The policy making process requires effective forecasting, recommending, and monitoring of policy outcomes. In this Section, the Learner will have the opportunity to explore each of these facets of the policy analysis process.
Dunn, W. (2012): Chapters 4, 5, 6, 7
Ghosh, C., & Raychaudhuri, A. (2010)
Forecasting Policy Outcomes
Compare Forecasting Methods
Forecasting policy outcomes takes time and practice to become proficient in this skill. Forecasting the outcome of a policy is necessary for a decision maker to understand the impact of the policy if implemented. While forecasting is not a guarantee, it provides an estimate of the future.
Prepare a paper that includes a discussion of the following points:
1. What are the various forecasting methods that are available?
2. Compare and contrast the strengths and weaknesses of each of the methods you identified above in question one.
Length: 4-5 pages (app. 350 words per page)
Your paper should demonstrate thoughtful consideration of the ideas and concepts that are presented in the course and provide new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and the current APA standards.
This material may consist of step-by-step explanations on how to solve a problem or examples of proper writing, including the use of citations, references, bibliographies, and formatting. This material is made available for the sole purpose of studying and learning - misuse is strictly forbidden.In a strict sense, forecasting is taking past performance and making statements about the future. For example, global climate change is a forecast of changes in weather patterns based on what we have observed about the past. There are various methods used to make a forecast. Some are mathematical and data-based, some are logical and are assumption-based, and some are in-between. Within the arena of policy analysis there are three principal forms of forecasts: Projections, predictions, and conjectures.
Projections are based on extrapolation of past and current trends. A relationship between historical variables is established, and the model is projected into the future. For example, a simple growth model might an equation relating the population and the year. The equation relates the population to the year. To obtain the forecast of a future year’s population, plug the future year into the equation and the forecasted population is the result. Models can be as simple or as complex as we like. Statisticians worry about the quality of the model and the possible error in the modeled values, and it is important for policy analysts to understand the sources of uncertainty. Most statistical packages will perform analyses and give the user an idea of the quality of the model with “goodness of fit” statistics.
Projections implicitly assume that...