Details
| Original language | English |
|---|---|
| Article number | 1474 |
| Number of pages | 13 |
| Journal | Symmetry |
| Volume | 14 |
| Issue number | 7 |
| Early online date | 19 Jul 2022 |
| Publication status | Published - Jul 2022 |
Abstract
In spatial econometrics, we usually assume that the spatial dependence structure is known and that all information about it is contained in a spatial weights matrix (Formula presented.). However, in practice, the structure of (Formula presented.) is unknown a priori and difficult to obtain, especially for asymmetric dependence. In this paper, we propose a data-driven method to obtain (Formula presented.), whether it is symmetric or asymmetric. This is achieved by calculating the area overlap of the adjacent regions/districts with a given shape (a pizza-like shape, in our case). With (Formula presented.) determined in this way, we estimate the potentially asymmetric spatial autoregressive dependence on irregular lattices. We verify our method using Monte Carlo simulations for finite samples and compare it with classical approaches such as Queen’s contiguity matrices and inverse-distance weighting matrices. Finally, our method is applied to model the evolution of sales prices for building land in Brandenburg, Germany. We show that the price evolution and its spatial dependence are mainly driven by the orientation towards Berlin.
Keywords
- Akaike information criterion (AIC), maximum likelihood estimation, model selection, spatial autoregressive model (SAR), weights matrix
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science (miscellaneous)
- Chemistry(all)
- Chemistry (miscellaneous)
- Mathematics(all)
- Physics and Astronomy(all)
- Physics and Astronomy (miscellaneous)
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Symmetry, Vol. 14, No. 7, 1474, 07.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Estimation of Asymmetric Spatial Autoregressive Dependence on Irregular Lattices
AU - Harke, Franz H.
AU - Merk, Miryam S.
AU - Otto, Philipp
PY - 2022/7
Y1 - 2022/7
N2 - In spatial econometrics, we usually assume that the spatial dependence structure is known and that all information about it is contained in a spatial weights matrix (Formula presented.). However, in practice, the structure of (Formula presented.) is unknown a priori and difficult to obtain, especially for asymmetric dependence. In this paper, we propose a data-driven method to obtain (Formula presented.), whether it is symmetric or asymmetric. This is achieved by calculating the area overlap of the adjacent regions/districts with a given shape (a pizza-like shape, in our case). With (Formula presented.) determined in this way, we estimate the potentially asymmetric spatial autoregressive dependence on irregular lattices. We verify our method using Monte Carlo simulations for finite samples and compare it with classical approaches such as Queen’s contiguity matrices and inverse-distance weighting matrices. Finally, our method is applied to model the evolution of sales prices for building land in Brandenburg, Germany. We show that the price evolution and its spatial dependence are mainly driven by the orientation towards Berlin.
AB - In spatial econometrics, we usually assume that the spatial dependence structure is known and that all information about it is contained in a spatial weights matrix (Formula presented.). However, in practice, the structure of (Formula presented.) is unknown a priori and difficult to obtain, especially for asymmetric dependence. In this paper, we propose a data-driven method to obtain (Formula presented.), whether it is symmetric or asymmetric. This is achieved by calculating the area overlap of the adjacent regions/districts with a given shape (a pizza-like shape, in our case). With (Formula presented.) determined in this way, we estimate the potentially asymmetric spatial autoregressive dependence on irregular lattices. We verify our method using Monte Carlo simulations for finite samples and compare it with classical approaches such as Queen’s contiguity matrices and inverse-distance weighting matrices. Finally, our method is applied to model the evolution of sales prices for building land in Brandenburg, Germany. We show that the price evolution and its spatial dependence are mainly driven by the orientation towards Berlin.
KW - Akaike information criterion (AIC)
KW - maximum likelihood estimation
KW - model selection
KW - spatial autoregressive model (SAR)
KW - weights matrix
UR - http://www.scopus.com/inward/record.url?scp=85137361680&partnerID=8YFLogxK
U2 - 10.3390/sym14071474
DO - 10.3390/sym14071474
M3 - Article
AN - SCOPUS:85137361680
VL - 14
JO - Symmetry
JF - Symmetry
SN - 2073-8994
IS - 7
M1 - 1474
ER -