Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241933 
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP58/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We propose a new estimation methodology to address the presence of covariate measurement error by exploiting the availability of spatial data. The approach uses neighboring observations as repeated measurements, after suitably controlling for the random distance between the observations in a way that allows the use of operator diagonalization methods to establish identification. The method is applicable to general nonlinear models with potentially nonclassical errors and does not rely on a priori distributional assumptions regarding any of the variables. The method's implementation combines a sieve semiparametric maximum likelihood with a first-step kernel conditional density estimator and simulation methods. The method's effectiveness is illustrated through both controlled simulations and an application to the assessment of the effect of pre-colonial political structure on current economic development in Africa.
Subjects: 
Errors-in-variables
Economic development
Operator methods
Spatial statistics
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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