The paper develops an asymptotically valid F test that is robust to spatial autocorrelation in a GMM framework. The test is based on the class of series covariance matrix estimators and fixed-smoothing asymptotics. The fixed-smoothing asymptotics and F approximation are established under mild...

In the presence of heteroscedasticity and autocorrelation of unknown forms, the covariance matrix of the parameter estimator is often estimated using a nonparametric kernel method that involves a lag truncation parameter. Depending on whether this lag truncation parameter is specified to grow at...

The method of sieves has been widely used in estimating semiparametric and nonparametric models. In this paper, we first provide a general theory on the asymptotic normality of plug-in sieve M estimators of possibly irregular functionals of semi/nonparametric time series models. Next, we...

This paper establishes the asymptotic normality of plug-in sieve M estimators of possibly irregular functionals of semi-nonparametric time series models. We show that, even when the sieve score process is not a martingale difference sequence, the asymptotic variance in the case of irregular...

This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005, Journal of...

In this paper we provide a brief overview of some of the most recent empirical research on spatial econometric models and spatial data mining. Data mining in general is the search for hidden patterns that may exist in large databases. Spatial data mining is a process to discover interesting,...

In this paper we provide a brief overview of some of the most recent empirical research on spatial econometric models and spatial data mining. Data mining in general is the search for hidden patterns that may exist in large databases. Spatial data mining is a process to discover interesting,...

This paper presents a theoretical model, based on the neoclassical growth literature, which explicitly takes into account technological interdependence among economies and examines the impact of location and neighborhood effects in explaining growth. Technological interdependence is supposed...

In this paper we provide a brief overview of some of the most recent empirical research on spatial econometric models and spatial data mining. Data mining in general is the search for hidden patterns that may exist in large databases. Spatial data mining is a process to discover interesting,...