In the present note we demonstrate how to implement the Lee and Yu (2010) procedure for fixed effects spatial panel data models available from the R (R Development Core Team 2012) package splm (Millo and Piras 2012). Additionally, we also show how to compute the impact estimates introduced by Kelejian, Tavlas, and Hondroyiannis (2006) and formalized in LeSage and Pace (2009). Unlike Matlab (MAT...

This paper proposes the Fixed Effects Filtered (FEF) and Fixed Effects Filtered instrumental variable (FEF-IV) estimators for estimation and inference in the case of time-invariant effects in static panel data models when N is large and T is fixed. The FEF-IV allows for endogenous time-invariant regressors but assumes that there exists a suffi cient number of instruments for such regressors. It...

Purpose: The threat of global warming has escalated as a result industrialization, urbanization, population growth, and lifestyle changes in Brazil, Russia, India, China, South Africa (BRICS). amount electricity generated by various sources is directly influenced their respective carbon dioxide (CO2
 ) emissions. This study’s primary goal to determine which are bad for the environment not....

Regression analysis with geographic information needs to take into consideration the inherent spatial autocorrelation and heterogeneity of the data. Due to such spatial effects, it is found that local regression such as the geographically weighted regression (GWR) tends to capture the relationships better. In addition, in panel data analysis, the variable coefficient panel regression can borrow...

An estimation framework and a user-friendly software implementation are described for maximum likelihood estimation of panel data models with random effects, a spatially lagged dependent variable and spatially and serially correlated errors. This specification extends static panel data models in the direction of serial error correlation, allowing richer modelling possibilities and more thorough...