<p>I have transaction data for luxury retail domain. It is roughly 5 years of transaction data which provides week / month level details in terms of number of units being sold at a given price point. I want to use this data to create elasticity curve. The elasticity curves are needed at a product level so that we can estimate potential units sold for any given price point.</p>
<p>However, I am not sure what is the ideal duration of the data which I should consider for this? Should I consider entire history or just most recent data? Could you please share point of view on how much data is sufficient and why?</p>

CC BY-SA 4.0
2024-05-22T22:10:50.773Z
There does not exist an ideal time frame, it depends on the nature of the data on your prediction horizon - long run or short run predictions - as well as other matters such as data availability. However, I think it could be argued that the more data the better so at least you need a positive reason to exclude any data.
Jesper Hybel
CC BY-SA 4.0
2024-05-23T06:14:20.460Z
Thank you @JesperHybel. Based on a few other paper, we are attempting this with 2 years of data for now. We will increase the timeframe based on the results.
Sourabh
CC BY-SA 4.0
2024-05-23T07:34:05.043Z
One of the dangers of using short time series such as 2 years is that you underestimate the probability of large shocks. Example: The recent energy crisis and electricity prices. This may or may not be a relevant consideration, depending on the nature of your project.
Jesper Hybel
<elasticity>
Ideal timeframe to consider for elasticity curve
Sourabh
# Ideal timeframe to consider for elasticity curve

(Asked by: username_0 on 2024-05-22)

I have transaction data for luxury retail domain. It is roughly 5 years of transaction data which provides week / month level details in terms of number of units being sold at a given price point. I want to use this data to create elasticity curve. The elasticity curves are needed at a product level so that we can estimate potential units sold for any given price point.

However, I am not sure what is the ideal duration of the data which I should consider for this? Should I consider entire history or just most recent data? Could you please share point of view on how much data is sufficient and why?

**Tags:** elasticity
> **Comments:**
> **username_1**: There does not exist an ideal time frame, it depends on the nature of the data on your prediction horizon - long run or short run predictions - as well as other matters such as data availability. However, I think it could be argued that the more data the better so at least you need a positive reason to exclude any data.  
> **username_0**: Thank you @JesperHybel. Based on a few other paper, we are attempting this with 2 years of data for now. We will increase the timeframe based on the results.  
> **username_1**: One of the dangers of using short time series such as 2 years is that you underestimate the probability of large shocks. Example: The recent energy crisis and electricity prices. This may or may not be a relevant consideration, depending on the nature of your project.  

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