Announcing the Option to run Autobox with where it will calculate Price elasticities automatically. Why is this so powerful? If you have ever calculated a price elasticity, you might already know the modeling tradeoffs by using LOGS for a simplistic trick to quickly build the model to get the elasticity and the downside of doing so. We aren't trading off anything here and using Autobox's power to do build a robust model automatically and considerig the impact of lags, autocorrelation and outliers.
We are rolling out an Option for Autobox 7.0 Command Line Batch to calculate "Short-Run" Price Elasticities. The Price Elasticity Option can be purchased to produce Price elasticities automatically AND done the right way as opposed to assuming the model form to get a fast, but sub-standard econometric approach to calculating Price elasticities. The Option allows you to specify what % change in price, specify the problems you want to run and then let Autobox run and model the data and produce a one period out forecast. Autobox will run again on the same data, but this time using the change in price and generate another forecast and then calculate the elasticity with the supporting math used to calculate it stored in a report file for further use to prepare pricing strategy. That introduction is great, but now let's dive into the importance of modeling the data the right way and what this means for a more accurate calculation of elasticities! Click on the hyperlinks when you see them to follow the example.
For anyone who has calculated a price elasticity like this it was in your Econ101 class. You may have then been introduced to the "LOG" based approach which is a clean and simple approach to modeling the data in order to do this calcuation of elasticity as the coefficient will be the elasticity. Life isn't that simple and with the simplicity there are some tradeoffs you may not be aware that came with this simplicity. We like to explain these tradeoffs like this "closing the door, shutting the blinds, lowering the lights and doing voodoo modeling". Most of tthe examples you see in books and literally just about everywhere well intentioned analysts try and do their calculations useing a simple Sales (Quantity) and Price model. There typically is no concern for any other causals as it will complicate this simplistic world. Why didn't your analyst tell you about these tradeoffs? You probably didn't ask. If you did, the analyst would say to you in a Brooklyn accent "Hey, read a book!" as the books show it done this way. Analysts take LOGS of both variables and then can easily skip the steps needed in the first link above and calculate the elasticity, running a simple regression ignoring the impact of autocorrelation or other important causals and voila you have the elasticity which is simply found as the coefficient in the model. The process is short and sweet and "convenient", but what happens when you have an outlier in the data? What happens if you need to include a dummy variable? What about the other causals? What about lags in the X? What about autocorrelation? Does their impact just magically disappear? No. No and no. Read more about this with a case study on our Blog.
Forecasting Weekly Beer Sales
