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Estimation of Country-Pair Data Models Controlling for Clustered Errors: with International Trade Applications

Author

Listed:
  • A. Colin Cameron
  • Natalia Golotvina

    (Department of Economics, University of California Davis)

Abstract

We consider cross-section regression models for country-pair data, such as gravity models for trade volume between countries or models of exchange rate volatility, allowing for the presence of country-specific errors. This induces clustered errors in a nonstandard setting. OLS standard errors that ignore this clustering are greatly underestimated. Under the assumption of random country-specific effects we provide analytical results that permit more efficient GLS estimation even in settings where the number of unique country-pairs is very large. We include applications to international data on real exchange rates and on bilateral trade that provided the motivation for this paper. The results are more generally applicable to regression with paired data.

Suggested Citation

  • A. Colin Cameron & Natalia Golotvina, 2005. "Estimation of Country-Pair Data Models Controlling for Clustered Errors: with International Trade Applications," Working Papers 182, University of California, Davis, Department of Economics.
    • Handle: RePEc:cda:wpaper:182
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    References listed on IDEAS

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    2. Laura Serlenga & Yongcheol Shin, 2004. "Gravity Models of the Intra-EU Trade: Application of the Hausman-Taylor Estimation in Heterogeneous Panels with Common Time-specific Factors," Edinburgh School of Economics Discussion Paper Series 105, Edinburgh School of Economics, University of Edinburgh.
    3. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    4. Kloek, T, 1981. "OLS Estimation in a Model Where a Microvariable Is Explained by Aggregates and Contemporaneous Disturbances Are Equicorrelated," Econometrica, Econometric Society, vol. 49(1), pages 205-207, January.
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    Cited by:

    1. Jonah B. Gelbach & Doug Miller, 2009. "Robust Inference with Multi-way Clustering," Working Papers 226, University of California, Davis, Department of Economics.
    2. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 318, University of California, Davis, Department of Economics.
    3. Müller, Oliver & Uhde, André, 2013. "Cross-border bank lending: Empirical evidence on new determinants from OECD banking markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 23(C), pages 136-162.
    4. Fabio Montobbio & Annalisa Primi & Valerio Sterzi, 2015. "IPRs and International Knowledge Flows: Evidence from Six Large Emerging Countries," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 106(2), pages 187-204, April.
    5. Mazouz, Khelifa & Wood, Geoffrey & Yin, Shuxing & Zhang, Mao, 2021. "Comprehending the outward FDI from Latin America and OCED: A comparative perspective," International Business Review, Elsevier, vol. 30(5).
    6. Douglas L. Campbell, 2010. "History, Culture, and Trade: A Dynamic Gravity Approach," EERI Research Paper Series EERI_RP_2010_26, Economics and Econometrics Research Institute (EERI), Brussels.
    7. Montobbio, Fabio & Sterzi, Valerio, 2013. "The Globalization of Technology in Emerging Markets: A Gravity Model on the Determinants of International Patent Collaborations," World Development, Elsevier, vol. 44(C), pages 281-299.
    8. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 107, University of California, Davis, Department of Economics.
    9. Bryan S. Graham, 2019. "Network Data," NBER Working Papers 26577, National Bureau of Economic Research, Inc.
    10. Lorenzo Cassi & Andrea Morrison & Roberta Rabellotti, 2015. "Proximity and Scientific Collaboration: Evidence from the Global Wine Industry," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 106(2), pages 205-219, April.
    11. Harold D Chiang & Yukun Ma & Joel Rodrigue & Yuya Sasaki, 2021. "Dyadic double/debiased machine learning for analyzing determinants of free trade agreements," Papers 2110.04365, arXiv.org, revised Dec 2022.
    12. Bryan S. Graham, 2019. "Network Data," CeMMAP working papers CWP71/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    More about this item

    Keywords

    clustered errors; random effects; country-pair data; international trade data; exchange rate data;
    All these keywords.

    JEL classification:

    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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