Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/261229 
Year of Publication: 
2022
Series/Report no.: 
Cardiff Economics Working Papers No. E2022/1
Publisher: 
Cardiff University, Cardiff Business School, Cardiff
Abstract: 
In this paper, we discuss estimation procedure and various inferential methods for varying coefficient panel data models that include spatially correlated error components. Our estimation procedure is an extension of the quasi-maximum likelihood method for spatial panel data regression to the conditional local kernel-weighted likelihood. We allow both relevant and irrelevant regressors in our model and propose a variable selection procedure that we show to perform well for models that involve spatial error dependence. We also extend our procedure so that it allows empirical modelling and testing of the so-called semi-varying coefficient specification. To ensure the statistical validity of our methods, we derive a set of asymptotic properties based on a collection of primitive assumptions that appear regularly in the nonparametric literature. Finally, we use the proposed model and methods to analyse the municipal disparities in mental health service spending by local authorities in England in order to illustrate practicability and empirical relevance.
Subjects: 
Spatial models
Error components
Local maximum likelihood
Varying coefficient
Variable selection
Mental health services and expenditures
JEL: 
C14
C51
C52
G12
G17
Document Type: 
Working Paper

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