地理加權自相關分量迴歸

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Basic & Applied Sciences > Mathematical Statistics
College of Management > Master's Program, Department of Statistics

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顏吟真 , Masters　　Advisor：陳怡如　　

繁體中文

空間資料分析；地理加權迴歸；分量迴歸；地理加權分量迴歸；自迴歸模式；工具變數；殘差拔靴法；蒙地卡羅模擬法； Spatial data analysis ； Geographically Weighted Regression ； Quantile regression ； Geographically Weighted Quantile Regression ； Autoregressive model ； Instrumental Variables ； Residual bootstrap method ； Monte-Carlo simulation

Abstract │ Reference (27) │ Times Cited (2) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

Geographically Weighted Regression (GWR; Brunsdon et al., 1998) and Quantile Regression (QR; Koenker and Bassett, 1978) are two important tools respectively in geography and econometrics in analyzing various issues of empirical studies. The former is designed to explore spatial nonstationarity and the latter is constructed to model relationships among variables across the whole distribution of a dependent variable. While both of these methods have been widely used in literature, they seem to be two unconnected lines of knowledge inquiry until recently (Chen et al., 2012). Chen et al. developed an approach so-called GeographicallyWeighted Quantile Regression (GWQR) to integrate QR and GWR. This innovative approach can explore the spatial nonstationarity not only over space but also across different levels of the dependent variable. It is, however, argued as a methodological issue that the GWQR does not account for spatial dependence between geographic locations. The goal of this study is then to address such perceived gap, and to introduce a Geographically Weighted Autoregressive Quantile Regression (GWAQR) model which includes (local) spatial lag autocorrelation components. A simulation study is conducted as well to examine the performance of the proposed estimator and further validate the GWAQR methodology.

Reference ( 27 ) 〈TOP〉

[1] Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer.
連結：
[2] Anselin, L., Florax, R.J.G.M., and Rey, S.J. (2004). Advances in Spatial Econometrics: Methodology, Tools and Applications. Berlin: Springer Verlag.
連結：
[4] Brunsdon, C., Fotheringham, A.S., and Charlton, M. (1998). Spatial nonstationarity and autoregressive models. Environment and Planning A, 30(6): 957-973.
連結：
[5] Chen, V.Y.-J., Deng, W.-S., Yang, T.C. and Matthews, S.A. (2012). Geographically Weighted Quantile Regression (GWQR): An Application to U.S. Mortality Data. Geographically Analysis, 44(2): 134-150.
連結：
[6] Chen, V.Y.-J., Wu, P.C., Yang, T.C. and Su, H.J. (2011). Examiningnonstationary effects of social determinants on cardiovascular mortality after cold surges in Taiwan. Science of The Total Environment, 408(9): 2042-2049.
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