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Activity Number: 288
Type: Invited
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #318149 View Presentation
Title: Functional Regression Models for Large Spatial Data with Endogeneity and Unstructured Dependence
Author(s): Arnab Bhattacharjee* and Tapabrata Maiti
Companies: Heriot-Watt University and Michigan State University
Keywords: spatial functional regression ; endogenous spatial weights ; functional instrumental variables ; hedonic house price models

Recent literature has highlighted the usefulness of the functional regression model for estimation of models with endogenous spatial dependence and spatial heterogeneity. The key idea is that, since the functional regression estimator is essentially a method-of-moments estimator, the assumption of independent and identically distributed errors is not required here. We extend this approach to the context where the nature of spatial dependence is estimated from the data, or is otherwise endogenously determined with the dependent variable. Using a distance-based, or some other purely exogenous, spatial weights matrix as the instrument, we develop methods for functional regression with endogenous functional regressors and correlated errors in a large data setting. The proposed methods are illustrated using an application to hedonic house price models.

Authors who are presenting talks have a * after their name.

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