<p>I take from your comments that you are interested in airline seating prices.</p>

<p>The subject of airline pricing is very complex: </p>

<ul>
<li>There are different seating classes at different sizes, where the offered quantities vary over time</li>
<li>There is overbooking, because more expensive tariffs allow "last second changing" into and out of a specific flight, and companies try to accommodate for that</li>
<li>Demand for the same flights vary with season and even day of the week. Typical example: There is higher demand on short distance flights for Thursday Evening/Friday Afternoon and Sunday night / Monday morning flights</li>
<li>Elasticities vary with the distance: The more short term flights are being booked, the "more desperate" customers are, and the higher the faires are that companies can charge them. However, at the "last minute", the companies potentially become "desperate" to fill seatings themselves, and offer low prices themselves</li>
</ul>

<p>I remember sitting into one, two lectures on this when a professor of Business Administration was treating the subject. The elasticities part of could equally well be treated by Economists, but most who look at the subject are BAs involved with logistics and similar - I would refer you to look for their literature (I have none at hand) for more detailed information.</p>

CC BY-SA 3.0
2015-03-22T15:06:05.837Z
2015-03-22T15:06:05.837Z
FooBar
<p>I am having trouble formulating the concept I am thinking about. It has to do with looking at observed behavior of the sales of a particular product during each hour of the day, and trying to adjust prices accordingly.</p>

<p>If you have a time series of 24 pairs of values, $(x_1,y_1), (x_2,y_2), \ldots, (x_{24},y_{24})$, where $(x_t,y_t)$ represents the data at time $t$ o'clock: $x_t$ is the number of sales and $y_t$ is the average price paid (both at time $t$).</p>

<p>Given this information, is there anything that can be deduced about how the product should be priced? What about by the hour?</p>

<p>My background is not in economics, so I don't know if there is a framework for this type of problem.</p>

CC BY-SA 3.0
2015-03-22T02:56:41.487Z
What do you mean "about how a product should be priced?" What kind of good are we talking about? Do you have any more context? Is your data from one specific firm or are you looking at market averages?
jmbejara
CC BY-SA 3.0
2015-03-22T08:39:31.820Z
To follow up on @jmbejara's quesiton, it would be useful to know why you have different prices for different hours. Figuring out the best price often depends upon figuring out the shape of the demand curve, so we need to know whether the demand curve moves around during the day or whether you observations are just various points on a static demand curve.
Ubiquitous
CC BY-SA 3.0
2015-03-22T12:48:29.187Z
I'm thinking more on a general level, but maybe this example can help: Suppose the product is a specific airline flight from NY to London, and instead of tracking hours, we track averages over months. So we know that in February x purchases are made at y price, for example. Same for all other months, with 10 years of history. How then should the airline price its future flights based on this demand history?
TecoDeco
CC BY-SA 3.0
2015-03-22T13:58:20.863Z
So we're facing different demand curves: Depending on time left to the flight, people's elasticities change, and airlines use this.
FooBar
CC BY-SA 3.0
2015-03-22T14:49:33.880Z
@FooBar, so every month has its own demand curve? How does this help to set prices? How do the airlines adjust their prices? Is this known?
TecoDeco
CC BY-SA 3.0
2015-03-25T03:22:06.557Z
You can certainly have different factors driving demand across time. However, you must think about the fact that changing prices will also change demand. You may not capture as much excess profit as you think.
123
<time-series><probability>
Is there a name for this type of problem?
TecoDeco
# Is there a name for this type of problem?

(Asked by: username_0 on 2015-03-22)

I am having trouble formulating the concept I am thinking about. It has to do with looking at observed behavior of the sales of a particular product during each hour of the day, and trying to adjust prices accordingly.

If you have a time series of 24 pairs of values, $(x\_1,y\_1), (x\_2,y\_2), \ldots, (x\_{24},y\_{24})$, where $(x\_t,y\_t)$ represents the data at time $t$ o'clock: $x\_t$ is the number of sales and $y\_t$ is the average price paid (both at time $t$).

Given this information, is there anything that can be deduced about how the product should be priced? What about by the hour?

My background is not in economics, so I don't know if there is a framework for this type of problem.

**Tags:** time-series, probability
> **Comments:**
> **username_1**: What do you mean "about how a product should be priced?" What kind of good are we talking about? Do you have any more context? Is your data from one specific firm or are you looking at market averages?  
> **username_2**: To follow up on @jmbejara's quesiton, it would be useful to know why you have different prices for different hours. Figuring out the best price often depends upon figuring out the shape of the demand curve, so we need to know whether the demand curve moves around during the day or whether you observations are just various points on a static demand curve.  
> **username_0**: I'm thinking more on a general level, but maybe this example can help: Suppose the product is a specific airline flight from NY to London, and instead of tracking hours, we track averages over months. So we know that in February x purchases are made at y price, for example. Same for all other months, with 10 years of history. How then should the airline price its future flights based on this demand history?  
> **username_3**: So we're facing different demand curves: Depending on time left to the flight, people's elasticities change, and airlines use this.  
> **username_0**: @FooBar, so every month has its own demand curve? How does this help to set prices? How do the airlines adjust their prices? Is this known?  
> **username_4**: You can certainly have different factors driving demand across time. However, you must think about the fact that changing prices will also change demand. You may not capture as much excess profit as you think.  

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## 1 answers

### Answer by username_3 on 2015-03-22. Score: 1

I take from your comments that you are interested in airline seating prices.

The subject of airline pricing is very complex:

* There are different seating classes at different sizes, where the offered quantities vary over time
* There is overbooking, because more expensive tariffs allow "last second changing" into and out of a specific flight, and companies try to accommodate for that
* Demand for the same flights vary with season and even day of the week. Typical example: There is higher demand on short distance flights for Thursday Evening/Friday Afternoon and Sunday night / Monday morning flights
* Elasticities vary with the distance: The more short term flights are being booked, the "more desperate" customers are, and the higher the faires are that companies can charge them. However, at the "last minute", the companies potentially become "desperate" to fill seatings themselves, and offer low prices themselves

I remember sitting into one, two lectures on this when a professor of Business Administration was treating the subject. The elasticities part of could equally well be treated by Economists, but most who look at the subject are BAs involved with logistics and similar - I would refer you to look for their literature (I have none at hand) for more detailed information.


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