Get an overview of economic analysis.
- [Narrator] Economic forecasting is complex. Most economic forecasts are built on three key parts, data, econometrics, and judgment. These three aspects of any forecast form the stool on which our results are built. Take away any of the three legs and the stool falls. Our focus is going to be on understanding how to use that data and combine it with data analysis through econometrics and make judgements based on the results that are going to form the outcomes for our forecasts.
In particular, we're going to be looking at two types of data, microeconomic data and macroeconomic data. Microeconomic data is firm-level data. It's focused on data about individual consumers, individual companies, et cetera. Microeconomic data is great for making powerful analysis, but it's harder to gather. There are simply fewer sources of microeconomic data out there. It's harder to gather as a result. Macroeconomic data, on the other hand, is national-level data.
Think about things like gross domestic product, unemployment rates, interest rates. These are factors that affect everyone across the country. This kind of data is easier to gather and find, but it's harder to analyze. In particular, if we're trying to make forecasts about GDP or unemployment or interest rates, we're often going to face an insidious problem what we call omitted variables. Omitted variables simply means that there's other factors that we can't take into account when we're trying to forecast based on macroeconomic data.
Our focus today is going to be on making forecasts. In particular, we've got someone joining us. His name is Ed. Ed is a business economist at an online rental property company. Ed is trying to forecast housing demand and interest rates to help with his firm's expansion plans.
Professor Michael McDonald demonstrates how to harness the wealth of information available on the Internet to forecast statistics such as industry growth, GDP, and unemployment rates, as well as factors that directly affect your business, like property prices and future interest rate hikes. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. He also covers time series exponential smoothing, fixed effects regression, and difference estimators. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.
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- Identify a good source of free data.
- Name the term for the estimate of the impact of an X variable on a Y variable.
- Tell which statistic offers a bounds on the estimate of the impact of an X variable on a Y variable.
- Assess the type of variable that can be used to capture fixed effects.
- Cite the method by which a forecast can be done with a regression.