<p>If I understand your question properly, it is correct that we do more ordinary statistical analyses such as linear regression to determine relationships between economic variables (e.g. price and demand.)  Utility is a tool which gives us a model of consumer behaviour based on very loose assumptions about how consumers behave (convexity of preferences, monotonicity, etc.)  While the demand curve can be derived from utility if we somehow knew the full preferences of all consumers, it can also be approximated from data by observing the real-world relationship between price and demand.  It is very difficult/impossible to measure utility in the real world, so using regular statistical techniques like linear regression to estimate the relationship is the logical thing to do.</p>

<p>Of course, the real-world demand curve may not be linear, so a simple linear regression on observed data in the form $p = \beta_0 + \beta_1 q$ may not accurately the demand curve.  Here, theory may help you decide on what nonlinear terms you should include in your model, but you would still need to do the regression analysis on real-world data to actually find the parameters.</p>

CC BY-SA 3.0
2016-06-29T17:17:06.940Z
So it would not be sensible to estimate ordinal utilities for Demand curve derivation?
Erik Hambardzumyan
CC BY-SA 3.0
2016-06-29T17:31:14.453Z
@ErikHambardzumyan I have not encountered anyone using ordinal utilities to derive the demand curve for real-world data (usually it's the other way around; you use the observed demand curve to infer preferences).  I can't guarantee it doesn't have some niche use or academic interest, but for any real-world purpose, you would just estimate the curve using traditional regression techniques.
Blue_vision
CC BY-SA 3.0
2016-07-04T19:05:53.090Z
thank you a lot. What is then the practical usage of utility functions ?
Erik Hambardzumyan
CC BY-SA 3.0
2016-06-29T16:57:48.107Z
2016-06-29T16:57:48.107Z
Blue_vision
<p>My fundamental concern is whether in real life when applying to microeconomic theory, we derive the demand curve using utility and indifference curves approach, or we simply may get data on quantity demanded of the consumer for different prices and using linear regression derive the demand curve?</p>

CC BY-SA 3.0
2016-06-29T13:55:21.377Z
What do you mean by in real life?
Kontorus
<utility><supply-and-demand>
Demand curve deriviation
Erik Hambardzumyan
# Demand curve deriviation

(Asked by: username_0 on 2016-06-28)

My fundamental concern is whether in real life when applying to microeconomic theory, we derive the demand curve using utility and indifference curves approach, or we simply may get data on quantity demanded of the consumer for different prices and using linear regression derive the demand curve?

**Tags:** utility, supply-and-demand

> **Comments:**
> **username_1**: What do you mean by in real life?  

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## 1 answers

### Answer by username_2 on 2016-06-29. Score: 1 | Accepted answer

If I understand your question properly, it is correct that we do more ordinary statistical analyses such as linear regression to determine relationships between economic variables (e.g. price and demand.) Utility is a tool which gives us a model of consumer behaviour based on very loose assumptions about how consumers behave (convexity of preferences, monotonicity, etc.) While the demand curve can be derived from utility if we somehow knew the full preferences of all consumers, it can also be approximated from data by observing the real-world relationship between price and demand. It is very difficult/impossible to measure utility in the real world, so using regular statistical techniques like linear regression to estimate the relationship is the logical thing to do.

Of course, the real-world demand curve may not be linear, so a simple linear regression on observed data in the form $p = \beta\_0 + \beta\_1 q$ may not accurately the demand curve. Here, theory may help you decide on what nonlinear terms you should include in your model, but you would still need to do the regression analysis on real-world data to actually find the parameters.

> **Comments:**
> **username_0**: So it would not be sensible to estimate ordinal utilities for Demand curve derivation?  
> **username_2**: @ErikHambardzumyan I have not encountered anyone using ordinal utilities to derive the demand curve for real-world data (usually it's the other way around; you use the observed demand curve to infer preferences).  I can't guarantee it doesn't have some niche use or academic interest, but for any real-world purpose, you would just estimate the curve using traditional regression techniques.  
> **username_0**: thank you a lot. What is then the practical usage of utility functions ?  

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