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Aggregation Effects And Panel Data Estimation Problems: An Investigationof the R&D Intensity Decision

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  • Ariel Pakes

Abstract

This paper considers why the determinants of the inter- and intra-industry variance in R&D intensity in U.S. manufacturing differ markedly even though response parameters are similar across industries. A similar aggregation effect is noted by Grunfeld and Griliches (1960), and this paper gives that effect operational content in terms of grouped data estimation procedures. Observationally equivalent aggregation results can be generated by errors in variables models (see Aigner and Goldfeld [1974]).A later section considers specifications which identify the empirical importance of both these problems. Finally, a summary of the empirical results on the determinants of R&D intensity is provided.

Suggested Citation

  • Ariel Pakes, 1979. "Aggregation Effects And Panel Data Estimation Problems: An Investigationof the R&D Intensity Decision," NBER Working Papers 0344, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:0344
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    1. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
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    3. Aigner, Dennis J & Goldfeld, Stephen M, 1974. "Estimation and Prediction from Aggregate Data when Aggregates are Measured More Accurately than Their Components," Econometrica, Econometric Society, vol. 42(1), pages 113-134, January.
    4. Zvi Griliches, 1958. "Research Costs and Social Returns: Hybrid Corn and Related Innovations," Journal of Political Economy, University of Chicago Press, vol. 66(5), pages 419-419.
    5. Milton Friedman, 1957. "A Theory of the Consumption Function," NBER Books, National Bureau of Economic Research, Inc, number frie57-1, July.
    6. Kenneth Arrow, 1962. "Economic Welfare and the Allocation of Resources for Invention," NBER Chapters, in: The Rate and Direction of Inventive Activity: Economic and Social Factors, pages 609-626, National Bureau of Economic Research, Inc.
    7. Aigner, D.J. & Goldfeld, S.M., 1974. "Estimation and prediction from aggregate data when aggregates are measured more accurately than their components," LIDAM Reprints CORE 190, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
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