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Below results based on the criteria 'Time-Series Cross-Sectional Data'
Total number of records returned: 5
Beyond "Fixed Versus Random Effects": A Framework for Improving Substantive and Statistical Analysis of Panel, TSCS, and Multilevel Data
time-series cross-sectional data
Researchers analyzing panel, time-series cross-sectional, and multilevel data often choose between a random effects, fixed effects, or complete pooling modeling approach. While pros and cons exist for each approach, I contend that some core issues concerning clustered data continue to be ignored. I present a unified and simple modeling framework for analyzing clustered data that solves many of the substantive and statistical problems inherent in extant approaches. The approach: (1) solves the substantive interpretation problems associated with cluster confounding, which occurs when one assumes that within- and between-cluster effects are equal; (2) accounts for cluster-level unobserved heterogeneity via a random intercept model; (3) satisfies the controversial statistical assumption that level-1 variables be uncorrelated with the random effects term; (4) allows for the inclusion of level-2 variables; and (5) allows for statistical tests of cluster confounding. I illustrate this approach using three substantive examples: global human rights abuse, oil production for OPEC countries, and Senate voting on Supreme Court nominations. Reexaminations of these data produce refined interpretations of some of the core substantive conclusions.
Explaining Fixed Effects: Random Effects modelling of Time-Series Cross-Sectional and Panel Data
Random Effects models
Fixed Effects models
Random coefficient models
Fixed effects vector decomposition
Time-Series Cross-Sectional Data
This article challenges Fixed Effects (FE) modelling as the ‘default’ for time-series-cross-sectional and panel data. Understanding differences between within- and between-effects is crucial when choosing modelling strategies. The downside of Random Effects (RE) modelling – correlated lower-level covariates and higher-level residuals – is omitted-variable bias, solvable with Mundlak’s (1978a) formulation. Consequently, RE can provide everything FE promises and more, and this is confirmed by Monte-Carlo simulations, which additionally show problems with another alternative, Plümper and Troeger’s Fixed Effects Vector Decomposition method, when data are unbalanced. As well as being able to model time-invariant variables, RE is readily extendable, with random coefficients, cross-level interactions, and complex variance functions. An empirical example shows that disregarding these extensions can produce misleading results. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context and heterogeneity, modelled using RE. The implications extend beyond political science, to all multilevel datasets.
GEE Models of Judicial Behavior
generalized estimating equations
time-series cross-sectional data
judicial decision making
The assumption of independent observations in judicial decision making flies in the face of our theoretical understanding of the topic. In particular, two characteristics of judicial decision making on collegial courts introduce heterogeneity into successive decisions: individual variation in the extent to which different jurists maintain consistency in their voting behavior over time, and the ability of one judge or justice to influence another in their decisions. This paper addresses these issues by framing judicial behavior in a time-series cross-section context and using the recently developed technique of generalized estimating equations (GEE) to estimate models of that behavior. Because the GEE approach allows for flexible estimation of the conditional correlation matrix within cross-sectional observations, it permits the researcher to explicitly model interjustice influence or over-time dependence in judicial decisions. I utilize this approach to examine two issues in judicial decision making: latent interjustice influence in civil rights and liberties cases during the Burger Court, and temporal consistency in Supreme Court voting in habeas corpus decisions in the postwar era. GEE estimators are shown to be comparable to more conventional pooled and TSCS techniques in estimating variable effects, but have the additional benefit of providing empirical estimates of time- and panel- based heterogeneity in judicial behavior.
Causal Inference of Repeated Observations: A Synthesis of the Propensity Score Methods and Multilevel Modeling
The fundamental problem of causal inference is that an individual cannot be simultaneously observed in both the treatment and control states (Holland 1986). The propensity score methods that compare the treatment and control groups by discarding the unmatched units are now widely used to deal with this problem. In some situations, however, it is possible to observe the same individual or unit of observation in the treatment and control states at different points in time. The data has the structure that is often refer to as time-series-cross-sectional (TSCS) data. While multilevel modeling is often applied to analyze TSCS data, this paper proposes that synthesizing the propensity score methods and multilevel modeling is preferable. The paper conducts a Monte Carlo simulation with 36 different scenarios to test the performance of the two combined methods. The result shows that synthesizing the propensity score matching with multilevel modeling performs better in that such method yields less biased and more efficient estimates. An empirical case study that reexamine the model of Przeworksi et al (2000) on democratization and development also shows the advantage of this synthesis.
Generalized Synthetic Control Method for Causal Inference in Time-Series Cross-Sectional Data
synthetic control method
time-series cross-sectional data
Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, I propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond and Hainmueller 2010) with linear fixed effect models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit in post-treatment periods using control group information based on a linear interactive fixed effect model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modelling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is transparent and easy to implement. Monte Carlo results show that this method performs well with small numbers of control units and pre-treatment periods.