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A Spatial Diffusion Model with Common Factors and an Application to Cigarette Consumption




This paper adopts a dynamic spatial panel data model with common factors to explain the non-stationary diffusion process of cigarette consumption across 69 Italian provinces over the period 1877-1913. The Pesaran (2015) CD-test and the exponent a-test of Bailey et al. (2015) are used to show that both weak and strong cross-sectional dependence are important drivers of the propagation of cigarette demand over this period. Stability tests on the coefficients and the CD-test on the residuals of the model are used to verify whether the data and both forms of cross-sectional dependence are modeled adequately. Cigarettes are found to be a normal good with an income elasticity of 0.4 and a price elasticity -0.4 in the long term. The price elasticity can be decomposed into a direct effect of -0.54 in the own region and a spillover effect to other regions of 0.15. This positive spillover effect is in line with previous spatial econometric studies which investigated cigarette demand in the U.S. states over a more recent period.

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  • Carlo Ciccarelli & Jean Paul Elhorst, 2016. "A Spatial Diffusion Model with Common Factors and an Application to Cigarette Consumption," CEIS Research Paper 381, Tor Vergata University, CEIS, revised 31 May 2016.
  • Handle: RePEc:rtv:ceisrp:381

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    References listed on IDEAS

    1. Parent, Olivier & LeSage, James P., 2010. "A spatial dynamic panel model with random effects applied to commuting times," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 633-645, June.
    2. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2016. "Exponent of Cross‐Sectional Dependence: Estimation and Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 929-960, September.
    3. Paul Elhorst & Eelco Zandberg & Jakob De Haan, 2013. "The Impact of Interaction Effects among Neighbouring Countries on Financial Liberalization and Reform: A Dynamic Spatial Panel Data Approach," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 293-313, September.
    4. Alessandra Fogli & Laura Veldkamp, 2011. "Nature or Nurture? Learning and the Geography of Female Labor Force Participation," Econometrica, Econometric Society, vol. 79(4), pages 1103-1138, July.
    5. Nicolas DEBARSY (CERPE De Namur) & Cem ERTUR & James P. LeSAGE, 2010. "Interpreting Dynamic Space-Time Panel Data Models," LEO Working Papers / DR LEO 800, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    6. Carlo Ciccarelli & Jacob Weisdorf, 2016. "The Effect of the Italian Unification on the Comparative Regional Development in Literacy, 1821-1911," CEIS Research Paper 392, Tor Vergata University, CEIS, revised 25 Jul 2016.
    7. Jesús Mur & Ana Angulo, 2006. "The Spatial Durbin Model and the Common Factor Tests," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 207-226.
    8. Carlo Ciccarelli & Gianni De Fraja, 2014. "The demand for tobacco in post-unification Italy," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 8(2), pages 145-171, May.
    9. Fenoaltea,Stefano, 2014. "The Reinterpretation of Italian Economic History," Cambridge Books, Cambridge University Press, number 9781107658080, July.
    10. Carlo Ciccarelli, 2012. "The Consumption of Tobacco in Italy, 1871-1913: National and Regional Estimates," Rivista di storia economica, Società editrice il Mulino, issue 3, pages 409-452.
    11. Ryan R. Brady, 2011. "Measuring the diffusion of housing prices across space and over time," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 213-231, March.
    12. Mur, Jesús & Angulo, Ana, 2009. "Model selection strategies in a spatial setting: Some additional results," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 200-213, March.
    13. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
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    More about this item


    diffusion; non-stationarity; spatial dependence; common factors; cigarette demand;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • N33 - Economic History - - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy - - - Europe: Pre-1913
    • N93 - Economic History - - Regional and Urban History - - - Europe: Pre-1913
    • R22 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other Demand

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