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Journal articles on the topic 'Spatial autoregressions'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Beenstock, Michael, and Daniel Felsenstein. "Spatial Vector Autoregressions." Spatial Economic Analysis 2, no. 2 (2007): 167–96. http://dx.doi.org/10.1080/17421770701346689.

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2

Kelley Pace, R., and Ronald Barry. "Sparse spatial autoregressions." Statistics & Probability Letters 33, no. 3 (1997): 291–97. http://dx.doi.org/10.1016/s0167-7152(96)00140-x.

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3

Bao, Yong, Xiaotian Liu, and Lihong Yang. "Indirect Inference Estimation of Spatial Autoregressions." Econometrics 8, no. 3 (2020): 34. http://dx.doi.org/10.3390/econometrics8030034.

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The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the properly recentered OLS estimator and proposes a new estimator that is based on the indirect inference (II) procedure. The resulting estimator can always be used regardless of the degree of aggregate influence on each spatial unit from other units and is consistent and asymptotically normal. The new estimator

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4

Kelley Pace, R. "Performing large spatial regressions and autoregressions." Economics Letters 54, no. 3 (1997): 283–91. http://dx.doi.org/10.1016/s0165-1765(97)00026-8.

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5

Martellosio, Federico. "THE CORRELATION STRUCTURE OF SPATIAL AUTOREGRESSIONS." Econometric Theory 28, no. 6 (2012): 1373–91. http://dx.doi.org/10.1017/s0266466612000175.

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This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation parameter. A graph theoretic representation of the covariances in terms of walks connecting the spatial units helps to clarify a number of correlation properties of the processes. In particular, we study some implications of row-standardizing the weights matrix, the dependence of the correlations on graph distance, and the behavior of the correlations at the extremes of the parameter space. Throughout the analysis differen

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6

Robinson, Peter M., and Francesca Rossi. "Improved Lagrange multiplier tests in spatial autoregressions." Econometrics Journal 17, no. 1 (2014): 139–64. http://dx.doi.org/10.1111/ectj.12025.

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7

Gupta, Abhimanyu. "ESTIMATION OF SPATIAL AUTOREGRESSIONS WITH STOCHASTIC WEIGHT MATRICES." Econometric Theory 35, no. 2 (2018): 417–63. http://dx.doi.org/10.1017/s0266466618000142.

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We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weight matrices. Allowing a general spatial linear process form for the disturbances that permits many common types of error specifications as well as potential ‘long memory’, we provide sufficient conditions for consistency and asymptotic normality of instrumental variables, ordinary least squares, and pseudo maximum likelihood estimates. The implications of popular weight matrix normalizations and structures for our theoretical conditions are discussed. A set of Monte Carlo simulations examines the

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8

Jenish, Nazgul. "SPATIAL SEMIPARAMETRIC MODEL WITH ENDOGENOUS REGRESSORS." Econometric Theory 32, no. 3 (2014): 714–39. http://dx.doi.org/10.1017/s0266466614000905.

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This paper proposes a semiparametric generalized method of moments estimator (GMM) estimator for a partially parametric spatial model with endogenous spatially dependent regressors. The finite-dimensional estimator is shown to be consistent and root-n asymptotically normal under some reasonable conditions. A spatial heteroscedasticity and autocorrelation consistent covariance estimator is constructed for the GMM estimator. The leading application is nonlinear spatial autoregressions, which arise in a wide range of strategic interaction models. To derive the asymptotic properties of the estimat

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9

Griffith, Daniel A. "SIMPLIFYING THE NORMALIZING FACTOR IN SPATIAL AUTOREGRESSIONS FOR IRREGULAR LATTICES." Papers in Regional Science 71, no. 1 (2005): 71–86. http://dx.doi.org/10.1111/j.1435-5597.1992.tb01749.x.

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10

Griffith, Daniel A. "Simplifying the normalizing factor in spatial autoregressions for irregular lattices." Papers in Regional Science 71, no. 1 (1992): 71–86. http://dx.doi.org/10.1007/bf01538661.

