Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes

Franzese, Robert J.; Hays, Jude C.; Cook, Scott

doi:10.1017/psrm.2015.14

Cited by 29 publications

(25 citation statements)

References 30 publications

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“… 9 Limited and full information estimators allowing for both spatial and non-spatial endogeneity have been established, with Kelejian and Prucha (2004) the first to derive formal large sample results; see also Drukker, Egger and Prucha (2013) for a GMM estimator. Franzese, Hays and Cook (2016) discuss the complications of modeling spatial interdependence in discrete-choice models. …”

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confidence: 99%

Spatial interdependence and instrumental variable models

Betz

Cook

Hollenbach

2019

PSRM

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Instrumental variable (IV) methods are widely used to address endogeneity concerns. Yet, a specific kind of endogeneity – spatial interdependence – is regularly ignored. We show that ignoring spatial interdependence in the outcome results in asymptotically biased estimates even when instruments are randomly assigned. The extent of this bias increases when the instrument is also spatially clustered, as is the case for many widely used instruments: rainfall, natural disasters, economic shocks, and regionally- or globally-weighted averages. Because the biases due to spatial interdependence and predictor endogeneity can offset, addressing only one can increase the bias relative to ordinary least squares. We demonstrate the extent of these biases both analytically and via Monte Carlo simulation. Finally, we discuss a general estimation strategy – S-2SLS – that accounts for both outcome interdependence and predictor endogeneity, thereby recovering consistent estimates of predictor effects.

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confidence: 99%

Spatial interdependence and instrumental variable models

Betz

Cook

Hollenbach

2019

PSRM

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“…Although both LSGM and SP allow for modeling dependence in binary data, the two estimators are based on rather different theoretical assumptions regarding the nature of the modeled dependence, and are not substitutable in any general sense. SP was developed within the spatial econometrics literature to model spatially dependent outcomes as steady-state equilibria resulting from some shock that reverberates through the whole system (Beron, Murdoch, and Vijverberg 2003; Franzese, Hays, and Cook 2016). The effect of the shock is conditioned by the weights matrix.…”

Section: A Monte Carlo Comparison: Lsgm Vs Spatial Probitmentioning

confidence: 99%

A Local Structure Graph Model: Modeling Formation of Network Edges as a Function of Other Edges

Chyzh

Kaiser

2019

Polit. Anal.

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Localized network processes are central to the study of political science, whether in the formation of political coalitions and voting blocs, balancing and bandwagoning, policy learning, imitation, diffusion, tipping-point dynamics, or cascade effects. These types of processes are not easily modeled using traditional network approaches, which focus on global rather than local structures within networks. We show that localized network processes, in which network edges form in response to the formation of other edges, are best modeled by shifting from the traditional theoretical framework of nodes-as-actors to what we term a nodes-as-actions framework, which allows for zeroing in on relationships among network connections. We show that the proposed theoretical framework is statistically compatible with a local structure graph model (LSGM). We demonstrate the properties of LSGMs using a Monte Carlo experiment and explore action–reaction processes in two empirical applications: formation of alliances among countries and legislative cosponsorships in the US Senate.

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“…These cover the number of mitigation and adaptation frameworks ("Diffusion, mitigation"/"Diffusion, adaptation") in other countries within the sample. For this specific operationalization, we follow Fankhauser et al (2016) by considering all EU members and not just neighboring countries as the relevant peer group (for an overview on spatiotemporal-autoregressive processes see Franzese, Hays & Cook, 2016).…”

Section: Drivers For National Climate Change Mitigation and Adaptatiomentioning

confidence: 99%

Legislative Dynamics of Mitigation and Adaptation Framework Policies in the EU

Fleig¹,

Schmidt²,

Tosun³

2017

European Policy Analysis

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In this study we examine the adoption of climate framework laws in 20 member states of the European Union (EU) from 1990 until 2015. Our analysis is guided by the following research questions: First, which EU member states have adopted climate change legislation that addresses mitigation, adaptation, or both? Second, do EU countries act coherently or do we observe differences across countries regarding the response strategy or point in time of policies directed towards climate change? Third, comparing mitigation and adaptation frameworks, which approach came first and did it affect the adoption of the respective other? Our findings show that all countries save one (i.e., Hungary) covered in the analysis have adopted framework laws tackling mitigation and/or adaptation to climate change. While we did not observe a coherent pattern with respect to timing or sequence of adaptation and mitigation frameworks, we found that the best predictor of a national government's behavior is the behavior of the governments of other countries. However, the response of EU member states to climate change is less homogenous than one would have expected based on previous literature. In addition, by highlighting that mitigation and adaptation measures are equally prominent, at least within our sample of EU countries, we complement current climate change literature that often places mitigation efforts at the forefront in terms of legislation activity. Future research should examine this issue more closely, as this dynamic is likely to continue since the Paris Agreement gave prominence to adaptation.

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Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes

Cited by 29 publications

References 30 publications

Spatial interdependence and instrumental variable models

Spatial interdependence and instrumental variable models

A Local Structure Graph Model: Modeling Formation of Network Edges as a Function of Other Edges

Legislative Dynamics of Mitigation and Adaptation Framework Policies in the EU

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