Dissertations / Theses on the topic 'Spatial autoregressions'

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.

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.

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.

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.

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.

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.

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.

This article assesses the empirical relationship between per capita income growth fluctuations and the age-structured human capital variations across four groups of geographically clustered developed and developing countries from spatial perspective. We estimate a spatial Vector Autoregressive (VAR) model of income dynamics where the distance between countries is defined on relational space based on their similarity in appropriation tendency of human capital in the production processes. These distances are computed using a newly developed human capital data set which fully characterizes the demographic structure of human capital, and thus underlines the joint relevance of demography and human capital in economic growth. Spatial effects on growth interdependence and complementarity are then explored with respect to the proposed distance metrics. Our results imply that significant cross-country growth interdependence based on human capital distances exists among defined country groups suggesting the need for a cooperative policy programme among them. We also find that the relationship between economic growth and human capital is highly nonlinear as a function of the proposed human capital distance.

The big-box retail stores such as Wal-Mart and Target have become the focus of many studies researching their impacts on local economic outcomes. This dissertation studies three related topics: (i) the dynamic interrelationship among the presence of the big-box stores, retail wage, and employment, (ii) the impact of the big-box retailers on personal income growth, and (iii) the dynamic interrelationship between the presence of big-box retailers and personal income growth. The research draws important insights with potential implications for regional developers and policy makers. The first essay analyzes the dynamic relationship among the presence of the big-box retailers, retail wage, and employment at the county level for 1986-2005. A vector autoregression model is applied on panel data. Impulse response functions and variance decompositions are also presented. Results suggest that the presence of big-box stores decreases retail wages and increases retail employment. Retail employment has a higher impact on the retailers’ location decision than retail wage. The results also show that the presence of Wal-Mart drives the above-mentioned effects, while the presence of Target is insignificant. The second essay investigates the impact from the presence of big-box retailers on personal income growth in U.S. counties between 2000 and 2005 - based on neoclassical growth models of cross-country income convergence. Results suggest that counties having both Wal-Mart and Target stores experienced slower growth in personal income. After controlling for spatial autocorrelation, similar to the first essay, the effect of Wal-Mart’s presence on personal income growth is dominant in terms of statistical significance relative to Target’s. The third essay expands the second essay and investigates the dynamic interaction between the presence of big-box retailers and personal income growth over time at the county level for the period 1987-2005, using a panel vector autoregression model. For this analysis, the earning shares of natural resources and manufacturing sectors are included - assuming that all the variables are endogenous to one another. The findings indicate that big-box retailers negatively affect personal income growth, which is consistent with the second essay. However, personal income growth has an insignificant effect on the big-box retailers’ location decision.