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11

Nguyen, Hien D., Geoffrey J. McLachlan, Jeremy F. P. Ullmann, and Andrew L. Janke. "Spatial clustering of time series via mixture of autoregressions models and Markov random fields." Statistica Neerlandica 70, no. 4 (2016): 414–39. http://dx.doi.org/10.1111/stan.12093.

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12

Gutiérrez, David, and Rocio Salazar-Varas. "Using eigenstructure decompositions of time-varying autoregressions in common spatial patterns-based EEG signal classification." Biomedical Signal Processing and Control 7, no. 6 (2012): 622–31. http://dx.doi.org/10.1016/j.bspc.2012.03.004.

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13

Fingleton, Bernard. "Spatial Autoregression." Geographical Analysis 41, no. 4 (2009): 385–91. http://dx.doi.org/10.1111/j.1538-4632.2009.00765.x.

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14

Barbosa, S. M., M. E. Silva, and M. J. Fernandes. "Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry." Nonlinear Processes in Geophysics 13, no. 2 (2006): 177–84. http://dx.doi.org/10.5194/npg-13-177-2006.

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Abstract. This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a generalisation of Principal Oscillation Pattern (POP) analysis, widely used in the geosciences for the extraction of dynamical modes by eigen-decomposition of a first order autoregressive model fitt

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15

Kyriacou, Maria, Peter C. B. Phillips, and Francesca Rossi. "Indirect inference in spatial autoregression." Econometrics Journal 20, no. 2 (2017): 168–89. http://dx.doi.org/10.1111/ectj.12084.

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16

Bhattacharyya, B. B., J. J. Ren, G. D. Richardson, and J. Zhang. "Spatial autoregression model: strong consistency." Statistics & Probability Letters 65, no. 2 (2003): 71–77. http://dx.doi.org/10.1016/j.spl.2003.07.004.

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17

DUBIN, ROBIN, KELLEY PACE, and THOMAS THIBODEAU. "Spatial Autoregression Techniques for Real Estate Data." Journal of Real Estate Literature 7, no. 1 (1999): 79–95. http://dx.doi.org/10.1080/10835547.1999.12090079.

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18

Goryainov, V. B. "M-estimates of the spatial autoregression coefficients." Automation and Remote Control 73, no. 8 (2012): 1371–79. http://dx.doi.org/10.1134/s0005117912080103.

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19

Ma, Chunsheng. "Spatial autoregression and related spatio-temporal models." Journal of Multivariate Analysis 88, no. 1 (2004): 152–62. http://dx.doi.org/10.1016/s0047-259x(03)00067-8.

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20

Goryainov, V. B. "Least-modules estimates for spatial autoregression coefficients." Journal of Computer and Systems Sciences International 50, no. 4 (2011): 565–72. http://dx.doi.org/10.1134/s1064230711040101.

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21

Xu, Ke, Luping Sun, Jin Liu, Xuening Zhu, and Hansheng Wang. "A spatial autoregression model with time-varying coefficients." Statistics and Its Interface 13, no. 2 (2020): 261–70. http://dx.doi.org/10.4310/sii.2020.v13.n2.a10.

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22

Goryainov, V. B. "Identification of a spatial autoregression by rank methods." Automation and Remote Control 72, no. 5 (2011): 975–88. http://dx.doi.org/10.1134/s0005117911050067.

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23

Long, Dan S. "Spatial autoregression modeling of site-specific wheat yield." Geoderma 85, no. 2-3 (1998): 181–97. http://dx.doi.org/10.1016/s0016-7061(98)00019-6.

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24

Huang, Danyang, Xiangyu Chang, and Hansheng Wang. "Spatial autoregression with repeated measurements for social networks." Communications in Statistics - Theory and Methods 47, no. 15 (2017): 3715–27. http://dx.doi.org/10.1080/03610926.2017.1361989.

