathematical. The running example used is the King et al. model of cabinet durations with particular attention paid to detecting and interpreting duration dependence in that model. There is some new discussion of ascertaining duration dependence using discrete methods and the relationship between discrete duration data and binary time-series--cross-section data.

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Beyond Ordinary Logit: Taking Time Seriously in Binary Time-Series--Cross-Section Models Beck, Nathaniel Katz, Jonathan Tucker, Richard Uploaded 08-22-1997 Keywords binary time-series--cross-section data logit/probit temporal dependence grouped duration models complementary log-log cubic spline economic interdependence democratic peace war Abstract Researchers typically analyze time-series--cross-section data with a binary dependent variable (BTSCS) using ordinary logit or probit. However, BTSCS observations are likely to violate the independence assumption of the ordinary logit or probit statistical model. It is well known that if the observations are temporally related that the results of an ordinary logit or probit analysis may be misleading. In this paper, we provide a simple diagnostic for temporal dependence and a simple remedy. Our remedy is based on the idea that BTSCS data is identical to grouped duration data. This remedy does not require the BTSCS analyst to acquire any further methodological skills and it can be easily implemented in any standard statistical software package. While our approach is suitable for any type of BTSCS data, we provide examples and applications from the field of International Relations, where BTSCS data is frequently used. We use our methodology to re-assess Oneal and Russett's (1997) findings regarding the relationship between economic interdependence, democracy, and peace. Our analyses show that 1) their finding that economic interdependence is associated with peace is an artifact of their failure to account for temporal dependence and 2) their finding that democracy inhibits conflict is upheld even taking duration dependence into account.

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Paper

The Analysis of Binary Time-Series--Cross-Section Data and/or The Democratic Peace Beck, Nathaniel Katz, Jonathan Uploaded 07-18-1997 Keywords binary dependent variable time-series--cross-section data serially correlated errors event history analysis Abstract The analysis of binary time-series--cross-section (BTSCS) data almost invariably ignores temporal dependence. Using Monte Carlo we show that ordinary probit standard errors underestimate variability in the presence of serially correlated errors. This underestimate, while severe, is smaller than in a corresponding OLS analysis. The simulations show that the standard errors can be partially corrected using Huber's method. We then discuss a variety of other methods for allowing temporal dependency in BTSCS estimation. The simulations show that using a lagged dependent variable will not be the panacea that it is for continuous data. We briefly examine the ``general estimating equation approach.'' We then note the equivalence of BTSCS and event history data, and thus show that common event history techniques which allow for ``duration dependence'' can be used for temporally dependent BTSCS data. The methods are used to re-analyze Oneal and Russett's study of the role of democracy and trade in facilitating peace. After correcting for temporal dependence we still find support for the democratic peace hypothesis but no longer find support for the liberal trade hypothesis.

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Paper

Spatio-Temporal Models for Political-Science Panel and Time-Series-Cross-Section Data Franzese, Robert Hays, Jude Uploaded 07-18-2006 Keywords Spatial Econometrics Spatial-Lag Model Spatio-Temporal Model Panel Data Time-Series-Cross-Section Data Spatio-Temporal Multiplier Spatio-Temporal Dynamics Spatio-Temporal Steady-State Effects Abstract Building from our broader project exploring spatial-econometric models for political science, this paper discusses estimation, interpretation, and presentation of spatio-temporal models. We first present a generic spatio-temporal-lag model and two methods, OLS and ML, for estimating parameters in such models. We briefly consider those estimators’ properties analytically before showing next how to calculate and to present the spatio-temporal dynamic and long-run steady-state equilibrium effects—i.e., the spatio-temporal substance of the model—implied by the coefficient estimates. Then, we conduct Monte Carlo experiments to explore the properties of the OLS and ML estimators, and, finally, we conclude with a reanalysis of Beck, Gleditsch, and Beardsley’s (2006) state-of-the-art study of directed export flows among major powers.

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Sweeping fewer things under the rug: tis often (usually?) better to model than be robust Beck, Nathaniel Uploaded 07-16-2012 Keywords Cluster Robust Standard Errors Moulton Problem Time Series Cross Section Data Difference in Difference Random Effects Abstract The use of ``robust'' standard errors is now commonplace in political science. This paper considers one such type of errors, those that are robust to clustering of the data. While these give accurate estimates of parameter variability, we often can do better by direct modeling of the clustering process; such modeling can give insight into important sources of cluster effects. Applications are to grouped data with group level variables, difference in difference designs and time-series--cross-section data. Analysts should always ask whether clustering can be no more than an estimation nuisance before simply resorting to cluster robust standard errors.