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Weak and Strong Cross-Sectional Dependence: a Panel Data Analysis of International Technology Diffusion

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  • Antonio Musolesi
  • Cem Ertur

Abstract

This paper provides an econometric examination of technological knowledge spillovers among countries by focusing on the issue of error cross-sectional dependence, particularly on the dif- ferent ways – weak and strong – that this dependence may affect model specification and estimation. A preliminary analysis based on estimation of the exponent of cross-sectional de- pendence provides a clear result in favor of strong cross-sectional dependence. This result has relevant implications in terms of econometric modeling and suggests that a factor structure is preferable to a spatial error model. The common correlated effects approach is then used be- cause it remains valid in a variety of situations that are likely to occur, such as the presence of both forms of dependence or the existence of nonstationary factors. According to the estimation results, richer countries benefit more from domestic R&D and geographic spillovers than poorer countries, while smaller countries benefit more from spillovers originating from trade. The re- sults also suggest that when the problem of (possibly many) correlated unobserved factors is addressed, the quantity of education no longer has a significant effect. Finally, a comparison of the results with those obtained from a spatial model provides interesting insights into the bias that may arise when we allow only for weak dependence, despite the presence of strong dependence in the data.

Suggested Citation

  • Antonio Musolesi & Cem Ertur, 2016. "Weak and Strong Cross-Sectional Dependence: a Panel Data Analysis of International Technology Diffusion," Working Papers 2016035, University of Ferrara, Department of Economics.
  • Handle: RePEc:udf:wpaper:2016035
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    Cited by:

    1. Hean, Oudom, 2018. "The Effect of Metropolitan Technological Progress on the Non-metropolitan Labor Market: Evidence from U.S. Patent Counts," 2018 Annual Meeting, August 5-7, Washington, D.C. 274176, Agricultural and Applied Economics Association.
    2. R. Golinelli & I. Mammi & A. Musolesi, 2018. "Parameter heterogeneity, persistence and cross-sectional dependence: new insights on fiscal policy reaction functions for the Euro area," Working Papers wp1120, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Antonio Musolesi & Michel Simioni & Georgios Gioldasis, 2018. "Nonparametric estimation of international R&D spillovers," Working Papers 2018037, University of Ferrara, Department of Economics.
    4. Ciccarelli, Carlo & Elhorst, J.Paul, 2018. "A dynamic spatial econometric diffusion model with common factors: The rise and spread of cigarette consumption in Italy," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 131-142.
    5. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    6. Diego-Ivan Ruge-Leiva, 2015. "International R&D Spillovers and other Unobserved Common Spillovers and Shocks," Papers 1502.06805, arXiv.org.
    7. Elhorst, J. Paul & Madre, Jean-Loup & Pirotte, Alain, 2020. "Car traffic, habit persistence, cross-sectional dependence, and spatial heterogeneity: New insights using French departmental data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 614-632.
    8. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2018. "Nonparametric estimation of international R&D spillovers," SEEDS Working Papers 0318, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Mar 2018.
    9. De Visscher, Stef & Eberhardt, Markus & Everaert, Gerdie, 2017. "Measuring Productivity and Absorptive Capacity Evolution in OECD Economies," CEPR Discussion Papers 12261, C.E.P.R. Discussion Papers.
    10. Ruge-Leiva, Diego-Ivan, 2015. "The Online Supplement to “International R&D Spillovers and other Unobserved Common Spillovers and Shocks”," MPRA Paper 62205, University Library of Munich, Germany.
    11. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    12. Debarsy, Nicolas & Yang, Zhenlin, 2018. "Editorial for the special issue entitled: New advances in spatial econometrics: Interactions matter," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 1-5.
    13. Heng Chen & Matthew Strathearn, 2020. "A Spatial Model of Bank Branches in Canada," Staff Working Papers 20-4, Bank of Canada.
    14. Zhang, Yuanqing & Feng, Shuhui & Jin, Fei, 2019. "QML estimation of the matrix exponential spatial specification panel data model with fixed effects and heteroskedasticity," Economics Letters, Elsevier, vol. 180(C), pages 1-5.
    15. Ruge Leiva, Diego Ivan, 2015. "International R&D Spillovers and other Unobserved Common Spillovers and Shocks," MPRA Paper 63500, University Library of Munich, Germany.
    16. Massimiliano Mazzanti & Antonio Musolesi, 2017. "The effect of Rio Convention and other structural breaks on long-run economic development-CO2 relationships," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 34(3), pages 389-405, December.
    17. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," SEEDS Working Papers 0120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Jan 2020.
    18. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2019. "Nonparametric estimation of R&D international spillovers," Post-Print hal-02789474, HAL.
    19. Chakraborty, Saptorshee Kanto & Mazzanti, Massimiliano, 2020. "Energy intensity and green energy innovation: Checking heterogeneous country effects in the OECD," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 328-343.

    More about this item

    Keywords

    cross-sectional dependence; large panels; factor models; spatial models; heterogeneous slopes; unit root; total factor productivity; research and development; human capital;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • F0 - International Economics - - General
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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