A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

Shor, Boris; Bafumi, Joseph; Keele, Luke; Park, David

doi:10.1093/pan/mpm006

Cited by 87 publications

(74 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A dyadic diffusion model could be conceptualized as a nonnested structure in which observations are grouped within State i , State j , and years. Such a model could be written as follows (see also Shor, Bafumi, Keele, and Park 2007):…”

Section: Methodological Issuesmentioning

confidence: 99%

Empirical Modeling of Policy Diffusion in Federal States: The Dyadic Approach

Gilardi

Füglister

2008

Swiss Political Sci Review

102

View full text Add to dashboard Cite

show abstract

Section: Methodological Issuesmentioning

confidence: 99%

Empirical Modeling of Policy Diffusion in Federal States: The Dyadic Approach

Gilardi

Füglister

2008

Swiss Political Sci Review

102

View full text Add to dashboard Cite

show abstract

“…For each simulation scenario, the data were generated and models estimated 1,000 times, and three quantities were calculated: bias, root mean square error (RMSE) and optimism, calculated as in Shor et al (2007) and in line with the simulations presented by Plu¨mper andTroeger (2007, 2011). Bias is the mean of the ratios of the true parameter value to the estimated parameter, and so a value of 1 suggests that the model estimates are, on average, exactly correct.…”

Section: Simulationsmentioning

confidence: 99%

Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data

Bell¹,

Jones²

2014

PSRM

1,016

887

View full text Add to dashboard Cite

This article challenges Fixed Effects (FE) modeling as the ‘default’ for time-series-cross-sectional and panel data. Understanding different within and between effects is crucial when choosing modeling strategies. The downside of Random Effects (RE) modeling—correlated lower-level covariates and higher-level residuals—is omitted-variable bias, solvable with Mundlak's (1978a) formulation. Consequently, RE can provide everything that FE promises and more, as confirmed by Monte-Carlo simulations, which additionally show problems with Plümper and Troeger's FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions and complex variance functions. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context/heterogeneity, modeled using RE. The implications extend beyond political science to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.

show abstract

“…For the FE models, we calculated both naive and robust SEs. Figure 1 shows the 'optimism'-the ratio of the true sampling variability to the sampling variability estimated by the standard error (see Shor et al 2007)-for a single covariate, in a variety of scenarios.…”

Section: Random Slopes Modelsmentioning

confidence: 99%

“…This paper therefore presents and clarifies the differences between two key approaches: fixed effects (FE) and random effects (RE) models. We argue that in most research scenarios, a well-specified RE model provides everything that FE provides and more, making it the superior method for most practitioners (see also Shor et al 2007;Western 1998). However, this view is at odds with the common suggestion that FE is often preferable (e.g.…”

Section: Introductionmentioning

confidence: 99%

Fixed and random effects models: making an informed choice

2018

View full text Add to dashboard Cite

This paper assesses the options available to researchers analysing multilevel (including longitudinal) data, with the aim of supporting good methodological decision-making. Given the confusion in the literature about the key properties of fixed and random effects (FE and RE) models, we present these models' capabilities and limitations. We also discuss the within-between RE model, sometimes misleadingly labelled a 'hybrid' model, showing that it is the most general of the three, with all the strengths of the other two. As such, and because it allows for important extensions-notably random slopes-we argue it should be used (as a starting point at least) in all multilevel analyses. We develop the argument through simulations, evaluating how these models cope with some likely mis-specifications. These simulations reveal that (1) failing to include random slopes can generate anticonservative standard errors, and (2) assuming random intercepts are Normally distributed, when they are not, introduces only modest biases. These results strengthen the case for the use of, and need for, these models.

show abstract

A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data

Cited by 87 publications

References 20 publications

Empirical Modeling of Policy Diffusion in Federal States: The Dyadic Approach

Empirical Modeling of Policy Diffusion in Federal States: The Dyadic Approach

Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data

Fixed and random effects models: making an informed choice

Contact Info

Product

Resources

About