Spatial Fixed Effects and Spatial Dependence

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification re- moves spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effects may be spurious when the true DGP takes the form of a spatial lag or spatial error dependence. In addition, we also show that only in the special case where the dependence is group-wise, with all observations in the same group as neighbors of each other, do spatial fixed effects correctly remove spatial correlation.spatial autocorrelation, spatial econometrics, spatial externalities, spatial fixed effects, spatial interaction, spatial weights

Integrating spatial dependence into stochastic frontier analysis

An approach to incorporate spatial dependence into Stochastic Frontier analysis is developed and applied to a sample of 215 dairy farms in England and Wales. A number of alternative specifications for the spatial weight matrix are used to analyse the effect of these on the estimation of spatial dependence. Estimation is conducted using a Bayesian approach and results indicate that spatial dependence is present when explaining technical inefficiency.Spatial dependence, technical efficiency, Bayesian, spatial weight matrix

Directional Spatial Dependence and Its Implications for Modeling Systemic Yield Risk

The objective of this study is to evaluate and model the spatial dependence of systemic yield risk. Various spatial autoregressive models are explored to account for county level dependence of crop yields. The results show that the time trend parameters of yields are correlated across spaces and the spatial correlations are changing with time. In addition, the spatial correlation of neighborhood in west/east direction is stronger than that of north/south direction. The information of the spatial dependence of yield risk will help the construction of better risk management programs for protecting producers from systemic yield risks.Spatial Autoregressive Model, Spatial Dependence, Risk and Uncertainty,

SPACE-TIME LAGS: SPECIFICATION STRATEGY IN SPATIAL REGRESSION MODELS

he purpose of this article is to analyse the dynamic trend of spatial dependence, which is not only contemporary but time-lagged in many socio-economic phenomena. Firstly, we show some of the commonly used exploratory spatial data analysis (ESDA) techniques and we propose other new ones, the exploratory space-time data analysis (ESTDA) that evaluates the instantaneity of spatial dependence. We also propose the space-time correlogram as an instrument for a better specification of spatial lag models, which should include both kind of spatial dependence. Some applications with economic data for Spanish provinces shed some light upon these issues.Spatial dependence, spatial diffusion, ESDA, correlogram, Spanish provinces

Dundee Discussion Papers in Economics 253:Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales

Large Panels with Common Factors and Spatial Correlations

This paper considers the statistical analysis of large panel data sets where even after conditioning on common observed effects the cross section units might remain dependently distributed. This could arise when the cross section units are subject to unobserved common effects and/or if there are spill over effects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the concepts of time-specific weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight matrix

MODELING SPATIAL DEPENDENCE AND SPATIAL HETEROGENEITY IN COUNTY YIELD FORECASTING MODELS

The implications of ignoring potential spatial dependence in county-level yield data are discussed. Spatial dependence in a county-level yield data set is identified and methods for correcting the dependence via spatial weighting matrices and generalized least squares regression are performed. The paper also examines how the spatial dependence declines as the distance between observations increases.Productivity Analysis, Research Methods/ Statistical Methods,

Large Panels with Common Factors and Spatial Correlations

This paper considers the statistical analysis of large panel data sets where even after condi-tioning on common observed effects the cross section units might remain dependently distrib-uted. This could arise when the cross section units are subject to unobserved common effects and/or if there are spill over effects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the con-cepts of time-specific weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight matrix.panels, Common Correlated Effects, strong and weak cross section dependence

Spatial probit and the geographic patterns of state lotteries

We implement a spatial probit model to differentiate states with a lottery from those without a lottery. Our analysis extends the basic spatial probit model by allowing spatial dependence to vary across geographic regions. We also separate the spatial effects of neighbors versus non-neighbors. The methodology provides consistent and efficient coefficient estimation in light of the simultaneity in spatial dependence. We find evidence of spatial dependence and spatial heterogeneity in lottery usage, and we find that spatial patterns differ significantly by geographic region. The importance of spatial dependence in state lottery usage suggests the need to consider spatial effects in empirical models examining the use of any policy tool by subnational governmental units.Regional economics