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Non-discriminatory Trade Policies in Structural Gravity Models Evidence from Monte Carlo Simulations

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  • Richard Sellner

Abstract

This paper provides Monte Carlo simulation evidence on the performance of methods used for identifying the effects of non-discriminatory trade policy (NDTP) variables in structural gravity models (SGM). The benchmarked methods include the identification strategy of Heid, Larch & Yotov (2015) that utilizes data on intra-national trade flows and three other methods that do not rely on this data. Results indicate that under the assumption of a data generating process that conforms with SGM theory, data on intra-national trade flows is required for identification. The bias of the three methods that do not utilize this data, is a result of the correlation between the NDTP variable and the collinear fixed effects. The MC results and an empirical application demonstrate the severity of this bias in methods that have been applied in previous empirical research.

Suggested Citation

  • Richard Sellner, 2017. "Non-discriminatory Trade Policies in Structural Gravity Models Evidence from Monte Carlo Simulations," FIW Working Paper series 178, FIW.
  • Handle: RePEc:wsr:wpaper:y:2017:i:178
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    Cited by:

    1. Harald Oberhofer & Michael Pfaffermayr & Richard Sellner, 2021. "Revisiting time as a trade barrier: Evidence from a panel structural gravity model," Review of International Economics, Wiley Blackwell, vol. 29(5), pages 1382-1417, November.

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    More about this item

    Keywords

    Structural Gravity Model; Non-discriminatory Trade Policies; Monte Carlo Simulation;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • F10 - International Economics - - Trade - - - General
    • F13 - International Economics - - Trade - - - Trade Policy; International Trade Organizations

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