Details
| Original language | English |
|---|---|
| Pages (from-to) | 696-711 |
| Journal | Journal of Computational and Graphical Statistics |
| Volume | 32 |
| Issue number | 2 |
| Early online date | 4 Oct 2022 |
| Publication status | Published - 2023 |
Abstract
In this article, we propose a two-stage LASSO estimation approach for the estimation of a full spatial weight matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial location. These locally varying mean levels, however, can easily be mistaken as spatial dependence and vice versa. Thus, the proposed approach jointly estimates the spatial dependence, all structural breaks, and the local mean levels. For selection of the penalty parameter, we propose a completely new selection criterion based on the distance between the empirical spatial autocorrelation and the spatial dependence estimated in the model. Through simulation studies, we will show the finite-sample performance of the estimators and provide practical guidance as to when the approach could be applied. Finally, the method will be illustrated by an empirical example of intra-city monthly real-estate prices in Berlin between 1995 and 2014. The spatial units will be defined by the respective postal codes. The new approach allows us to estimate local mean levels and quantify the deviation of the observed prices from these levels due to spatial spillover effects. In doing so, the entire spatial dependence structure is estimated on a data-driven basis. Supplementary materials for this article are available online.
Keywords
- LASSO estimation, Real-estate market, Spatial weight matrix, Spatiotemporal autoregressive models, Statistical learning, Structural breaks
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
- Mathematics(all)
- Discrete Mathematics and Combinatorics
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In: Journal of Computational and Graphical Statistics, Vol. 32, No. 2, 2023, p. 696-711.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Estimation of the Spatial Weighting Matrix for Spatiotemporal Data under the Presence of Structural Breaks
AU - Otto, Philipp
AU - Steinert, Rick
N1 - Funding Information: We want to thank the two anonymous reviewers of JCGS whose suggestions and comments helped us a lot in the revision process. We would also like to thank the Associate Editor for his detailed and thorough comments.
PY - 2023
Y1 - 2023
N2 - In this article, we propose a two-stage LASSO estimation approach for the estimation of a full spatial weight matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial location. These locally varying mean levels, however, can easily be mistaken as spatial dependence and vice versa. Thus, the proposed approach jointly estimates the spatial dependence, all structural breaks, and the local mean levels. For selection of the penalty parameter, we propose a completely new selection criterion based on the distance between the empirical spatial autocorrelation and the spatial dependence estimated in the model. Through simulation studies, we will show the finite-sample performance of the estimators and provide practical guidance as to when the approach could be applied. Finally, the method will be illustrated by an empirical example of intra-city monthly real-estate prices in Berlin between 1995 and 2014. The spatial units will be defined by the respective postal codes. The new approach allows us to estimate local mean levels and quantify the deviation of the observed prices from these levels due to spatial spillover effects. In doing so, the entire spatial dependence structure is estimated on a data-driven basis. Supplementary materials for this article are available online.
AB - In this article, we propose a two-stage LASSO estimation approach for the estimation of a full spatial weight matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial location. These locally varying mean levels, however, can easily be mistaken as spatial dependence and vice versa. Thus, the proposed approach jointly estimates the spatial dependence, all structural breaks, and the local mean levels. For selection of the penalty parameter, we propose a completely new selection criterion based on the distance between the empirical spatial autocorrelation and the spatial dependence estimated in the model. Through simulation studies, we will show the finite-sample performance of the estimators and provide practical guidance as to when the approach could be applied. Finally, the method will be illustrated by an empirical example of intra-city monthly real-estate prices in Berlin between 1995 and 2014. The spatial units will be defined by the respective postal codes. The new approach allows us to estimate local mean levels and quantify the deviation of the observed prices from these levels due to spatial spillover effects. In doing so, the entire spatial dependence structure is estimated on a data-driven basis. Supplementary materials for this article are available online.
KW - LASSO estimation
KW - Real-estate market
KW - Spatial weight matrix
KW - Spatiotemporal autoregressive models
KW - Statistical learning
KW - Structural breaks
UR - http://www.scopus.com/inward/record.url?scp=85139107395&partnerID=8YFLogxK
U2 - 10.48550/arXiv.1810.06940
DO - 10.48550/arXiv.1810.06940
M3 - Article
VL - 32
SP - 696
EP - 711
JO - Journal of Computational and Graphical Statistics
JF - Journal of Computational and Graphical Statistics
SN - 1061-8600
IS - 2
ER -