What To Do About Atheoretic Lags

Cranmer, Skyler J.; Rice, Douglas; Siverson, Randolph M.

doi:10.1017/psrm.2015.36

Cited by 6 publications

(7 citation statements)

References 71 publications

(81 reference statements)

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“…This result suggest that this practice is generally inadequate for those purposes, and that lags of at least five years should be considered in order to achieve the desired effect. For a detailed discussion of how to choose lag lengths, see Cranmer, Rice, and Siverson (2015).…”

Section: Resultsmentioning

confidence: 99%

What Can We Learn from Predictive Modeling?

Cranmer¹,

Desmarais²

2017

Polit. Anal.

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The large majority of inferences drawn in empirical political research follow from model-based associations (e.g. regression). Here, we articulate the benefits of predictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that were not used to estimate the model's parameters. Our goals are threefold. First, we review the central benefits of this under-utilized approach from a perspective uncommon in the existing literature: we focus on how predictive modeling can be used to complement and augment standard associational analyses. Second, we advance the state of the literature by laying out a simple set of benchmark predictive criteria. Third, we illustrate our approach through a detailed application to the prediction of interstate conflict. Forthcoming in Political AnalysisWord count: 9,205 * Many thanks to Alison Craig for research assistance. Sincere thanks also to Matt Blackwell and Michael Neblo for helpful comments on an earlier draft.

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Section: Resultsmentioning

confidence: 99%

What Can We Learn from Predictive Modeling?

Cranmer¹,

Desmarais²

2017

Polit. Anal.

Self Cite

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“…On the other hand, the model corresponds to a situation in which substantive knowledge and theory are not precise enough to determine the correct temporal lag for the effect of X on Y. A model with both a contemporaneous and a lagged effect allows researchers to address this uncertainty by estimating both effects (for an approach to handle atheoretical lags see also Cranmer et al 2017). …”

mentioning

confidence: 99%

How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study

Leszczensky¹,

Wolbring²

2018

Preprint

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Does X affect Y? Answering this question is particularly difficult if reverse causality is present. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses reverse causality threatens causal inference based on conventional panel models. Whereas the methodological literature has suggested various alternative solutions, these approaches face many criticisms, chief among them to be sensitive to the correct specification of temporal lags. Applied researchers are thus left with little guidance. Seeking to provide such guidance, we compare how different panel models perform under a range of different conditions. Our Monte Carlo simulations reveal that unlike conventional panel models, a cross-lagged panel model with fixed effects not only offers protection against bias arising from reverse causality under a wide range of conditions but also helps to circumvent the problem of misspecified temporal lags.

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“…It is therefore almost impossible to test all possible combinations of covariates and all assumptions necessary to specify a model since the size of the model space quickly exceeds beyond the limits of implementability (Neumayer and Plümper 2017, 72). By the same token, BMA is difficult to implement for complex model structures (e.g., Cranmer, Rice, and Siverson 2017). It is not the most powerful tool for model selection as there are alternative algorithms that can handle complex model structures in a fast and efficient manner (e.g., Piribauer and Crespo Cuaresma 2016).…”

Section: Bma For Spatial Econometric Modelsmentioning

confidence: 99%

“…In a similar vein, Kostov (2010, 2013) proposes component-wise model-boosting to find the appropriate network structure among a predefined set of candidate networks. This algorithm has the appeal that it is comparatively fast and efficient which makes it a powerful tool for model selection (Cranmer, Rice, and Siverson 2017).…”

Section: Alternative Approaches To Network Uncertaintymentioning

confidence: 99%

“…Finally, each of the articles identifies a set of feasible network specifications based on theoretical considerations. This is important since, as discussed in Section 3, the composition of the set of alternative models is a crucial step in the BMA approach (see also Barker and Link 2015; Cranmer, Rice, and Siverson 2017).…”

Section: Political Science Applicationsmentioning

confidence: 99%

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The Sensitivity of Spatial Regression Models to Network Misspecification

Juhl

2019

Polit. Anal.

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Spatial econometric models become increasingly popular in various subfields of political science. However, the necessity to specify the underlying network of dependencies, denoted by $\boldsymbol{W}$, prior to estimation is a prevalent source of criticism since the true dependence structure is rarely known and theories mostly provide insufficient guidance. The present study investigates the effects of this network uncertainty which is a special case of model uncertainty that arises from uncertainty about the correct specification of $\boldsymbol{W}$. It advocates Bayesian model averaging (BMA) as a superior approach to this problem, located at the intersection of theory and empirics. Conducting Monte Carlo experiments, I demonstrate that, while the effect estimates are robust toward a misspecification in the functional form of $\boldsymbol{W}$, uncertainty in the neighborhood definition can bias the effect estimates derived from spatial autoregressive models. In contrast to alternative techniques, BMA directly addresses network uncertainty, correctly identifies the true network structure in the set of feasible alternatives, and provides unbiased effect estimates. Two replication studies from different subfields of the discipline illustrate the benefits of this approach for applied research.

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What To Do About Atheoretic Lags

Cited by 6 publications

References 71 publications

What Can We Learn from Predictive Modeling?

What Can We Learn from Predictive Modeling?

How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study

The Sensitivity of Spatial Regression Models to Network Misspecification

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