The contribution of this paper is to explore time and spatial scale dimensions of economic growth in Brazil using alternative panel data techniques to provide a measure of the extent of spatial autocorrelation (in kilometres) over three decades (1970–2000) as well as discussing the determinants of economic growth at a variety of geographic scales (minimum comparable areas, micro-regions, meso-regions, and states). The magnitude and statistical significance of growth determinants such as schooling, population density, population growth, and transportation costs are dependent on the scale of analysis. Moreover, the extent of residual spatial autocorrelation showed that it seems to vary across spatial scales. Indeed, spatial autocorrelation seems to be bounded at the state level and it shows positive and statistically significant values across distances of more than 1,500？kilometres at the other three spatial scales. Among other results, the study suggests that the nonspatial panel data techniques are not able to deal with spatially correlated omitted variables across different spatial scales, except for the state level where nonspatial panel data models seem to be appropriate to investigate growth determinants and convergence process in the Brazilian states case. 1. Introduction This paper explores time and spatial scale dimensions of economic growth in Brazil using alternative panel-data techniques to provide a measure of the extent of spatial autocorrelation (in kilometres) over three decades (1970–2000) and it discusses the determinants of economic growth at a variety of geographic scales (minimum comparable areas, micro-regions, meso-regions, and states). The focus is on a descriptive analysis of the extent of the spatial autocorrelation effects in the context of regional growth literature, by testing whether the residuals of traditional growth model estimates are spatially autocorrelated at different spatial scales using standard panel data models between 1970 and 2000 in Brazil. This approach allows us to investigate whether alternative nonspatial panel data models (that control for time invariant fixed effects) eliminate or, at least, mitigate the spatial autocorrelation. One of the advantages of using panel data framework is that it allows, as noted by Islam [1], for differences in aggregate production functions for all the regions in the form of unobservable individual fixed effects, for instance correcting for omitted variable bias. Some studies so far, such as Elhorst et al. [2], advances the growth literature by using spatial econometric
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