IDEAS home Printed from https://ideas.repec.org/p/sas/wpaper/20171.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.dss.uniroma1.it/RePec/sas/wpaper/20171_CF.pdf
    File Function: First version, 2017
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sas:wpaper:20171. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Stefano Fachin (email available below). General contact details of provider: https://edirc.repec.org/data/ddrosit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.