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25

Wang, Huiwen, Jie Gu, Shanshan Wang, and Gilbert Saporta. "Spatial partial least squares autoregression: Algorithm and applications." Chemometrics and Intelligent Laboratory Systems 184 (January 2019): 123–31. http://dx.doi.org/10.1016/j.chemolab.2018.12.001.

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26

Pietrzak, Michał Bernard. "Interpretation of Structural Parameters for Models with Spatial Autoregression." Equilibrium 8, no. 2 (2013): 129–55. http://dx.doi.org/10.12775/equil.2013.010.

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The main purpose of the article is to consider a important issue of spatial econometrics, which is a proper interpretation of structural parameters of econometric models with spatial autoregression. The problem will be considered basing on the example of the spatial SAR model. Another purpose of the article is to make an overview of measures of average spatial impact proposed by the subject literature (see Lesage and Pace 2009). The analysis will include such measures as Average Total Impact to an Observation, Average Total Impact from an Observation, Average Indirect Impact to an Observation,

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27

Peng, Xiaozhi, Hecheng Wu, and Ling Ma. "A study on geographically weighted spatial autoregression models with spatial autoregressive disturbances." Communications in Statistics - Theory and Methods 49, no. 21 (2019): 5235–51. http://dx.doi.org/10.1080/03610926.2019.1615507.

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28

Naumov, Ilya V., and Anna Z. Barybina. "The Spatial Autoregression Model of Innovative Development of Russian Regions." Vestnik Tomskogo gosudarstvennogo universiteta. Ekonomika, no. 52 (2020): 215–32. http://dx.doi.org/10.17223/19988648/52/13.

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This work examines the spatial heterogeneity of the innovative development of regional systems and forms a spatial autoregressive model that establishes the factors of its formation and stable inter-regional relationships in innovative development. The article presents a methodological toolkit for constructing a spatial autoregressive model for the innovative development of regional systems, which involves spatial analysis of data using the segmentation of regions by the level of innovative activity and the amount of funding, provision of territories with research personnel, and development of

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29

Serkov, L. A., and K. B. Kozhov. "Interregional Distribution of Energy Potential Based on Spatial Autoregression." Zhurnal Economicheskoj Teorii 17, no. 4 (2020): 799–810. http://dx.doi.org/10.31063/2073-6517/2020.17-4.5.

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The article proposes a methodological approach for assessing the conditions of interregional interaction of Russian regions in terms of energy conditions. To this end, we substantiate and analyze the spatial distribution of Russian regions’ energy potential. An integral index of energy potential is constructed, which characterizes the main energy and economic factors of regional development in Russia. To calculate the index, we used the statistical data from the Russian Federal Statistics Service (Rosstat) and departmental organizations for 84 regions. The energy potential is calculated by usi

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30

Bhattacharyya, B. B., G. D. Richardson, and L. A. Franklin. "Asymptotic inference for near unit roots in spatial autoregression." Annals of Statistics 25, no. 4 (1997): 1709–24. http://dx.doi.org/10.1214/aos/1031594738.

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31

Baran, Sándor, Gyula Pap, and Martien C. A. van Zuijlen. "Asymptotic Inference for Unit Roots in Spatial Triangular Autoregression." Acta Applicandae Mathematicae 96, no. 1-3 (2007): 17–42. http://dx.doi.org/10.1007/s10440-007-9097-y.

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32

Mohapl, Jaroslav. "On Maximum Likelihood Estimation for Gaussian Spatial Autoregression Models." Annals of the Institute of Statistical Mathematics 50, no. 1 (1998): 165–86. http://dx.doi.org/10.1023/a:1003457632479.

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33

Knafl, George J., Kathleen A. Knafl, and Ruth McCorkle. "Mixed models incorporating intra-familial correlation through spatial autoregression." Research in Nursing & Health 28, no. 4 (2005): 348–56. http://dx.doi.org/10.1002/nur.20082.

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34

Liu, Kongling, Mengjun Wang, Jianchang Li, et al. "Developing a Framework for Spatial Effects of Smart Cities Based on Spatial Econometrics." Complexity 2021 (June 11, 2021): 1–8. http://dx.doi.org/10.1155/2021/9322112.

