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Article: A geographically and temporally weighted autoregressive model with application to housing prices
Title | A geographically and temporally weighted autoregressive model with application to housing prices |
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Authors | |
Keywords | GTWAR housing price spatiotemporal autocorrelation spatiotemporal nonstationarity two-stage least squares estimation |
Issue Date | 2014 |
Citation | International Journal of Geographical Information Science, 2014, v. 28, n. 5, p. 1186-1204 How to Cite? |
Abstract | Spatiotemporal autocorrelation and nonstationarity are two important issues in the modeling of geographical data. Built upon the geographically weighted regression (GWR) model and the geographically and temporally weighted regression (GTWR) model, this article develops a geographically and temporally weighted autoregressive model (GTWAR) to account for both nonstationary and auto-correlated effects simultaneously and formulates a two-stage least squares framework to estimate this model. Compared with the maximum likelihood estimation method, the proposed algorithm that does not require a prespecified distribution can effectively reduce the computation complexity. To demonstrate the efficacy of our model and algorithm, a case study on housing prices in the city of Shenzhen, China, from year 2004 to 2008 is carried out. The results demonstrate that there are substantial benefits in modeling both spatiotemporal nonstationarity and autocorrelation effects simultaneously on housing prices in terms of R2 and Akaike Information Criterion (AIC). The proposed model reduces the absolute errors by 31.8% and 67.7% relative to the GTWR and GWR models, respectively, in the Shenzhen data set. Moreover, the GTWAR model improves the goodness-of-fit of the ordinary least squares model and the GTWR model from 0.617 and 0.875 to 0.914 in terms of R2. The AIC test corroborates that the improvements made by GTWAR over the GWR and the GTWR models are statistically significant. 2014 © 2014 Taylor & Francis. |
Persistent Identifier | http://hdl.handle.net/10722/329325 |
ISSN | 2021 Impact Factor: 5.152 2020 SCImago Journal Rankings: 1.294 |
DC Field | Value | Language |
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dc.contributor.author | Wu, Bo | - |
dc.contributor.author | Li, Rongrong | - |
dc.contributor.author | Huang, Bo | - |
dc.date.accessioned | 2023-08-09T03:31:59Z | - |
dc.date.available | 2023-08-09T03:31:59Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | International Journal of Geographical Information Science, 2014, v. 28, n. 5, p. 1186-1204 | - |
dc.identifier.issn | 1365-8816 | - |
dc.identifier.uri | http://hdl.handle.net/10722/329325 | - |
dc.description.abstract | Spatiotemporal autocorrelation and nonstationarity are two important issues in the modeling of geographical data. Built upon the geographically weighted regression (GWR) model and the geographically and temporally weighted regression (GTWR) model, this article develops a geographically and temporally weighted autoregressive model (GTWAR) to account for both nonstationary and auto-correlated effects simultaneously and formulates a two-stage least squares framework to estimate this model. Compared with the maximum likelihood estimation method, the proposed algorithm that does not require a prespecified distribution can effectively reduce the computation complexity. To demonstrate the efficacy of our model and algorithm, a case study on housing prices in the city of Shenzhen, China, from year 2004 to 2008 is carried out. The results demonstrate that there are substantial benefits in modeling both spatiotemporal nonstationarity and autocorrelation effects simultaneously on housing prices in terms of R2 and Akaike Information Criterion (AIC). The proposed model reduces the absolute errors by 31.8% and 67.7% relative to the GTWR and GWR models, respectively, in the Shenzhen data set. Moreover, the GTWAR model improves the goodness-of-fit of the ordinary least squares model and the GTWR model from 0.617 and 0.875 to 0.914 in terms of R2. The AIC test corroborates that the improvements made by GTWAR over the GWR and the GTWR models are statistically significant. 2014 © 2014 Taylor & Francis. | - |
dc.language | eng | - |
dc.relation.ispartof | International Journal of Geographical Information Science | - |
dc.subject | GTWAR | - |
dc.subject | housing price | - |
dc.subject | spatiotemporal autocorrelation | - |
dc.subject | spatiotemporal nonstationarity | - |
dc.subject | two-stage least squares estimation | - |
dc.title | A geographically and temporally weighted autoregressive model with application to housing prices | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/13658816.2013.878463 | - |
dc.identifier.scopus | eid_2-s2.0-84899120820 | - |
dc.identifier.volume | 28 | - |
dc.identifier.issue | 5 | - |
dc.identifier.spage | 1186 | - |
dc.identifier.epage | 1204 | - |
dc.identifier.eissn | 1362-3087 | - |