The GTAP v7 model in Julia
Maros Ivanic
Abstract
In this work, I introduce a formulation of the GTAP version 7 model (Corong et al., 2017) in an open-source algebraic modeling language, JuMP (Lubin et al., 2023), implemented in Julia (Bezanson et al., 2017), that closely follows the specification of the model in GEMPACK (Horridge et al., 2019), including equation and variable names. Unlike the linearized GEMPACK version, my formulation is in levels. However, my formulation of the GTAP model is quite different from the levels formulation in GAMS (Bussieck and Meeraus, 2004) by Mensbrugghe (2018), in following more closely the variable and equation names of the GEMPACK model. I show that my model produces essentially the same results as the GEMPACK model. Because it is expressed in levels, with unabridged functional forms underpinning its behavioral equations (e.g., containing all parameters in the case of CES functions), my model can address a wider range of policy questions, especially those involving parameter changes. Calibrating the model to additional data, e.g., quantities, additionally allows it to be used in scenario analyses involving absolute productivity metrics. As an important benefit, my implementation of the GTAP model using open-source Ipopt solver (W¨achter and Biegler, 2006), requires no software license to solve the model.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.