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Nonparametric estimation of international R&D spillovers

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  • Antonio Musolesi
  • Michel Simioni
  • Georgios Gioldasis

Abstract

In a recent paper, Ertur and Musolesi (Journal of Applied Econometrics 2017; 32: 477-503)employ the Common Correlated Effects(CCE)approach to address the issue of strong cross-sectional dependence while studying international technology diffusion.We carefully revisit this issue by adopting Su and Jin's (Journal of Econometrics 2012; 169: 34-47) method, which extends the CCE approach to nonparametric specications.Our results indicate that the adoption of a nonparametric approach provides signicant benefits in terms of predictive ability.This work also refines previous results by showing threshold effects, nonlinearities and interactions, which are obscured in parametric specications and which have relevant policy implications.

Suggested Citation

  • Antonio Musolesi & Michel Simioni & Georgios Gioldasis, 2018. "Nonparametric estimation of international R&D spillovers," Working Papers 2018037, University of Ferrara, Department of Economics.
    • Handle: RePEc:udf:wpaper:2018037
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    References listed on IDEAS

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    1. Maasoumi, Esfandiar & Racine, Jeff & Stengos, Thanasis, 2007. "Growth and convergence: A profile of distribution dynamics and mobility," Journal of Econometrics, Elsevier, vol. 136(2), pages 483-508, February.
    2. Simon N. Wood, 2006. "Low-Rank Scale-Invariant Tensor Product Smooths for Generalized Additive Mixed Models," Biometrics, The International Biometric Society, vol. 62(4), pages 1025-1036, December.
    3. Chihwa Kao & Min‐Hsien Chiang & Bangtian Chen, 1999. "International R&D Spillovers: An Application of Estimation and Inference in Panel Cointegration," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 691-709, November.
    4. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    5. Coe, David T. & Helpman, Elhanan & Hoffmaister, Alexander W., 2009. "International R&D spillovers and institutions," European Economic Review, Elsevier, vol. 53(7), pages 723-741, October.
    6. Kapetanios, G. & Pesaran, M. Hashem & Yamagata, T., 2011. "Panels with non-stationary multifactor error structures," Journal of Econometrics, Elsevier, vol. 160(2), pages 326-348, February.
    7. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    8. Coe, David T. & Helpman, Elhanan, 1995. "International R&D spillovers," European Economic Review, Elsevier, vol. 39(5), pages 859-887, May.
    9. Lichtenberg, Frank R. & Pottelsberghe de la Potterie, Bruno v., 1998. "International R&D spillovers: A comment," European Economic Review, Elsevier, vol. 42(8), pages 1483-1491, September.
    10. repec:bla:obuest:v:61:y:1999:i:0:p:691-709 is not listed on IDEAS
    11. del Barrio-Castro, Tomas & Lopez-Bazo, Enrique & Serrano-Domingo, Guadalupe, 2002. "New evidence on international R&D spillovers, human capital and productivity in the OECD," Economics Letters, Elsevier, vol. 77(1), pages 41-45, September.
    12. Shujie Ma & Jeffrey S. Racine & Lijian Yang, 2015. "Spline Regression in the Presence of Categorical Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 705-717, August.
    13. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    14. Lee, Gwanghoon, 2006. "The effectiveness of international knowledge spillover channels," European Economic Review, Elsevier, vol. 50(8), pages 2075-2088, November.
    15. Yingxing Li & David Ruppert, 2008. "On the asymptotics of penalized splines," Biometrika, Biometrika Trust, vol. 95(2), pages 415-436.
    16. Su, Liangjun & Jin, Sainan, 2012. "Sieve estimation of panel data models with cross section dependence," Journal of Econometrics, Elsevier, vol. 169(1), pages 34-47.
    17. Gerda Claeskens & Tatyana Krivobokova & Jean D. Opsomer, 2009. "Asymptotic properties of penalized spline estimators," Biometrika, Biometrika Trust, vol. 96(3), pages 529-544.
    18. Delgado, Michael S. & McCloud, Nadine & Kumbhakar, Subal C., 2014. "A generalized empirical model of corruption, foreign direct investment, and growth," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 298-316.
    19. Philip T. Reiss & R. Todd Ogden, 2009. "Smoothing parameter selection for a class of semiparametric linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 505-523, April.
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