Forecasting with spatial panel data

TL;DR: In this article, a panel of 29 Chinese provinces from 1995 to 2012 is utilized to predict China's coal consumption through 2020, and an inverted U-shaped Environmental Kuznets Curve (EKC) between coal consumption per capita and GDP per capita in China is detected.

TL;DR: In this paper, the authors evaluate Spanish regions' resilience to the economic crisis under three main resilience notions: Adaptive, Engineering, and Eco-Resilience. And they conclude that the probability of presenting a better behavior (lower “drop” in growth than the average) increases for those regions specialized in the service sector before the crisis.

TL;DR: In this paper, a land use shares model is proposed to improve the prediction accuracy of aggregated land use share models by controlling for both unobserved individual heterogeneity and spatial autocorrelation.

TL;DR: In this article, the authors consider a number of specification tests against this alternative, based on both a maximum likelihood framework as well as on a general method of moments estimation approach, and compare the performance of these tests in a series of Monte Carlo simulation experiments against a wide range of alternatives of spatial autocorrelation.

TL;DR: In this article, a typology of Spatial Econometric Models is presented, and the maximum likelihood approach to estimate and test Spatial Process Models is proposed, as well as alternative approaches to Inference in Spatial process models.

TL;DR: In panel data four procedures are widely used: pooling, aggregating, averaging group estimates, and cross-section regression as discussed by the authors, and the theoretical results on the properties of these procedures are illustrated by UK labour demand functions for 38 industries over 30 years.