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.

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 considers the specification and estimation of social interaction models with network structures and the presence of endogenous and contextural e¤ects. With macro group settings, group fixed effects are also incorporated. Networks provide information on the identification of endogenous, exogenous and unobserved interactions among specific peers. We consider the identification and estimation of such a model. Empirical applications are provided to illustrate the usefulness of such a model. In addition to asymptotic properties of estimators, Monte Carlo studies provide evidence on finite sample performance of the estimation methods.

Part of the International Economics Commons This Working Paper is brought to you for free and open access by the Maxwell School of Citizenship and Public Affairs at SURFACE. It has been accepted for inclusion in Center for Policy Research by an authorized administrator of SURFACE. For more information, please contact

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.

The French dairy sector has been undergoing significant changes both in term of scale and structure over the past several decades. There has been a general tendency toward fewer, yet larger operations, increased concentration, and consolidation of production. These changes become crucial because of the decrease of subsidies for the dairy and livestock sectors and the increase of public concerns about environmental quality degradation due to location of key production regions in environmentally sensitive areas. Indeed, the three traditional dairy producing regions, alone account for 47 % of the national production, with 11 billion liters of cow’s milk, and retain the greatest number of farms (42 % in 2005). This paper contributes to the literature by offering an insight in the spatial structure of the French dairy sector, allowing a better understanding of the agglomeration and dispersion forces that influence the location of dairy farms in France in 1995 and 2005. In addition to traditional determinants, we focus on spatial agglomeration externalities as well as the impact of agricultural policy subsidies and environmental regulations. We use a non-parametric heteroscedasticity and autocorrelation consistent (HAC) parameter covariance estimator proposed by Kelejian and Prucha (2007) that allows us to handle simultaneous equations and spatial endogeneity.

The development of a general inferential theory for nonlinear models with cross-sectionally or spatially dependent data has been hampered by a lack of appropriate limit theorems. To facilitate a general asymptotic inference theory relevant to economic applications, this paper first extends the notion of near-epoch dependent (NED) processes used in the time series literature to random fields. The class of processes that is NED on, say, an -mixing process, is shown to be closed under infinite transformations, and thus accommodates models with spatial dynamics. This would generally not be the case for the smaller class of -mixing processes. The paper then derives a central limit theorem and law of large numbers for NED random fields. These limit theorems allow for fairly general forms of heterogeneity including asymptotically unbounded moments, and accommodate arrays of random fields on unevenly spaced lattices. The limit theorems are employed to establish consistency and asymptotic normality of GMM estimators. These results provide a basis for inference in a wide range of models with spatial dependence. JEL Classification: C10, C21, C31 Key words: Random fields, near-epoch dependent processes, central limit theorem, law

In the last decade there has been an upsurge of studies on international comparisons of Total Factor Productivity (TFP). The empirical evidence suggests that countries and regions differ not only in traditional factor endowments (labour and physical capital) but mainly in productivity and technology. Therefore, a crucial issue is the analysis of the determinants of such differences in the efficiency levels across economies. In this paper we try to assess these issues by pursuing a twofold aim. First, we derive a regression based measure of regional TFP which have the nice advantage of not imposing a priori restrictions on the inputs elasticities; this is done by estimating a Cobb-Douglas production function relationship for 199 European regions over the period 1985-2006, which includes the traditional inputs as well as a measure of spatial interdependences across regions. Secondly, we investigate the determinants of the TFP levels by analyzing the role played by intangible factors: human capital, social capital and technological capital. It turns out that a large part of TFP differences across the European regions are explained by the disparities in the endowments of such assets. This outcome indicates the importance of policy strategies which aim at increasing the level of knowledge and social capital as stressed by the Lisbon agenda. Estimation is carried out by applying the spatial 2SLS method and the SHAC estimator to account for both heteroskedasticity and spatial autocorrelation.