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Below results based on the criteria 'TSCS'
Total number of records returned: 3
Problems with and Solutions for Two-dimensional Models of Continuous Dependent Variables
This paper addresses hierarchical models with continuous dependent variables, such as time-series-cross-section models. Building on the argument in Zorn (2001), the main point of this paper is that the pooled OLS estimator is deeply flawed – especially for time-series-cross-section data – but for reasons that have not explicitly been raised in previous papers. The pooled OLS estimator, the within-estimator, the between-estimator, and the random effects estimator can be seen as special cases of the fractionally pooled estimator presented in Bartels (1996), which allows all of these estimators to be evaluated in a common framework. Taking bias and efficiency into account, using both the within-estimator and the between-estimator is likely to be the best estimation strategy for the vast majority of applications in political science.
Modeling Dynamics in Time-Series-Cross-Section Political Economy Data
lagged dependent variable
This paper deals with a variety of dynamic issues in the analysis of time-series-cross-section (TSCS) data. While the issues raised are more general, we focus on applications to political economy. We begin with a discussion of specification and lay out the theoretical differences implied by the various types of time series 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 OLS that is inconsistent in this situation. We then show, via 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).
Should I Use Fixed or Random Effects?
Empirical analyses in political science very commonly confront data that are grouped---multiple votes by individual legislators, multiple years in individual states, multiple conflicts during individual years, and so forth. Modeling these data presents a series of potential challenges, of which accounting for differences across the groups is perhaps the most well-known. Two widely-used methods are the use of either "fixed" or "random" effects models. However, how best to choose between these approaches remains unclear in the applied literature. We employ a series of simulation experiments to evaluate the relative performance of fixed and random effects estimators for varying types of datasets. We further investigate the commonly-used Hausman test, and demonstrate that it is neither a necessary nor sufficient statistic for deciding between fixed and random effects. We summarize the results into a typology of datasets to offer practical guidance to the applied researcher.