The focus of this analysis involves analyzing key economic themes such as demand, production, cost, and market structure with quantitative techniques like regression analysis and linear programming for a specific company. For purposes of this project you will select a product or service with substantial data.
Some possible topics: unemployment and crime, exports and underdeveloped countries, demand/supply of higher education, air pollution and population, etc.
You will formulate an introduction and problem statement and collect data sources.
Introduction and Statement of Problema. Introduction and Statement of Problem: What problem are you trying to solve? This is where you would discuss key information; why the issue/problem is important, sources of data you plan to use, estimation procedure you will use, variables, assumptions, weaknesses of data, etc. (Some possible topics: unemployment and crime, exports and underdeveloped countries, demand/supply of higher education, air pollution and population, etc. )
i. Introduce and describe the product or service being evaluated. Why the issue/problem is important
ii. Model Formation: This refers to the model, hypothesis, or theoretical framework that will be used to explain and/or forecast some variables. In the usual case, the model will be in the form of functional equations. QD = f(P, Y, …)
iii. Data Sources: Include the sources of data you plant to use, complete description with references.
1. Variables: What variables are used? Why are the variables used in the model?, Consider relation among measurable variables, what is the impact of an independent variable X on a dependent variable Y, what are some additional independent variable that could influence variable Y
2. Assumptions of Data: Consider the following in your analysis; accurate/consistent, sample is representative of the population, unbiased, efficient
3. Weaknesses of Data: Currency, not complete data set, biased selection, not scientifically accurate
4. Estimation Procedure: Consider what estimation procedure you plan to use (if you are using time series data, be sure to account for the identification problem). Why this particular procedure was chosen.