Spatial econometric methods deal with the incorporation of spatial interaction and spatial structure into regression analysis. The field has seen a recent and rapid growth spurred both by theoretical concerns as well as by the need to be able to apply econometric models to emerging large geocoded data bases. The review presented in this chapter outlines the basic terminology and discusses in some detail the specification of spatial effects, estimation of spatial regression models, and specification tests for spatial effects.

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This paper reviews a number of conceptual issues pertaining to the implementation of an explicit “spatial ” perspective in applied econometrics. It provides an overview of the motivation for including spatial effects in regression models, both from a theory-driven as well as from a data-driven perspective. Considerable attention is paid to the inferential framework necessary to carry out estimation and testing and the different assumptions, constraints and implications embedded in the various specifications available in the literature. The review combines insights from the traditional spatial econometrics literature as well as from geostatistics, biostatistics and medical image analysis.

The empirical evidence that links the structure of a nation's political institutions and the distribution of the preferences of the actors that inhabit them to economic outcomes such as an improved policy environment, investment behavior and economic growth has grown dramatically in recent years. However, due to data limitations, virtually all of this analysis is undertaken using data from the past three decades. This paper extends this empirical framework backwards in time and performs a two century long historical analysis of the determinants of infrastructure investment in a panel of over one hundred countries. The results demonstrate that political environments that limit the feasibility of policy change are an important determinant of investment in vital economic infrastructure not only in recent years but also at the inception of these technologies in the nineteenth century. Keywords: Infrastructure, institutional environment, economic history, electricity, telecommunications

This paper derives several Lagrange Multiplier tests for the panel data regression model wih spatial error correlation. These tests draw upon two strands of earlier work. The …rst is the LM tests for the spatial error correlation model discussed in Anselin (1988, 1999) and Anselin, Bera, Florax and Yoon (1996), and the second is the LM tests for the error component panel data model discussed in Breusch and Pagan (1980) and Baltagi, Chang and Li (1992). The idea is to allow for both spatial error correlation as well as random region e¤ects in the panel data regression model and to test for their joint signi…cance. Additionally, this paper derives conditional LM tests, which test for random regional e¤ects given the presence of spatial error correlation. Also, spatial error correlation given the presence of random regional e¤ects. These conditional LM tests are an alternative to the one directional LM tests that test for random regional e¤ects ignoring the presence of spatial error correlation or the one directional LM tests for spatial error correlation ignoring the presence of random regional e¤ects. We argue that these joint and conditional LM tests guard against possible misspeci…cation. Extensive Monte Carlo experiments are conducted to study the performance of these LM tests as well as the corresponding Likelihood Ratio tests.

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.

This paper develops a method for performing inference using spatially dependent data. We consider test statistics formed using nonparametric covariance matrix estimators that account for heteroskedasticity and spatial correlation (spatial HAC). We provide distributions of commonly used test statistics under “fixed-b” asymptotics, in which HAC smoothing parameters are proportional to the sample size. Under this sequence, spatial HAC estimators are not consistent but converge to non-degenerate limiting random variables that depend on the HAC smoothing parameters and kernel. We show that the limit distributions of commonly used test statistics are pivotal but non-standard, so critical values must be obtained by simulation. We provide a simple and general simulation procedure based on the i.i.d. bootstrap that can be used to obtain critical values. We illustrate the potential gains of the new approximation through simulations and an empirical example that examines the effect of unjust dismissal doctrine on temporary help services employment.

Abstract: Is there a relationship between economic dependence on oil or minerals and authoritarianism? In order to answer this question we develop unique historical datasets that allow us to focus on within-country variance in resource dependence and regime types, test for long-run relationships between resource reliance and regime type, and estimate dynamic panel models. Our results indicate that dependence on oil and minerals is not associated with the undermining of democracy or less complete transitions to democracy. Our results are at variance with a large body of scholarship that finds a negative relationship between oil or mineral dependence and democracy using pooled, time-series cross-sectional techniques centered on the variation between countries using data substantially truncated with respect to time. We surmise that the reason for this discrepancy is that countries ’ underlying institutions jointly determine their resource

This paper considers estimation of slope coe ¢ cients in large panel data models where even after conditioning on common observed e¤ects the cross section units might remain dependently distributed. This could arise when the cross section units are subject to unobserved common e¤ects and/or if there are spill over e¤ects due to spatial or other forms of local dependencies. Initially it focuses on a regression model where the idiosyncratic errors are spatially dependent and possibly serially correlated, and derives the asymptotic distributions of the (generalized) …xed e¤ects and the mean group estimators under homogeneous and heterogeneous slope coe ¢ cients. Semiparametric and non-parametric estimation of the variances of these estimators is considered. The paper then focuses on a panel data model with a multifactor error structure and spatial correlation. It is established that, under this framework, the Common Correlated E¤ects (CCE) estimator, recently advanced by Pesaran (2006), continues to provide estimates of the slope coe ¢ cient that are consistent and asymptotically normal. 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.

This paper studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator, which is flexible to nest existing estimators as special cases with certain choices of bandwidths. For distributional approximations, we consider two different types of asymptotics. When the level of smoothing is assumed to increase with the sample size, the proposed estimator is consistent and the associated Wald statistic converges to a χ2 distribution. We show that our covariance estimator improves upon existing estimators in terms of robustness and efficiency. When we assume the level of smoothing to be held fixed, the covariance estimator has a random limit and we show by asymptotic expansion that the limiting distribution of the test statistic depends on the bandwidth parameters, the kernel function, and the number of restrictions being tested. As this distribution is nonstandard, we establish the validity of an F-approximation to this distribution, which greatly facilitates the test. For optimal bandwidth selection, we propose a procedure based on the upper bound of asymptotic mean square error criterion. The flexibility of our estimator and proposed bandwidth selection procedure make our estimator adaptive to the dependence structure in data. This adaptiveness automates the selection of covariance estimator. That is, our estimator reduces to the existing estimators which are designed to cope with the particular dependence structures. Simulation results show that the F-approximation and the adaptiveness work reasonably well.