New nonlinear estimators of the gravity equation
The gravity model of international trade is often applied by economists to explain bilateral trade between countries. Nevertheless, some estimation practices have been subject to criticism, namely how zero trade values and the heteroskedasticity are handled. This paper proposes new nonlinear estimat...
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2020
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| Online Access: | http://dx.doi.org/10.1016/j.econmod.2020.12.011 https://www.sciencedirect.com/science/article/pii/S0264999320312761 http://hdl.handle.net/10576/40171 |
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| _version_ | 1857415086597996544 |
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| author | Ayman, Mnasri |
| author2 | Nechi, Salem |
| author2_role | author |
| author_facet | Ayman, Mnasri Nechi, Salem |
| author_role | author |
| dc.creator.none.fl_str_mv | Ayman, Mnasri Nechi, Salem |
| dc.date.none.fl_str_mv | 2020-12-13 2023-02-20T09:11:13Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://dx.doi.org/10.1016/j.econmod.2020.12.011 Mnasri, A., & Nechi, S. (2021). New nonlinear estimators of the gravity equation. Economic Modelling, 95, 192-202. 0264-9993 https://www.sciencedirect.com/science/article/pii/S0264999320312761 http://hdl.handle.net/10576/40171 192-202 95 |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Gravity model Heteroscedasticity Structural zeros Generalized Heckman two-step Generalized nonlinear least squares PPML |
| dc.title.none.fl_str_mv | New nonlinear estimators of the gravity equation |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | The gravity model of international trade is often applied by economists to explain bilateral trade between countries. Nevertheless, some estimation practices have been subject to criticism, namely how zero trade values and the heteroskedasticity are handled. This paper proposes new nonlinear estimation techniques to address these issues. In particular, we propose standard and generalized versions of the nonlinear Heckman two-step approach that do not require the log-linearization of the gravity equation and corrects for non-random selection bias, and a generalized nonlinear least squares estimator that can be viewed as an iterative version of the normal family Quasi-Generalized Pseudo-Maximum-Likelihood estimator. Monte Carlo simulations show that our proposed estimators outperform existent linear and nonlinear estimators and are very efficient in correcting the selection bias and reducing the standard deviation of the estimates. Empirical results show that previous studies have overestimated the contribution of variables such as importer’s income, distance, remoteness, trade agreements, and openness. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | qu_56634d46d4180dc05fc9c8c1bb2ef788 |
| identifier_str_mv | Mnasri, A., & Nechi, S. (2021). New nonlinear estimators of the gravity equation. Economic Modelling, 95, 192-202. 0264-9993 192-202 95 |
| language_invalid_str_mv | en |
| network_acronym_str | qu |
| network_name_str | Qatar University repository |
| oai_identifier_str | oai:qspace.qu.edu.qa:10576/40171 |
| publishDate | 2020 |
| publisher.none.fl_str_mv | Elsevier |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| spelling | New nonlinear estimators of the gravity equationAyman, MnasriNechi, SalemGravity modelHeteroscedasticityStructural zerosGeneralized Heckman two-stepGeneralized nonlinear least squaresPPMLThe gravity model of international trade is often applied by economists to explain bilateral trade between countries. Nevertheless, some estimation practices have been subject to criticism, namely how zero trade values and the heteroskedasticity are handled. This paper proposes new nonlinear estimation techniques to address these issues. In particular, we propose standard and generalized versions of the nonlinear Heckman two-step approach that do not require the log-linearization of the gravity equation and corrects for non-random selection bias, and a generalized nonlinear least squares estimator that can be viewed as an iterative version of the normal family Quasi-Generalized Pseudo-Maximum-Likelihood estimator. Monte Carlo simulations show that our proposed estimators outperform existent linear and nonlinear estimators and are very efficient in correcting the selection bias and reducing the standard deviation of the estimates. Empirical results show that previous studies have overestimated the contribution of variables such as importer’s income, distance, remoteness, trade agreements, and openness.Elsevier2023-02-20T09:11:13Z2020-12-13Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1016/j.econmod.2020.12.011Mnasri, A., & Nechi, S. (2021). New nonlinear estimators of the gravity equation. Economic Modelling, 95, 192-202.0264-9993https://www.sciencedirect.com/science/article/pii/S0264999320312761http://hdl.handle.net/10576/40171192-20295enhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:qspace.qu.edu.qa:10576/401712024-07-23T13:52:31Z |
| spellingShingle | New nonlinear estimators of the gravity equation Ayman, Mnasri Gravity model Heteroscedasticity Structural zeros Generalized Heckman two-step Generalized nonlinear least squares PPML |
| status_str | publishedVersion |
| title | New nonlinear estimators of the gravity equation |
| title_full | New nonlinear estimators of the gravity equation |
| title_fullStr | New nonlinear estimators of the gravity equation |
| title_full_unstemmed | New nonlinear estimators of the gravity equation |
| title_short | New nonlinear estimators of the gravity equation |
| title_sort | New nonlinear estimators of the gravity equation |
| topic | Gravity model Heteroscedasticity Structural zeros Generalized Heckman two-step Generalized nonlinear least squares PPML |
| url | http://dx.doi.org/10.1016/j.econmod.2020.12.011 https://www.sciencedirect.com/science/article/pii/S0264999320312761 http://hdl.handle.net/10576/40171 |