Relevant bibliographies by topics / Spatial autoregressions

Academic literature on the topic 'Spatial autoregressions'

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Published: 9 March 2023
Last updated: 10 March 2023

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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 (June 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 (May 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 (September 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 does not rely on distributional assumptions and is robust to unknown heteroscedasticity. Its good finite-sample performance, in comparison with existing estimators that are also robust to heteroscedasticity, is demonstrated by a Monte Carlo study.
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Kelley Pace, R. "Performing large spatial regressions and autoregressions." Economics Letters 54, no. 3 (July 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 (April 27, 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 differences between directed and undirected networks are emphasized. The graph theoretic representation also clarifies why it is difficult to relate properties of W to correlation properties of SAR(1) models defined on irregular lattices.
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Robinson, Peter M., and Francesca Rossi. "Improved Lagrange multiplier tests in spatial autoregressions." Econometrics Journal 17, no. 1 (January 21, 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 (May 3, 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 behaviour of the estimates in a variety of situations. Our results are especially pertinent in situations where spatial weights are functions of stochastic economic variables, and this type of setting is also studied in our simulations.
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Jenish, Nazgul. "SPATIAL SEMIPARAMETRIC MODEL WITH ENDOGENOUS REGRESSORS." Econometric Theory 32, no. 3 (December 18, 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 estimator, the paper also establishes a stochastic equicontinuity criterion and functional central limit theorem for near-epoch dependent random fields.
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Griffith, Daniel A. "SIMPLIFYING THE NORMALIZING FACTOR IN SPATIAL AUTOREGRESSIONS FOR IRREGULAR LATTICES." Papers in Regional Science 71, no. 1 (January 14, 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 (January 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 finite sample corrections appears to be a crucially important task in order to obtain reliable tests. In this project we consider finite sample corrections based on formal Edgeworth expansions for the cumulative distribution function of some relevant test statistics. In Chapter 1 we provide the background for the results derived in this thesis. Specifically, we describe SAR models together with some established results in first order asymptotic theory for tests for independence in such models and give a brief account on Edgeworth expansions. In Chapters 2 and 3 we present refined procedures for testing nullity of the spatial parameter in pure SAR based on ordinary least squares and Gaussian maximum likelihood, respectively. In both cases, the Edgeworth-corrected tests are compared with those obtained by a bootstrap procedure, which is supposed to have similar properties. The practical performance of new tests is assessed with Monte Carlo simulations and two empirical examples. In Chapter 4 we propose finite sample corrections for Lagrange Multiplier statistics, which are computationally particularly convenient as the estimation of the spatial parameter is not required. Monte Carlo simulations and the numerical implementation of Imhof's procedure confirm that the corrected tests outperform standard ones. In Chapter 5 the slightly more general model known as \mixed" SAR is considered. We derive suitable finite sample corrections for standard tests when the parameters are estimated by ordinary least squares and instrumental variables. A Monte Carlo study again confirms that the new tests outperform ones based on the central limit theorem approximation in small and moderately-sized samples.
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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 thus the derivation of finite sample corrections appears to be a crucially important task in order to obtain reliable tests. In this project we consider finite sample corrections based on formal Edgeworth expansions for the cumulative distribution function of some relevant test statistics. In Chapters 1 and 2 we present refined procedures for testing nullity of the spatial parameter in pure SAR based on ordinary least squares and Gaussian maximum likelihood, respectively. In both cases, the Edgeworth-corrected tests are compared with those obtained by a bootstrap procedure, which is supposed to have similar properties. The practical performance of new tests is assessed with Monte Carlo simulations and two empirical examples. In Chapter 3 we propose finite sample corrections for Lagrange Multiplier statistics, which are computationally particularly convenient as the estimation of the spatial parameter is not required. Monte Carlo simulations and the numerical implementation of Imhof's procedure confirm that the corrected tests outperform standard ones.
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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.
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. Each of the proposed models are fit to hourly historical data from all of 2015 for 24 wind farms located in Sweden and Finland. Forecasts are generated for all of 2016 and are evaluated against historical data from that year for each of the 24 wind farms. The proposed models are successfully implemented into the .NET framework of Vitec Software’s Aiolos Forecast Studio, which is widely used in the Northern and Western Europe. Vitec’s Aiolos wind power forecast model is based on an ensemble of numerical weather prediction models and adaptive statistical machine learning algorithms. The proposed models are found to have significantly lower mean absolute error and root mean squared error compared to the Aiolos model and autoregressive model benchmarks. The improved short term wind power forecast will inform operation and trading decisions and translate to significant reductions in balancing costs for Vitecs customers. The improvement is valued at as much as between 9.4 million Euros to 42.3 million Euros in reduced balancing costs. Spatio-temporal aspects for wind power forecasting is shown to continue to be promising for improving current state-of-the-art wind power forecasting.
I detta arbete undersöks och implementeras autoregressiva modeller för vindkraftprognoser för en kort tidshorisont. Metoden tar hänsyn till samvariationer i tid och rum mellan olika vindkraftanläggningar och använder regimer som baseras på väderförhållanden för att förbättra prognoserna. Vi föreslår nya autoregressiva regimer, implementerar modellerna i .NET och utvärderar dem. Vektor autoregressiva modeller utnyttjar korrelationen mellan olika anläggningar genom att ta med information i närtid från andra anläggningar i samma region i modellen och på så vis förbättra prognoserna. Regimerna skapas med en klustermetod för K-medelvärde som baseras på väderförhållandena. Alla föreslagna modeller anpassas till historiska data för 2015 för 24 vindkraftanläggningar i Sverige och Finland. Prognoser skapas för 2016 och används för att utvärdera modellerna för var och en av de 24 anläggningarna. De föreslagna modellerna har implementerats i .NET i miljön för Vitecs Aiolos Forecast Studio, vilket är ett program som används av många operatörer i norra och västra Europa för att göra vindkraftprognoser. Aiolos modell baseras på en rad olika numeriska väderprognosmodeller och adaptiva statistiska maskinlärningsalgoritmer. De föreslagna modellerna visar sig ha lägre fel jämfört med Aiolos modell och andra autoregressiva modeller som använts som riktmärken. De förbättrade kortsiktiga vindkraftsprognoserna kommer vara underlag för operativa och finansiella beslut för Vitecs kunder och innebära betydande minskningar av balanskostnader. Förbättringen uppskattas kunna minska kostnaderna för Vitecs kunder med så mycket som mellan 9.4 miljoner och 42.3 miljoner Euro. Att utnyttja korrelationer mellan olika vindkraftanläggningar visar sig ha fortsatt stor betydelse för att förbättra vindkraftprognoser.
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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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8

