Advances in Econometrics is a research annual whose editorial policy is to publish original research articles that contain enough details so that economists and econometricians who are not experts in the topics will find them accessible and useful in their research. Volume 37 exemplifies this...

This volume focuses on econometric models that confront estimation and inference issues occurring when sample data exhibit spatial or spatiotemporal dependence. This can arise when decisions or transactions of economic agents are related to the behaviour of nearby agents. Dependence of one...

Front Cover -- Spatial Econometrics: Qualitative and Limited Dependent Variables -- Copyright Page -- Contents -- List of Contributors -- Part I: Introduction -- Progress in Spatial Modeling of Discrete and Continuous Dependent Variables -- Part II: Discrete Dependent Variables - Maximum...

There is near universal agreement that estimates and inferences from spatial regression models are sensitive to particular specifications used for the spatial weight structure in these models. We find little theoretical basis for this commonly held belief, if estimates and inferences are based...

Most spatial econometrics work focuses on spatial dependence in the regressand or disturbances. However, LeSage and Pace (2009) show that the bias from applying OLS to a regressand generated from a spatial autoregressive process was exacerbated by spatial dependence in the regressor. Also, the...

A number of authors have suggested that omitted variables affect spatial regression methods less than ordinary least-squares (OLS). To explore these conjectures, we derive an expression for OLS omitted variable bias in a univariate model with spatial dependence and show that positive dependence...

While salary benchmarking is widely used to help set compensation, there has been little econometric modeling of peer-group identification and the impacts of benchmarking on the compensation decision. We adapt some empirical tools from spatial econometrics to analyze compensation decisions...

Most hedonic pricing studies using transaction data employ only sold properties. Since the properties sold during any year or even decade represent only a fraction of all properties, this approach ignores the potentially valuable information content of unsold properties which have known...

We discuss Monte Carlo methodology that can be used to explore alternative approaches to estimating spatial regression models. Our focus is on models that include spatial lags of the dependent variable, e.g., the SAR specification: y = ρ W y X β Ε. A major point is that following publication...

Often, authors report materially different OLS and spatial error model estimates. However, under the null of correct specification, these estimates should be similar. We propose a spatial Hausman test and conduct a Monte Carlo experiment to examine its performance.