Strategy of Spatial Panel Estimation: Spatial Spillovers Between Taxation and Economic Growth

Abstract

Spatial panels are a powerful econometric tool for the estimation of spacedependent cross-sectional time-series models of economic phenomena. A plethora of parameters and possible specifications require a systematic approach to estimation. This paper presents a strategy of estimation to be considered in applied research on economic policy, including the concept of spatial spillovers and its local and global effects, direct and indirect impacts, as well as the role of different spatial weighting schemes. The paper presents fiscal factors affecting GDP between the years 2002-2015 in a number of European economies.

How are the potential gains from economic activity transmitted to the labour factor: more employment or more wages? Evidence from the Portuguese context

“…A conclusion that can be derived from the estimated coefficients of the spatially lagged variables and their statistical significance. In all of the estimations there are coefficients for the spatially lagged variables that are statistically significant, which is a sign of confirmation of the importance of the spatial effects (Kopczewska et al, ).…”

“…The most suitable spatial model must be chosen according to the empirical data. There are various strategies for their selection, the goal being to use the spatial model that best fits the data, with the highest number of significant variables (Kopczewska, Kudła, & Walczyk, ). We will therefore apply some tests to choose the appropriate spatial panel model for each industry.…”

“…However, in this case, the relationship between spatial units is linear, decreasing proportionally with distance. Due to its nature, the contiguity matrix considers only spatial effects between two contiguous regions, as the links between the units end there (Kopczewska et al, ).…”

Human rights protection and foreign direct investment: The case of post‐socialist countries

“…The inverse distance matrix with weights equal 1/ d ij , where d ij stands for distance between country i and country j , captures linear relations of neighbours with all territorial units (the strength of this relationship is proportional to the distance between units). The squared inverse distance matrix with weights equal represents both all‐to‐all relations and the neighbourhood clusters with stronger links (Kopczewska et al., ). In the case of spatial location of FDI, both interactions between all countries of the region and local clusters between countries of similar characteristics should be taken into account.…”

“…Another spatial parameter to be interpreted in SDEM is the one associated with a spatially autocorrelated error term (parameter ρ from Equation ). A significant and positive ρ reflects the short‐term spillovers’ fluctuations that are similar in neighbouring locations, whereas a significant and negative ρ indicates the existence of competitive mechanisms of reaction to common shocks modelled by the error term (Kopczewska et al., ). The SDEM model is estimated using a lagged dependent variable (LDV) GMM approach with instrumental variables (as recommended in Davies and Vadlamannati, for a similar econometric problem).…”