Learn about the concept of serial correlation and its use in forecasts with an example.
- [Instructor] Ed was intrigued by what we learned…about exponential smoothing being an effective predictor…for property prices.…Now he's wondering about a concept called serial correlation…or auto correlation…and he's wondering if this data on property values…for commercial real estate might be serially correlated.…What is serial correlation?…Well essentially serial correlation simply means…that the future values of a variable…are related to the past values of the variable.…
In Ed's case, this means that the future values of property…are driven by whatever previous changes…in property values are.…Why is this useful?…Well if property values are serially correlated,…it means that we can look at historical changes…in that property value in order to forecast…what future changes might be.…Now it's important to understand…that when we're building models using serial correlation,…we've got to be very careful…to make sure that we're using the right kind of analysis.…
We'll take a look at that in just a moment.…I'm in the 02_02 begin file from the exercise files folder.…
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.