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A Generalized Poisson-Pseudo Maximum Likelihood Estimator*

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Empirical application shows G-PPML provides a more efficient estimator compared to PPML in trade flows data, indicating potential advantages.

The proposed G-PPML estimator reduces standard errors by approximately 20% compared to PPML, improving estimation efficiency.
CVMR is a crucial condition for efficiency in PPML, and G-PPML effectively tests this assumption using iterated GMM methodology.
An empirical application to trade flows data reveals that while CVMR is often satisfied, it may not hold in every instance.
Establishing asymptotic properties for G-PPML supports its use under general assumptions about conditional variance.

Kwon et al. (2025) studied this question.

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Empirical application shows G-PPML provides a more efficient estimator compared to PPML in trade flows data, indicating potential advantages.