Course Information

In this cornerstone course, you will learn the fundamental economic theory of food and fiber production and consumption, agricultural and resource markets, and social welfare as influenced by the agricultural and resource sectors. Subsequent courses build on the knowledge gained during this course, which places a major emphasis on the application of this theory to current agricultural and resource problems.

This course uses extensive programming and data visualization platforms to analyze questions related to economic decision making. Students will learn how to use R and develop mathematical models to organize and interpret data as they relate to questions surrounding the agricultural and resource sectors.

This course gives you an introduction to basic econometric methods and illustrates how these methods can be applied to economic problems. The focus is on univariate and multivariate regression, regression with limited dependent variables, instrumental variables, and basic panel data methods. The course involves use of the programming language R and its accompanying development environment RStudio.

Course Objectives:

• Demonstrate basic knowledge of univariate and multivariate quantitative methods.
• Select appropriate methods and conduct quantitative analyses using economic data.
• Produce econometric models and apply models to economic problems.

This course provides a comprehensive and in-depth survey of commodity price analysis as commodity prices directly influence the economy. The course largely takes the perspective of forecasting as it teaches students how commodity prices influence farm income, consumer expenditures, and in many countries, the level of export earnings.  For nearly half of the world’s population, income is determined primarily by the price received for commodities.  In addition, tremendous resources, both in the private and public sector, are devoted to analyzing and forecasting commodity prices. Throughout the course, we emphasize theoretical relationships, analytical techniques, and practical applications. 

This course provides an overview of financial management concepts and their application to manage risk. The course focuses on the evaluation of financial performance, risk management, financial valuation, cost of capital, and sustainability. Based on agricultural and business case studies, the goal is to provide students with the ability to make or appraise decisions when a risk-return trade-off is at stake in various situations: investment in equity, debt contracting, strategic and risk-management decisions. The main concepts of the course are:

• Financial analysis (Profitability, Debt Risk and Leverage, Market-Based Ratios)
• Cost of Capital (Capital Asset Pricing Model, Weighted Average Cost of Capital)
• Risk Management (Risk Management Dashboard, Diversification, Vertical integration, Insurance)
• Net Present Value (Time Value of Money, Free Cash Flows)
• Valuation (Discounted Cash Flow Model, Comparables)
• Sustainability (Sustainable Development Goals, Impact Investment, Stranded Assets) 

This course will cover the theory of interrelated markets in both perfectly competitive and imperfectly competitive markets as applied to the food supply chain or food system.  Contemporary issues, such as COVID-19, food borne illness, climate change, trade policies, to name a few, will be explored in crop and animal production, food wholesaling, food processing, and food retailing, for their likely impact throughout the entire food supply chain. The theory of economic policy as applied to these markets will also be presented to gain a framework for assessing the types of responses that are viable and likely most effective in addressing these issues.

Application of econometric and statistical techniques for inferential and predictive problems. Overview of algorithmic thinking, computational techniques, and data science conventions. 

This course teaches you how to forecast data with a focus on basic linear and non-linear time series model. It includes a strong emphasis on programming and computational implementation of time series model-selection techniques and practical applications. At the end of this course, you will be able to graphically examine components of time series data: trend, seasonal, cyclical, and irregular components; write and execute computer programs for commonly used time series forecasting models; and form efficient forecasts by using a combination of forecasting methods.

This course studies the nature and linkages between agriculture, natural resources, and macroeconomic variables. Theories and methods are used to establish and quantify these linkages. Macroeconomic models are presented to measure the effect of national output, unemployment, interest rates, economic growth, exchange rates, and allocation and distribution of resources on the agricultural sector, the environment, and the international economy. 

In the program's culminating course, you will work with program peers to address a real-world challenge facing the food and ag or resource sector. Mentoring will be provided from Virginia Tech faculty as well as industry/government practitioners. Learn more about the Virginia Tech online ms ag and applied economics capstone project.