Making Gravity Great Again
The gravity model is now widely used for policy analysis and hypothesis testing, but different estimators give sharply different parameter estimates and popular estimators are likely biased because dependent variables are limited-dependent, error v...
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| Format: | Working Paper |
| Language: | English |
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World Bank, Washington, DC
2020
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| Online Access: | http://documents.worldbank.org/curated/en/100241599665436997/Making-Gravity-Great-Again http://hdl.handle.net/10986/34477 |
| Summary: | The gravity model is now widely used for
policy analysis and hypothesis testing, but different
estimators give sharply different parameter estimates and
popular estimators are likely biased because dependent
variables are limited-dependent, error variances are
nonconstant and missing data frequently reported as zeros.
Monte Carlo analysis based on real-world parameters for
aggregate trade shows that the traditional Ordinary Least
Squares estimator in logarithms is strongly biased
downwards. The popular Poisson Pseudo Maximum Likelihood
model also suffers from downward bias. An Eaton-Kortum
maximum-likelihood approach dealing with the identified
sources of bias provides unbiased parameter estimates. |
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