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We consider treatment effect estimation via a difference-in-difference approach for data with local spatial interaction such that the outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions...
Persistent link: https://www.econbiz.de/10011301196
Existing indices measuring the spatial distribution of economic activity such as the Krugman Specialisation Index, the Hirschmann-Herfindahl index and the Ellison-Glaeser index typically do not take into account the spatial structure of the data. In this paper, we first consider traditional...
Persistent link: https://www.econbiz.de/10011373826
, referring to both heterogeneity and interdependence of phenomena occurring in two-dimensional space. Spatial autocorrelation or …
Persistent link: https://www.econbiz.de/10011334352
error models – to correct for misspecification due to neglected spatial autocorrelation in the data set. Our empirical …
Persistent link: https://www.econbiz.de/10011343272
as employment, requires an understanding of spatial (or spatio-temporal) autocorrelation effects associated with a … deal with the analysis of and accounting for spatial autocorrelation by means of spatial filtering t! echniques for data …
Persistent link: https://www.econbiz.de/10011349204
A new model for time-varying spatial dependencies is introduced. It forms an extension to the popular spatial lag model and can be estimated conveniently by maximum likelihood. The spatial dependence parameter is assumed to follow a generalized autoregressive score (GAS) process. The theoretical...
Persistent link: https://www.econbiz.de/10010491085
We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...
Persistent link: https://www.econbiz.de/10010391531
This paper introduces a new model for spatial time series in which cross-sectional dependence varies nonlinearly over space by means of smooth transitions. We refer to our model as the Smooth Transition Spatial Autoregressive (ST-SAR). We establish consistency and asymptotic Gaussianity for the...
Persistent link: https://www.econbiz.de/10011658755
Persistent link: https://www.econbiz.de/10001718624
Persistent link: https://www.econbiz.de/10000151697