© Scott McDonald
Recursive Dynamic CGE Modelling
Target Audiences
The target audiences for this course are
- Potential model developers, e.g., graduate students, researchers, academics, etc.,
- Model users, e.g., policy analysts, researchers, consultants, etc.
Course Aims
To develop the CGE modelling skills of participants (using GAMS) so they
- understand the behavioural relationships used in RDYN CGE models;
- understand the impact of different behavioural relationships used to update model parameters in RDYN CGE models;
- understand the calibration of the model parameters in RDYN CGE models;
- can formulate appropriate RDYN CGE policy experiments; and
- can interpret the results generated by RDYN CGE models.
Course Objectives
On completion of the course the participants will be able:
- formulate and code appropriate policy experiments;
- identify and understand the strengths and limitations of RDYN CGE models;
- modify behavioural relationships that control the updating of model parameters;
- interpret the results from RDYN CGE models; and
- identify, and present, the policy implications of simulations using RDYN CGE models.
Approach
This course does NOT use a GUI (Graphical User Interface) to access GAMS. Experience has demonstrated that the use of GUI’s by participants on training programmes typically limits the development of the skills needed to be a good CGE modeller or user of CGE models. The development of GAMS, or GEMPACK, programming skills greatly extends the ability of the user to exploit the power of CGE models, while, at the same time, opening up the potential that participants can change behavioural relationships in CGE models
‘Black Boxes’
CGE models are NOT ‘black boxes’. Basic GAMS programming skills, and an understanding of economic theory, demonstrates that allegations that CGE models are ‘black boxes’ are false. Claims that CGE models are ‘black boxes’ say more about the claimant than about CGE models.