Estimation of the spatial weighting matrix for regular lattice data – An adaptive lasso approach with cross-sectional resampling

01/06/2020
by   Miryam S. Merk, et al.
12

Spatial econometric research typically relies on the assumption that the spatial dependence structure is known in advance and is represented by a deterministic spatial weights matrix. Contrary to classical approaches, we investigate the estimation of sparse spatial dependence structures for regular lattice data. In particular, an adaptive least absolute shrinkage and selection operator (lasso) is used to select and estimate the individual connections of the spatial weights matrix. To recover the spatial dependence structure, we propose cross-sectional resampling, assuming that the random process is exchangeable. The estimation procedure is based on a two-step approach to circumvent simultaneity issues that typically arise from endogenous spatial autoregressive dependencies. The two-step adaptive lasso approach with cross-sectional resampling is verified using Monte Carlo simulations. Eventually, we apply the procedure to model nitrogen dioxide (NO_2) concentrations and show that estimating the spatial dependence structure contrary to using prespecified weights matrices improves the prediction accuracy considerably.

READ FULL TEXT

page 22

page 23

page 26

research
10/16/2018

Estimation of the Spatial Weighting Matrix for Spatiotemporal Data under the Presence of Structural Breaks

In this paper, we propose a two-step lasso estimation approach to estima...
research
06/22/2022

Flexible Modeling of Multivariate Spatial Extremes

We develop a novel multi-factor copula model for multivariate spatial ex...
research
07/26/2019

A memory-free spatial additive mixed modeling for big spatial data

This study develops a spatial additive mixed modeling (AMM) approach est...
research
11/04/2019

Quantile regression: a penalization approach

Sparse group LASSO (SGL) is a penalization technique used in regression ...
research
01/22/2020

Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions

In this paper, we propose an adaptive group lasso procedure to efficient...
research
02/19/2019

Penalized basis models for very large spatial datasets

Many modern spatial models express the stochastic variation component as...

Please sign up or login with your details

Forgot password? Click here to reset