Estimating Gravity Equation Models in the Presence of Sample Selection and ...

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英文文献：Estimating Gravity Equation Models in the Presence of Sample Selection and Heteroskedasticity-在存在样本选择和异方差的情况下估计重力方程模型
英文文献作者：Xiong, Bo,Chen, Sixia
英文文献摘要：
Gravity equation models are widely used in international trade to assess the impact of various policies on the patterns of trade. Although recent literature provides solid micro-foundations for the gravity equation model, there is no consensus on how to estimate a gravity equation model in the presence of the two stylized features of trade data: frequent zeros and heteroskedasticity. We propose a Two-Step Nonlinear Least Square estimator that satisfactorily deals with both problems. Monte-Carlo experiments show that the proposed estimator strictly outperforms the Poisson Pseudo Maximum Likelihood (PPML), the Heckman sample selection model, and the E.T.-Tobit estimators, and that it weakly dominates the Truncated PPML model in the estimation of the intensive margin of trade. An empirical study of world trade in 1986 suggests that currency union and regional trade agreements facilitate trade primarily through improving market access, as opposed to intensifying pre-existing trade.

重力方程模型在国际贸易中被广泛使用，以评估各种政策对贸易模式的影响。虽然最近的文献为引力方程模型提供了坚实的微观基础，但是对于如何在贸易数据的两个类型化特征:频繁的零和异方差的情况下估计一个引力方程模型还没有达成共识。我们提出了一个两步非线性最小二乘估计，它能令人满意地处理这两个问题。蒙特卡罗实验表明，所提估计量严格优于泊松伪极大似然(PPML)估计量、Heckman样本选择模型和E.T.-Tobit估计量，并且在密集交易边际的估计上弱于截尾的PPML估计量。1986年对世界贸易的一项实证研究表明，货币联盟和区域贸易协定主要通过改善市场准入来促进贸易，而不是加强已有的贸易。

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