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The rapid urbanization in China has already put heavy pressures on imperfect infrastructure, especially for fundamental urban functions such as power and water supply, traffic, education, and healthcare. The emergence of smart cities can help cope with the rapidly expanding demands on urban infrastructure. However, the development of smart cities in China is just in its infancy, and there is still a lack of clear understanding of the development path of smart cities. This article focuses on the development of smart cities in China. It aims to (a) judge whether there is spatial autoregression i

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35

Rahmadeni, Rahmadeni, and Rahima Dina. "Model Spatial Autoregressive pada Tingkat Angka Kematian Korban Covid-19 di Provinsi Riau." Zeta - Math Journal 9, no. 1 (2024): 50–59. http://dx.doi.org/10.31102/zeta.2024.9.1.50-59.

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Riau province has the most cases on Sumatra island and has more COVID 19 cases, the third province in Indonesia. Spread of COVID 19 disease in spatial regression analysis with multiple Lagrange spatial lags to determine the dependence of spatial lags using spatial autoregression. This method can identify spatial autocorrelation in the spread pattern of COVID 19 disease in the county as well as determine the cause of COVID 19 mortality. Results of autoregression analysis According to space, there are three variables that significantly affect the COVID 19 mortality rate: poverty, unemployment an

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36

Ju, Yuanyuan, Yan Yang, Mingxing Hu, Lin Dai, and Liucang Wu. "Bayesian Influence Analysis of the Skew-Normal Spatial Autoregression Models." Mathematics 10, no. 8 (2022): 1306. http://dx.doi.org/10.3390/math10081306.

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In spatial data analysis, outliers or influential observations have a considerable influence on statistical inference. This paper develops Bayesian influence analysis, including the local influence approach and case influence measures in skew-normal spatial autoregression models (SSARMs). The Bayesian local influence method is proposed to evaluate the impact of small perturbations in data, the distribution of sampling and prior. To measure the extent of different perturbations in SSARMs, the Bayes factor, the ϕ-divergence and the posterior mean distance are established. A Bayesian case influen

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37

Bhattacharyya, B. B., T. M. Khalil, and G. D. Richardson. "Gauss-Newton estimation of parameters for a spatial autoregression model." Statistics & Probability Letters 28, no. 2 (1996): 173–79. http://dx.doi.org/10.1016/0167-7152(95)00114-x.

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38

Liu, Guochang, Xiaohong Chen, Jing Du, and Kailong Wu. "Random noise attenuation using f-x regularized nonstationary autoregression." GEOPHYSICS 77, no. 2 (2012): V61—V69. http://dx.doi.org/10.1190/geo2011-0117.1.

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We have developed a novel method for random noise attenuation in seismic data by applying regularized nonstationary autoregression (RNA) in the frequency-space ([Formula: see text]) domain. The method adaptively predicts the signal with spatial changes in dip or amplitude using [Formula: see text] RNA. The key idea is to overcome the assumption of linearity and stationarity of the signal in conventional [Formula: see text] domain prediction technique. The conventional [Formula: see text] domain prediction technique uses short temporal and spatial analysis windows to cope with the nonstationary

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39

Furková, Andrea, and Peter Knížat. "Beyond Parametric Bounds: Exploring Regional Unemployment Patterns Using Semiparametric Spatial Autoregression." Business Systems Research Journal 15, no. 2 (2024): 48–66. http://dx.doi.org/10.2478/bsrj-2024-0017.

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Abstract Background It is a well-known phenomenon that nonlinearities that are inherent in the relationship among economic variables negatively affect the commonly used estimators in the econometric models. The nonlinearities cause an instability of the estimated parameters that, in particular, are unable to capture a local relationship between the response and the covariate. Objectives The main aim of the paper is the simultaneous consideration of spatial effects as well as nonlinearities through an advanced semiparametric spatial autoregressive econometric model. The paper seeks to contribut

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40

Zhang, Jinping, Qiuru Lu, Li Guan, and Xiaoying Wang. "Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China." Energies 14, no. 2 (2021): 504. http://dx.doi.org/10.3390/en14020504.

