On Semiparametrically Dynamic Functional-Coefficient Autoregressive Spatio-Temporal Models with Irregular Location Wide Nonstationarity

References

• Al-Sulami, D., Jiang, Z., Lu, Z., and Zhu, J. (2017), “Estimation for Semiparametric Nonlinear Regression of Irregularly Located Spatial Time-Series Data,” Econometrics and Stats, 2, 22–35. DOI: 10.1016/j.ecosta.2017.01.002.
   Google Scholar
• Anselin, L. (1988), Spatial Econometrics: Methods and Models, Dordrecht: Springer.
   Google Scholar
• Banfi, S., Filippini, M., and Hunt, L. C. (2005), “Fuel Tourism in Border Regions: The Case of Switzerland,” Energy Economics, 27, 689–707. DOI: 10.1016/j.eneco.2005.04.006.
   Web of Science ®Google Scholar
• Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2015), Time Series Analysis: Forecasting and Control, Hoboken, NJ: Wiley.
   Google Scholar
• Cai, Z., Fan, J., and Yao, Q. (2000), “Functional-Coefficient Regression Models for Nonlinear Time Series,” Journal of the American Statistical Association, 95, 941–956. DOI: 10.1080/01621459.2000.10474284.
   Web of Science ®Google Scholar
• Chen, R., and Tsay, R. S. (1993), “Functional-Coefficient Autoregressive Models,” Journal of the American Statistical Association, 88, 298–308. DOI: 10.2307/2290725.
   Web of Science ®Google Scholar
• Clerides, S., (2010), “Retail Fuel Price Response to Oil Price Shocks in EU Countries,” Cyprus Economic Policy Review, 4, 25–45.
   Google Scholar
• Cliff, A., and Ord, K. (1972), “Testing for Spatial Autocorrelation among Regression Residuals,” Geographical Analysis, 4, 267–284. DOI: 10.1111/j.1538-4632.1972.tb00475.x.
   Web of Science ®Google Scholar
• Comber, A., and Wulder, M. (2019), “Considering Spatiotemporal Processes in Big Data Analysis: Insights from Remote Sensing of Land Cover and Land Use,” Transactions in GIS, 23, 879–891. DOI: 10.1111/tgis.12559.
   Web of Science ®Google Scholar
• Cressie, N., (1993), Statistics For Spatial Data, New York: Wiley.
   Google Scholar
• Cressie, N., and Wikle, C. K. (2015), Statistics for Spatio-Temporal Data, Hoboken, NJ: Wiley.
   Google Scholar
• Fan, J., and Gijbels, I. (1996), Local Polynomial Modelling and Its Applications. Monographs on Statistics and Applied Probability 66. Boca Ratoon, FL: CRC Press.
   Google Scholar
• Fan, J., and Yao, Q. (2003), Nonlinear Time Series: Nonparametric and Parametric Methods, New York: Springer.
   Google Scholar
• Fan, J., and Zhang, W. (1999), “Statistical Estimation in Varying Coefficient Models,” The Annals of Statistics, 27, 1491–1518. DOI: 10.1214/aos/1017939139.
   Web of Science ®Google Scholar
• Gao, J. (2007), Nonlinear Time Series: Semiparametric and Nonparametric Methods, Boca Raton, FL: CRC Press.
   Google Scholar
• Gao, J., Lu, Z., and Tjøstheim, D. (2006), “Estimation in Semiparametric Spatial Regression,” The Annals of Statistics, 34, 1395–1435. DOI: 10.1214/009053606000000317.
   Web of Science ®Google Scholar
• Gelfand, A. E., Kim, H. J., Sirmans, C., and Banerjee, S. (2003), “Spatial Modeling with Spatially Varying Coefficient Processes,” Journal of the American Statistical Association, 98, 387–396. DOI: 10.1198/016214503000170.
   Web of Science ®Google Scholar
• Hallin, M., Lu, Z., and Tran, L. T. (2004), “Local Linear Spatial Regression,” The Annals of Statistics, 32, 2469–2500. DOI: 10.1214/009053604000000850.
   Web of Science ®Google Scholar
• LeSage, J., and Pace, R. K. (2009), Introduction to Spatial Econometrics, Boca Raton, FL: CRC Press.
   Google Scholar
• Lu, Z., Steinskog, D. J., Tjøstheim, D., and Yao, Q. (2009), “Adaptively Varying-Coefficient Spatiotemporal Models,” Journal of the Royal Statistical Society, Series B, 71, 859–880. DOI: 10.1111/j.1467-9868.2009.00710.x.
   Google Scholar
• Lu, Z., Tjøstheim, D., and Yao, Q. (2008), “Smoothing, Nugget Effect and Infill Asymptotics,” Statistics and Probability Letters, 78, 3145–3151. DOI: 10.1016/j.spl.2008.06.002.
   Web of Science ®Google Scholar
• Ord, K., (1975), “Estimation Methods for Models of Spatial Interaction,” Journal of the American Statistical Association, 70, 120–126. DOI: 10.1080/01621459.1975.10480272.
   Web of Science ®Google Scholar
• Subba Rao, S., (2008), “Statistical Analysis of a Spatio-Temporal Model with Location-Dependent Parameters and a Test for Spatial Stationarity,” Journal of Time Series Analysis, 29, 673–694. DOI: 10.1111/j.1467-9892.2008.00577.x.
   Web of Science ®Google Scholar
• Sun, Y., Yan, H., Zhang, W., and Lu, Z. (2014), “A Semiparametric Spatial Dynamic Model,” Annals of Statistics, 42, 700–727.
   Web of Science ®Google Scholar
• Teräsvirta, T., Tjøstheim, D., and Granger, C. (2010), Modelling Nonlinear Economic Time Series, Oxford: Oxford University Press.
   Google Scholar
• Tong, H., (1990), Non-linear Time Series: A Dynamical System Approach, Oxford: Oxford University Press.
   Google Scholar
• Wlazlowski, S., Giulietti, M., Binner, J., and Milas, C. (2009), “Price Dynamics in European Petroleum Markets,” Energy Economics, 31, 99–108. DOI: 10.1016/j.eneco.2008.08.009.
   Web of Science ®Google Scholar
• Zhang, X., and Yu, J. (2018), “Spatial Weights Matrix Selection and Model Averaging for Spatial Autoregressive Models,” Journal of Econometrics, 203, 1–18. DOI: 10.1016/j.jeconom.2017.05.021.
   Web of Science ®Google Scholar