Relevant bibliographies by topics / Spatial autoregressions

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spatial autoregressions'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Spatial autoregressions"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Spatial autoregressions"

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Rossi, Francesca. "Improved tests for spatial autoregressions." Thesis, London School of Economics and Political Science (University of London), 2011. http://etheses.lse.ac.uk/164/.

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Econometric modelling and statistical inference are considerably complicated by the possibility of correlation across data data recorded at different locations in space. A major branch of the spatial econometrics literature has focused on testing the null hypothesis of spatial independence in Spatial Autoregressions (SAR) and the asymptotic properties of standard test statistics have been widely considered. However, finite sample properties of such tests have received relatively little consideration. Indeed, spatial datasets are likely to be small or moderately-sized and thus the derivation of
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ROSSI, FRANCESCA. "Inference for spatial data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/25536.

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It is well known that econometric modelling and statistical inference are considerably complicated by the possibility of correlation across data data recorded at different locations in space. A major branch of the spatial econometrics literature has focused on testing the null hypothesis of spatial independence in Spatial Autoregressions (SAR) and the asymptotic properties of standard test statistics have been widely considered. However, finite sample properties of such tests have received relatively little consideration. Indeed, spatial datasets are likely to be small or moderately-sized and
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3

Xu, JiQiang. "Parameter estimation and interpretation in spatial autoregression models." Diss., Connect to online resource - MSU authorized users, 1998.

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Thesis (Ph. D.)--Michigan State University. Dept. of Counseling, Educational Psychology and Special Education, 1998.<br>Title from PDF t.p. (viewed on July 2, 2009) Includes bibliographical references (p. 148-149). Also issued in print.
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Oleson, Jacob J. "Bayesian spatial models for small area estimation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052203.

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Gilleran, Sean. "Online Regime Switching Vector Autoregression Incorporating Spatio-temporal Aspects for Short Term Wind Power Forecasting." Thesis, KTH, Elkraftteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217117.

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This master thesis examines short term wind power forecasting time series models focusing on regimes conditioned to meteorological conditions and the incorporation of spatio-temporal aspects. Novel regime switching autoregressive and vector autoregressive models are proposed, implemented in a .NET framework, and evaluated. The vector autoregressive framework takes advantage of cross-correlation between sites incorporating upstream online production information from all wind farms within a given region. The regimes are formed using K-means clustering based on forecast meteorological conditions.
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Yang, Kai. "Essays on Multivariate and Simultaneous Equations Spatial Autoregressive Models." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461277549.

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Woodard, Roger. "Bayesian hierarchical models for hunting success rates /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9951135.

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Peterson, Samuel. "Spatial and Temporal Employment Relationships: Southern California as a Case Study." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/cmc_theses/1813.

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Southern California is the largest U.S. metropolitan area geographically, and demonstrates complex spatial relationships between county labor markets. This paper is interested in investigating the employment dependencies between the core city of Los Angeles its respective commuting sheds, such as San Bernardino and Riverside counties. Using time series data that includes labor demand shocks from the Great Recession, this analysis implements a vector autoregressive model to dissect the relationship between urban and suburban employment changes. The work finds a strong lagging-leading relationsh
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Keser, Saniye. "Investigation Of The Spatial Relationship Of Municipal Solid Waste Generation In Turkey With Socio-economic, Demographic And Climatic Factors." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611575/index.pdf.

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This thesis investigates the significant factors affecting municipal solid waste (MSW) generation in Turkey. For this purpose, both spatial and non-spatial tech&not<br>niques are utilized. Non-spatial technique is ordinary least squares (OLS) regression while spatial techniques employed are simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR). The independent variables include socio-economic, demographic and climatic indicators. The results show that nearer provinces tend to have similar solid waste generation rate. Moreover, it is shown that the effects of in
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Christmas, Jacqueline. "Robust spatio-temporal latent variable models." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3051.

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Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed
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Books on the topic "Spatial autoregressions"

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Kazar, Baris M., and Mete Celik. Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9.

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Mete, Celik, and SpringerLink (Online service), eds. Spatial AutoRegression (SAR) Model: Parameter Estimation Techniques. Springer US, 2012.

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Kazar, Baris M., and Mete Celik. Spatial AutoRegression Model: Parameter Estimation Techniques. Springer, 2012.

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4

Spacey parents: Spatial autoregressive patterns in inbound FDI. National Bureau of Economic Research, 2005.

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Iimi, Atsushi, Liangzhi You, Ulrike Wood-Sichra, and Richard Martin Humphrey. Agriculture Production and Transport Infrastructure in East Africa: An Application of Spatial Autoregression. The World Bank, 2015. http://dx.doi.org/10.1596/1813-9450-7281.

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Book chapters on the topic "Spatial autoregressions"

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Beenstock, Michael, and Daniel Felsenstein. "Spatial Vector Autoregressions." In Advances in Spatial Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03614-0_6.

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Kazar, Baris M., and Mete Celik. "Introduction." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_1.

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Kazar, Baris M., and Mete Celik. "Theory behind the SAR Model." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_2.

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Kazar, Baris M., and Mete Celik. "Parallel Exact SAR Model Solutions." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_3.

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Kazar, Baris M., and Mete Celik. "Comparing Exact and Approximate SAR Model Solutions." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_4.

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Kazar, Baris M., and Mete Celik. "Parallel Implementations of Approximate SAR Model Solutions." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_5.

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Kazar, Baris M., and Mete Celik. "A New Approximation: Gauss-Lanczos Approximated SAR Model Solution." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_6.

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Kazar, Baris M., and Mete Celik. "Conclusions and Future Work." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_7.

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Kazar, Baris M., and Mete Celik. "Supplementary Materials." In Spatial AutoRegression (SAR) Model. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-1842-9_8.

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Thayn, Jonathan B. "Eigenvector Spatial Filtering and Spatial Autoregression." In Encyclopedia of GIS. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23519-6_1526-1.

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Conference papers on the topic "Spatial autoregressions"

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Tian, Wenxin, Shaohui Li, Wenrui Dai, et al. "Learned Progressive Image Compression With Spatial Autoregression." In 2023 IEEE International Conference on Visual Communications and Image Processing (VCIP). IEEE, 2023. http://dx.doi.org/10.1109/vcip59821.2023.10402767.

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Dewan, Pranita, Raghu Ganti, Mudhakar Srivatsa, and Sebastian Stein. "NN-SAR: A Neural Network Approach for Spatial AutoRegression." In 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). IEEE, 2019. http://dx.doi.org/10.1109/percomw.2019.8730574.

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Reports on the topic "Spatial autoregressions"

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Celik, Mete, Baris M. Kazar, Shashi Shekhar, Daniel Boley, and David J. Lilja. NORTHSTAR: A Parameter Estimation Method for the Spatial Autoregression Model. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada463739.

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