Common Factors, spatial dependence, and regional growth in the Italian manufacturing industry
We review the methods currently available for the analysis of regional datasets characterised by possible non-stationarity over time and both strong and weak spatial dependence and present, as a representative case study, a comparative analysis of the regional development of the Italian manufacturing industries in the second halves of the 19th and 20th centuries. For highly heterogenous datasets we suggest a two-stages approach: (1) fit a dynam factor model with endogenous determination of the number of factors; (2) estimate a spatial model for the de-factored data. Applying this strategy we find two similar non-stationary afctors sufficient to explain long-run growth of the whole set of series examined in both centuries. Further, the results suggest that some conditional spatial error correction mechanisms seem to have been in action in both centuries.
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