One important goal of this study is to develop a methodology of inference for a widely used Cliff-Ord type spatial model containing spatial lags in the dependent variable, exogenous variables, and the disturbance terms, while allowing for unknown heteroskedasticity in the innovations. We first generalize the generalized moments (GM) estimator suggested in Kelejian and Prucha (1998,1999) for the spatial autoregressive parameter in the disturbance process. We prove the consistency of our estimator; unlike in our earlier paper we also determine its asymptotic distribution, and discuss issues of efficiency. We then define instrumental variable (IV) estimators for the regression parameters of the model and give results concerning the joint asymptotic distribution of those estimators and the GM estimator under reasonable conditions. Much of the theory is kept general to cover a wide range of settings. We note the estimation theory developed by Kelejian and Prucha (1998, 1999) for GM and IV estimators and by Lee (2004) for the quasi-maximum likelihood estimator under the assumption of homoskedastic innovations does not carry over to the case of heteroskedastic innovations. The paper also provides a critical discussion of the usual specification of the parameter space.

The authors are deeply indebted to Larry Neal and Joseph Mason who facilitated the collection of the

In this paper, we consider a spatial-autoregressive model with autoregressive disturbances, where we allow for endogenous regressors in addition to a spatial lag of the dependent vari-able. We suggest a two-step generalized method of moments (GMM) and instrumental variable (IV) estimation approach extending earlier work by, e.g., Kelejian and Prucha (1998, 1999). In contrast to those papers, we not only prove consistency for our GMM estimator for the spatial-autoregressive parameter in the disturbance process, but we also derive the joint lim-iting distribution for our GMM estimator and the IV estimator for the regression parameters. Thus the theory allows for a joint test of zero spatial interactions in the dependent variable, the exogenous variables and the disturbances. The paper also provides a Monte Carlo study to illustrate the performance of the estimator in small samples.1

1Our thanks for very helpful comments are owed to Peter Egger, Michael Pfaffermayr, and Gianfranco Piras. Also, we gratefully acknowledge financial support from the National Institute of Health through the SBIR grant 1 R43 AG027622. Ingmar Prucha also thanks the CESifo in Munich for their hospitality and appreciates their support in writing this paper.

While much of the literature on cross section dependence has fo-cused mainly on estimation of the regression coe¢cients in the under-lying model, estimation and inferences on the magnitude and strength of spill-overs and interactions has been largely ignored. At the same time, such inferences are important in many applications, not least because they have structural interpretations and provide useful inter-pretation and structural explanation for the strength of any interac-tions. In this paper we propose GMM methods designed to uncover underlying (hidden) interactions in social networks and committees. Special attention is paid to the interval censored regression model. Our methods are applied to a study of committee decision making within the Bank of England’s monetary policy committee.

In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms (errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR model proposed in this study also considers the spatial and temporal correlations across observations, making the model more behaviorally convincing and applicable to circumstances where a three-dimensional correlation exists, across time, space and equations. An example of crash rates in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing severe crash rates, but with an elasticity of only-0.078. It is also observed that though vehicle ownership is associated with higher crash per capita rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18 respectively; much lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations in the presence of temporal and spatial effects. KEY WORDS: Spatial econometrics, seemingly unrelated regression, spatial and temporal

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In this paper we merge techniques from the efficiency literature with spatial econometric techniques. In particular, we combine calculation of efficiency from the unit specific effects with the spatial autoregressive model to develop a spatial autoregressive frontier model for panel data. Features of the modeling include time-varying efficiency and estimation of own and spillover returns to scale. The model is applied to aggregate production in European countries over the period 1995 2008. Among other things, we find that production in the sample average country is characterized by increasing returns to scale when we allow for returns to scale spillovers from other countries, and constant returns when these spillovers are ignored.

Household and employment counts (by type) are key inputs to models of travel demand. For a variety of reasons, spatial dependence is very likely present in and across these counts. In order to identify the nature of these unobserved relationships, this study performs a series of Lagrange multiplier tests to confirm the co-existence of spatial lag and error processes within individual equations (6 household types and 3 employment categories). It then provides the first application of a feasible generalized spatial 3SLS estimation procedure for a seemingly unrelated regression (SUR) model of these equations. In the resulting model of Austin, Texas data, local land use conditions offer substantial predictive power of households and jobs, and transportation access plays a role, as anticipated. The work demonstrates that SUR estimation of land use intensities from parcel-level data with two types of spatial dependence is feasible and meaningful. Coupled with an upstream model of land use type, this work offers the key inputs for travel demand analyses, with transportation system performance feedback.

Abstract A spatial simultaneous-equations growth equilibrium model estimated by GS2SLS and GS3SLS estimators is used to determine the interdependence between small business growth and poverty. The parameter estimates are mostly consistent with the theoretical expectations. The coefficients for the endogenous variables of the model are positive and significant indicating strong interdependence (feedback simultaneity) between small business and median household income growth rates. The results also show the presence of spatial autoregressive lag simultaneity and spatial cross-regressive lag simultaneity, with respect to both small business and median household income growth rates, and the existence of spatial correlation in the error terms. In addition, the estimates of the structural parameters show that there were strong agglomerative effects and significant conditional convergence with respect to both small business growth and median household income growth in Appalachia during the study period.