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Common Factors, spatial dependence, and regional growth in the Italian manufacturing industry

Author

Listed:
  • Carlo Ciccarelli

    (University of Rome "Tor Vergata")

  • Stefano Fachin

    ("Sapienza" University of Rome)

Abstract

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.

Suggested Citation

  • Carlo Ciccarelli & Stefano Fachin, 2017. "Common Factors, spatial dependence, and regional growth in the Italian manufacturing industry," DSS Empirical Economics and Econometrics Working Papers Series 2017/1, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    • Handle: RePEc:sas:wpaper:20171
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    File URL: http://www.dss.uniroma1.it/RePec/sas/wpaper/20171_CF.pdf
    File Function: First version, 2017
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    Cited by:

    1. Di Iorio, Francesca & Fachin, Stefano, 2021. "Evaluating restricted common factor models for non-stationary data," Econometrics and Statistics, Elsevier, vol. 17(C), pages 64-75.

    More about this item

    Keywords

    Cross-sectional dependence; approximate factor models; dynamic spatial panel models; Italy; manufacturing industries.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • N13 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - Europe: Pre-1913
    • N63 - Economic History - - Manufacturing and Construction - - - Europe: Pre-1913
    • N93 - Economic History - - Regional and Urban History - - - Europe: Pre-1913

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