This course lays the foundations for building models of economic systems using mathematical programming. A brief introduction of the relationship between optimization and applied economics is followed by a survey of the GAMS language – a high level piece of software that was specifically designed to facilitate the formulation and solution of mathematical programming (optimization) problems. Basic models of the consumer and the producer are introduced and approaches to calibrating those models are addressed.
Students will Learn:
- How to modify the models of consumers to reflect government policies such as taxes, prohibitions, rations, etc.
- How to modify models of producers to reflect changes in government regulations, introduction of new technology, contracts, etc.
Module 1 - Single Agent Models, Benchmarking, Alternative Technology Specification (Including Linear Production), and Dynamic Models
- Learning Objective: Learning Objective: Survey Basic Economic Model Building for Individual Agents (Consumers, Producers, and Competitive Sectors), Define Benchmarking and Learn to Benchmark Cobb-Douglas and Constant Elasticity of Substitution Utility and Production Relationships, Address Implementation Issues, Demonstrate Modifications of Consumer Problems to Reflect Interactions with Other Agents, Test Efficiency of Firm Behavior, and Realize the Flexibility and Simplicity of Linear Production.
Module 2 - Nonparametric Efficiency Testing
- Learning Objective: Learning Objective: Define Tests for Nonparametric Efficiency (Including the Cases of: One Output with Multiple Inputs, Two Outputs with Multiple Inputs, and Multiple Output with Multiple Inputs) Applied to Test for Technical Efficiency, Cost Minimization Efficiency, Profit Maximization Efficiency, and Revenue Maximization Efficiency.
Module 3 – Endogenous Price Models and Introduction to Modeling Decision Making Under Risk
- Learning Objective: Understand the Formulation of Endogenous Price Models and the Role of Market Power Assumptions, explore the building blocks of Single-Agent, Static, Stochastic Models, understand the concept of Static Risk, explore the nature of Choice Variables and Random Variables, and Their Interrelationship Through the Payoff Functions, Common Utility Functions, and the classifications of utility functions according to how Risk Aversion Measures Change with Wealth.
Module 4 – Single Agent, Dynamic Determinisitic Models: Inventory, and Scheduling Models; Example – A Maize Marketing System
- Learning Objective: Explore How Time Is Included in Planning Models including Models of Inventory Management, and Scheduling Models and Take an in Depth Analytical Look at an Example Model for a Maize Marketing System.
Module 5 – Farm Planning Example, Economic Growth Models, and the Eta-Macro Example
- Learning Objective: Assess the PC/LP example, other types of Dynamic Models, and breakdown the ETA-Macro Model.
- Professionals interested in applied economic analysis that have completed an undergraduate degree with a strong mathematical background.