We study the role and drivers of persistence in the extensive margin of bilateral trade. Motivated by a stylized heterogeneous firms model of international trade with market entry costs, we propose new bias-corrected dynamic binary choice estimators with three sets of high-dimensional fixed effects . Monte Carlo simulations confirm their desirable statistical properties. A reassessment of the most commonly studied determinants of the extensive margin of trade demonstrates that both true state dependence and unobserved heterogeneity contribute strongly to trade persistence and that taking this persistence into account matters significantly in identifying the effects of trade policies on the extensive margin.
We study the role and drivers of persistence in the extensive margin of bilateral trade. Motivated by a stylized heterogeneous firms model of international trade with market entry costs, we consider dynamic three-way fixed effects binary choice models and study the corresponding incidental parameter problem. The standard maximum likelihood estimator is consistent under asymptotics where all panel dimensions grow at a constant rate, but it has an asymptotic bias in its limiting distribution, invalidating inference even in situations where the bias appears to be small. Thus, we propose two different bias-corrected estimators . Monte Carlo simulations confirm their desirable statistical properties. We apply these estimators in a reassessment of the most commonly studied determinants of the extensive margin of trade . Both true state dependence and unobserved heterogeneity contribute considerably to trade persistence and taking this persistence into account matters significantly in identifying the effects of trade policies on the extensive margin.
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propose new bias-corrected dynamic binary choice estimators with three sets of high-dimensional fixed effects
consider dynamic three-way fixed effects binary choice models and study the corresponding incidental parameter problem. The standard maximum likelihood estimator is consistent under asymptotics where all panel dimensions grow at a constant rate, but it has an asymptotic bias in its limiting distribution, invalidating inference even in situations where the bias appears to be small. Thus, we propose two different bias-corrected estimators
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We apply these estimators in a
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demonstrates that both
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strongly
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that