Spatial econometrics has been an ongoing research …eld. Recently, it has been extended to the panel data settings. The spatial panel data can allow cross sectional dependence as well as state dependence, and it can also enable researchers to control for unknown heteorgeneity. This paper reports some recent developments in the econometric speci…cation and estimation on spatial panel data models....

introduction: under the principles of international law, no state has the right to use or permit the use of its territory in such a manner as to cause damage to the environment of other states. spatial econometrics provides a powerful tool to assess the influence of the pollution of neighboring countries on a country's pollution level. spatial spillover effects play a significant role in a...

The journal ’Computational Statistics and Data Analysis’ aims to have regular issues on computational econometrics (Amendola et al., 2006a,b; Belsley and Kontoghiorghes, 2003, 2005; Belsley et al., 2007). Of particular interest are papers in important areas of econometric applications where both computational techniques and numerical methods have a major impact. The goal is to provide sources o...

Due to criticisms of potential identification issues within spatial panel data models, this study contributes to the literature by comparing forecasts of provincelevel carbon dioxide emissions against empirical reality using dynamic, spatial panel data models with and without fixed effects. From a policy standpoint, understanding how to predict emissions is important for designing climate chang...

This paper examines the relationship between input-output linkages and industry agglomeration by combining the strength of spatial econometrics and the input-output model. This paper uses the location quotient as a measure of industrial agglomeration and regresses it on the intermediate demand variable, which is an important variable in the integrated econometric and input-output models. The na...

In this paper we employ techniques developed in spatial econometrics to analyse spatial patterns of technology diffusion, to detect clusters and to estimate theoretical models that incorporate space explicitly. These techniques correct for misspecifications resulting from the omission of spatial linkages in standard empirical models of economic growth. Our dataset consists of TFP estimates for ...

Spatial covariance matrix estimation is of great significance in many applications in climatology, econometrics and many other fields with complex data structures involving spatial dependencies. High dimensionality brings new challenges to this problem, and no theoretical optimal estimator has been proved for the spatial high-dimensional covariance matrix. Over the past decade, the method of re...

This Fourth Edition updates the "Solutions Manual for Econometrics" to match Sixth of Econometrics textbook.