Sliced Inverse Regression for Spatial Data

Christoph Muehlmann (), Hannu Oja () and Klaus Nordhausen ()
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Christoph Muehlmann: Vienna University of Technology, Institute of Statistics & Mathematical Methods in Economics
Hannu Oja: University of Turku, Department of Mathematics and Statistics
Klaus Nordhausen: Vienna University of Technology, Institute of Statistics & Mathematical Methods in Economics

A chapter in Festschrift in Honor of R. Dennis Cook, 2021, pp 87-107 from Springer

Abstract: Abstract Sliced inverse regression is one of the most popular sufficient dimension reduction methods. Originally, it was designed for independent and identically distributed data and recently extend to the case of serially and spatially dependent data. In this work we extend it to the case of spatially dependent data where the response might depend also on neighbouring covariates when the observations are taken on a grid-like structure as it is often the case in econometric spatial regression applications. We suggest guidelines on how to decide upon the dimension of the subspace of interest and also which spatial lag might be of interest when modeling the response. These guidelines are supported by a conducted simulation study.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-69009-0_5

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DOI: 10.1007/978-3-030-69009-0_5

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