Publication Type
Master Thesis
Version
publishedVersion
Publication Date
2007
Abstract
The gravity model is a workhorse for econometric studies of the impact of regional trade agreements (RTAs). Despite its initial lack of theoretical basis, the model has been successfully derived from various trade theories. The latest theoretical derivation by Anderson and van Wincoop (2003) reveals that prior gravity studies have made the critical error of omitting the multilateral resistance variable, which results in biased estimates. Other recent studies have highlighted empirical issues with the commonly used procedure of log-linearizing the gravity model and estimating the parameters using Ordinary Least Squares (OLS) regression. Silva and Tenreyro (2006) point out that this method yields inconsistent estimates in the presence of heteroskedasticity. Helpman, Melitz and Rubinstein (2004) show that the concomitant practice of dropping observations with zero trade values (because the log-linearized model is not defined for such observations) will also give rise to biased results. To deal with these two issues of inconsistency and bias, we estimate the gravity model in its multiplicative form. Both crosssectional and panel data analysis are performed, employing Poisson Pseudo Maximum Likelihood Estimator and Poisson Quasi-Conditional Maximum Likelihood Estimator respectively. Whilst analyzing the impact of RTAs in the light of the new estimation methods, this study will also re-evaluate the impact of the Asean Free Trade Area in the context of other major RTAs.
Keywords
gravity model, intraregional trade, Ordinary Least Squares, regional trade agreements, trade diversion
Degree Awarded
MSc in Economics
Discipline
Econometrics | International Business
Supervisor(s)
HOON, Hian Teck
Publisher
Singapore Management University
City or Country
Singapore
Citation
TAN, Hui Chin.
Regional Trade Agreements Revisited. (2007).
Available at: https://ink.library.smu.edu.sg/etd_coll/35
Copyright Owner and License
Author
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.