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This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating Moran’sI and local indicators of spatial association (LISA) of energy efficiency of regions in mainland China, we found that energy efficiency shows obvious spatial autocorrelation and spatial clustering phenomena. Secondly, we established the spatial quantile autoregression (SQAR) model, in which the energy efficiency is the response variable with seven influence factors. The seven factors include industrial structure, resource endowment, level of economic development etc. Based

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41

Martins, Emilia P. "Phylogenies, Spatial Autoregression, and the Comparative Method: A Computer Simulation Test." Evolution 50, no. 5 (1996): 1750. http://dx.doi.org/10.2307/2410733.

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42

Arató, M., G. Pap, and M. C. A. van Zuijlen. "Asymptotic inference for spatial autoregression and orthogonality of Ornstein-Uhlenbeck sheets." Computers & Mathematics with Applications 42, no. 1-2 (2001): 219–29. http://dx.doi.org/10.1016/s0898-1221(01)00146-8.

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43

Martins, Emília P. "PHYLOGENIES, SPATIAL AUTOREGRESSION, AND THE COMPARATIVE METHOD: A COMPUTER SIMULATION TEST." Evolution 50, no. 5 (1996): 1750–65. http://dx.doi.org/10.1111/j.1558-5646.1996.tb03562.x.

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44

Malikov, Emir, Yiguo Sun, and Diane Hite. "(Under)Mining local residential property values: A semiparametric spatial quantile autoregression." Journal of Applied Econometrics 34, no. 1 (2018): 82–109. http://dx.doi.org/10.1002/jae.2655.

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45

Lu, Yin, Chunbai Tao, Di Wang, Gazi Salah Uddin, Libo Wu, and Xuening Zhu. "Robust estimation for dynamic spatial autoregression models with nearly optimal rates." Journal of Econometrics 251 (September 2025): 106065. https://doi.org/10.1016/j.jeconom.2025.106065.

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46

Li, Hong, and Yang Lu. "COHERENT FORECASTING OF MORTALITY RATES: A SPARSE VECTOR-AUTOREGRESSION APPROACH." ASTIN Bulletin 47, no. 2 (2017): 563–600. http://dx.doi.org/10.1017/asb.2016.37.

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AbstractThis paper proposes a spatial-temporal autoregressive model for the mortality surface, where mortality rates of each age depend on the historical values of itself (temporality) and the neighbouring ages (spatiality). The mortality dynamics is formulated as a large, first order vector autoregressive model which encompasses standard factor models such as the Lee and Carter (1992) model. Sparsity and smoothness constraints are then introduced, based on the idea that the nearer the two ages, the more important the dependence between mortalities at these ages. Our model has several noveltie

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47

Goryainov, V. B., and E. R. Goryainova. "Nonparametric identification of the spatial autoregression model under a priori stochastic uncertainty." Automation and Remote Control 71, no. 2 (2010): 198–208. http://dx.doi.org/10.1134/s0005117910020049.

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48

Roknossadati, S. M., and M. Zarepour. "M-estimation for near unit roots in spatial autoregression with infinite variance." Statistics 45, no. 4 (2010): 337–48. http://dx.doi.org/10.1080/02331881003768792.

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49

Ren, Yimeng, Zhe Li, Xuening Zhu, Yuan Gao, and Hansheng Wang. "Distributed estimation and inference for spatial autoregression model with large scale networks." Journal of Econometrics 238, no. 2 (2024): 105629. http://dx.doi.org/10.1016/j.jeconom.2023.105629.

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50

Jimenez, J. C., R. Biscay, and O. Montoto. "Modeling the electroencephalogram by means of spatial spline smoothing and temporal autoregression." Biological Cybernetics 72, no. 3 (1995): 249–59. http://dx.doi.org/10.1007/bf00201488.

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