2022
DOI: 10.1007/s10258-021-00203-w
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The Log of Gravity at 15

Abstract: We review the contribution of “The Log of Gravity” (Santos Silva and Tenreyro, Rev Econ Stat 88:641–658, 2006), summarize the main results in the ensuing literature, and provide a brief review of the state-of-the-art in the estimation of gravity equations and other constant-elasticity models.

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Cited by 46 publications
(25 citation statements)
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“…In the PPML, possible biases due to the amount of zero observations in the dependent variable are corrected while also accounting for heteroscedasticity. Further, Monte‐Carlo simulations show that the estimator performs well in spite a large proportion of zeros (also see Yotov et al, 2016, p. 20) and the validity of the estimator does not depend on very strong assumptions of the distribution of the data as for example would be the case for a zero‐inflated model (Silva & Tenreyro, 2021). Overall, the PPML estimator is widely accepted to be very well suited for gravity estimations.…”
Section: Methodology and Empirical Modelingmentioning
confidence: 96%
“…In the PPML, possible biases due to the amount of zero observations in the dependent variable are corrected while also accounting for heteroscedasticity. Further, Monte‐Carlo simulations show that the estimator performs well in spite a large proportion of zeros (also see Yotov et al, 2016, p. 20) and the validity of the estimator does not depend on very strong assumptions of the distribution of the data as for example would be the case for a zero‐inflated model (Silva & Tenreyro, 2021). Overall, the PPML estimator is widely accepted to be very well suited for gravity estimations.…”
Section: Methodology and Empirical Modelingmentioning
confidence: 96%
“…The regressions are estimated using the Poisson Pseudo‐Maximum Likelihood (PPML) estimator developed by Gourieroux et al (1984a) and popularised by Santos Silva and Tenreyro (2006, 2011). This estimation method is preferred by Blackburn (2007), Fally (2015), Santos Silva and Tenreyro (2022) and Kren and Lawless (2022) among others to overcome the potential bias related to heterogeneity arising from log‐linearizing (Mayer et al, 2019) as well to handle the problem of zeros. In PPML regression, only a correct specification of the conditional mean of the dependent variable is required for consistency of the regression estimator (Gourieroux et al, 1984b), and hence can be applied to dependent variables with nonnegative values without the need to explicitly specify a distribution for the dependent variable (Correia et al, 2020).…”
Section: Methodsmentioning
confidence: 99%
“…One cannot tell whether the sample selection depends on the covariates, but the sensitivity of the sample selection problem can be tested by treating the missing observations as zeros in our gravity equation. Theoretically, the disparity should be limited because the observations where the conditional mean is close to zero have low variance and therefore have a minimal contribution to the value of the pseudo log‐likelihood function (Silva & Tenreyro, 2021).…”
Section: Methodsmentioning
confidence: 99%