Estimating and Forecasting with a Dynamic Spatial Panel Data Model*

Baltagi, Badi H.; Fingleton, Bernard; Pirotte, Alain

doi:10.1111/obes.12011

Cited by 101 publications

(97 citation statements)

References 48 publications

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“…Conditions for invertibility of (I N − ρW ) and temporal stability of the dynamic spatial panel specication for the state variables as given by Equation (2) are discussed in Elhorst (2012) and Baltagi et al (2014). For a W matrix with real eigenvalues, a sucient condition for invertibility is that ρ ∈ (1/ζ max , 1/ζ min ), where ζ min and ζ max are the extreme eigenvalues.…”

Section: Model Specicationmentioning

confidence: 99%

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Likelihood Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes

2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. recently developed ecient importance sampling techniques applicable to high-dimensional spatial models with sparse precision matrices. Our estimation results conrm socio-economic explanations for crime and, foremost, the broken-windows hypothesis, whereby less severe crimes in a region is a leading indicator for severe crimes. In addition to ML parameter estimates, we compute several other statistics of interest for law enforcement such as elasticities (idiosyncratic, total, short-term as well as long-term) of severe crimes w.r.t. less severe crimes, one-month-ahead out-of-sample forecasts, predictive cumulative distribution functions and validation test statistics based on these cdf's. Terms of use: Documents inJEL classication: C15; C23; C25; C51; C53; K42; R15.

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Section: Model Specicationmentioning

confidence: 99%

“…The latent process is assumed to be a linear Gaussian dynamic panel model in space and time as discussed, e.g., by Elhorst (2010Elhorst ( , 2012 and Baltagi et al (2014). It has the following form:…”

Section: Model Specicationmentioning

confidence: 99%

Likelihood Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes

2015

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“…This paper builds on the work of Fingleton et al (2012), Martin (2012), Fingleton et al (2015), and Martin et al (2016), who analyse the impact of recessionary shocks to UK or EU regions, by applying a dynamic spatial panel model (DSPM) estimator, following Baltagi et al (2014).…”

Section: Introductionmentioning

confidence: 99%

US Metropolitan Area Resilience: Insights from dynamic spatial panel estimation

Doran

Fingleton

2017

Environ Plan A

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In this paper, we show that the economic crisis commencing in 2007 had different impacts across US Metropolitan Statistical Areas, and seek to understand why differences occurred. The hypothesis of interest is that differences in industrial structure are a cause of variations in response to the crisis. Our approach uses a state-of-the art dynamic spatial panel model to obtain counterfactual predictions of Metropolitan Statistical Area employment levels from 2008 to 2014. The counterfactual employment series are compared with actual employment paths in order to obtain Metropolitan Statistical Area-specific measures of crisis impact, which then are analysed with a view to testing the hypothesis that resilience to the crisis was dependent on Metropolitan Statistical Area industrial structure.

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“…In the context of spatial panel data, the prediction is for a location (or locations) at a future time, so the covariance vector is the (estimated) covariances between the residual at the site in the future and set of known residuals, and the covariance matrix relates to the known sites. Various modifications to give the appropriate BLUP or tractable predictors in the context of spatial panel data, inclusive of spatial dependence, are introduced by Baltagi, Fingleton, and Pirotte (2014) in the case of a spatial lag model with error component structure and in the case of a dynamic model with a spatial lag on the dependent variable and a spatial autoregressive process of the error components. Goulard, Laurent and Thomas-Aignan provide a valuable service to the academic community by suggesting some interesting extensions and new proposals and integrating these with existing spatial-econometric (cross-sectional) literature via a consistent mathematical notation, and in their comparisons of various predictors via Monte Carlo simulation methods.…”

Section: Palabras Clavesmentioning

confidence: 99%