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 relationship between counties that varies by business cycle phase, and provides policy implications from this relationship.
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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¬
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 independent variables vary among provinces. It is demonstrated that educational status and unemployment are significant factors of waste generation in Turkey.
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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 to be Gaussian distributed. This thesis introduces two new models, named PPCA-AR and ProbCCA-AR, that augment PPCA and ProbCCA respectively with autoregressive processes over the latent variables to additionally capture temporal relationships between the observations. To make PPCA-AR and ProbCCA-AR robust to outliers and able to model leptokurtic data, the Gaussian assumptions are replaced with infinite scale mixtures of Gaussians, using the Student-t distribution. Bayesian inference calculates posterior probability distributions for each of the parameter variables, from which we obtain a measure of confidence in the inference. It avoids the pitfalls associated with the maximum likelihood method: integrating over all possible values of the parameter variables guards against overfitting. For these new models the integrals required for exact Bayesian inference are intractable; instead a method of approximation, the variational Bayesian approach, is used. This enables the use of automatic relevance determination to estimate the model orders. PPCA-AR and ProbCCA-AR can be viewed as linear dynamical systems, so the forward-backward algorithm, also known as the Baum-Welch algorithm, is used as an efficient method for inferring the posterior distributions of the latent variables. The exact algorithm is tractable because Gaussian assumptions are made regarding the distribution of the latent variables. This thesis introduces a variational Bayesian forward-backward algorithm based on Student-t assumptions. The new models are demonstrated on synthetic datasets and on real remote sensing and EEG data.
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Books on the topic "Spatial autoregressions"

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Kazar, Baris M., and Mete Celik. Spatial AutoRegression (SAR) Model. Boston, MA: 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. Boston, MA: 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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Spacey parents: Spatial autoregressive patterns in inbound FDI. Cambridge, MA: 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, 129–61. Cham: 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, 1–5. Boston, MA: 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, 7–17. Boston, MA: 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, 19–33. Boston, MA: 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, 35–46. Boston, MA: 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, 47–50. Boston, MA: 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, 51–58. Boston, MA: 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, 59–60. Boston, MA: 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, 61–73. Boston, MA: 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, 1–11. Cham: 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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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. Fort Belvoir, VA: Defense Technical Information Center, February 2007. http://dx.doi.org/10.21236/ada463739.

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