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Soil land leveling is a technique used in low land areas and has the objective to improve agricultural use to facilitate the management of water both for irrigation and drainage operations, for the establishment of agricultural practices and crop harvest. However, it causes changes in the physical environment where the plant grows, and many studies have sought to identify the effect of this practice in the structure of soil spatial variability and in the relationship between the hydric-physical and chemical soil attributes. Thus, the objective of this study was to identify and characterize the structure of spatial variability of soil hydric-physical and chemical attributes of a low land soil, before and after land leveling, and to study the relationship between these soil attributes through an autoregressive state space model. In an experimental area of 0.81 ha belongs to Embrapa Clima Temperado situated in Capão do Leão county, state of Rio Grande do Sul, Brazil, was established a regular grid of 100 points spaced 10 m apart in both directions. At each point, soil disturbed and undisturbed samples were collected at the depth of 0-0.20 m to determine, before and after land leveling, the following soil attributes: clay, silt and sand contents, soil macroporosity, soil microporosity and soil total porosity, soil bulk density and soil water content at field capacity and permanent wilting point, soil organic carbon and cation exchange capacity. All data sets were organized into a spreadsheet in the form of a spatial transect consisting of 100 points and they were ordered following the gradient slope area resulting from the soil land leveling. Autocorrelograms and crosscorrelograms were built to evaluate the structure of spatial correlation of all soil attributes having served as a subsidy for the selection of variables in each autoregressive state-space model. The results show that the soil land leveling changed the structure of soil spatial dependence of all variables and between them as well. The soil cation exchange capacity and soil microporosity variables were the variables that made up the largest number of state space models, before and after soil land leveling. The contribution of the each variable at position i-1 to estimate its value at position increased to the sand content, silt content, soil bulk density, soil microporosity, soil macroporosity, soil water content at permanent wilting point, soil organic carbon and cation exchange capacity variables and decreased to soil water content at field capacity variable after land leveling. Soil land leveling improved the state space model performance for soil organic carbon content, sand content, soil bulk density, soil total porosity and soil water content at field capacity and permanent wilting point variables. The worst state space model performances, after soil land leveling, were found taking silt content, soil microporosity and cation exchange capacity variables as response variables. The best state space model performance, before land leveling, was obtained taking the soil total porosity as response variable.
A sistematização do solo é uma técnica utilizada em regiões planas, com características de várzea, e tem por objetivo aperfeiçoar o uso agrícola facilitando o manejo da água tanto de irrigação como de drenagem, as operações de implantação da lavoura, de tratos culturais e de colheita. No entanto, a sistematização do solo provoca alterações no ambiente físico onde a planta se desenvolve, sendo que muitos estudos têm buscado identificar o efeito dessa prática na estrutura de variabilidade espacial e no relacionamento entre os atributos físico-hídricos e químicos do solo. Dessa forma, o objetivo deste trabalho foi identificar e caracterizar a estrutura de variabilidade espacial dos atributos físico-hídricos e químicos de um solo de várzea, antes e depois da sistematização, assim como estudar o relacionamento entre esses atributos por meio de um modelo autoregressivo de espaço de estados. Em uma área experimental de 0,81 ha pertencente a Embrapa Clima Temperado, Capão do Leão-RS, foi estabelecida uma malha regular de 100 pontos, espaçados de 10 m entre si em ambas as direções. Em cada ponto foram coletadas amostras de solo deformadas e com estrutura preservada na profundidade de 0-0,20 m para a determinação, antes e depois da sistematização, dos teores de argila, silte e areia, macroporosidade, microporosidade e porosidade total, densidade do solo, conteúdo de água retido na capacidade de campo e ponto de murcha permanente, carbono orgânico e capacidade de troca de cátions. Os dados foram organizados em uma planilha de cálculo na forma de uma transeção espacial composta de 100 pontos e foram ordenados seguindo o gradiente de declividade da área resultante do processo de sistematização do solo. Para avaliar a estrutura de correlação espacial foram construídos autocorrelogramas e crosscorrelogramas que serviram de subsídio para a seleção de variáveis em cada um dos modelos autoregressivos de espaço de estados. Os resultados mostram que a sistematização do solo alterou a estrutura de dependência espacial tanto da variável como entre as variáveis deste estudo. A capacidade de troca de cátions e a microporosidade do solo foram as variáveis que compuseram o maior número de modelos de espaço de estados, antes e depois da sistematização. A contribuição da variável na posição i-1 na estimativa na posição i, por meio do modelo autoregressivo de espaço de estados, aumentou com a sistematização para as variáveis teor de areia, teor de silte, densidade do solo, microporosidade, macroporosidade, conteúdo de água no solo retido no ponto de murcha permanente, carbono orgânico e da capacidade de troca de cátions; e diminuiu para a variável conteúdo de água no solo retido na capacidade de campo.A sistematização do solo melhorou a estimativa, por meio dos modelos de espaço de estados, das variáveis carbono orgânico, teor de areia, densidade do solo, macroporosidade e do conteúdo de água no solo retido na capacidade de campo e no ponto de murcha permanente, sendo o modelo da variável porosidade total, antes da sistematização, que apresentou o melhor desempenho. Já os piores desempenhos dos modelos, depois da sistematização do solo, foram encontrados quando utilizadas as variáveis teor de silte, microporosidade e capacidade de troca de cátions como resposta.