Which regression technique is used for calculating price elasticity in practice Since we need to consider 'Endogeneity' between price and quantity while calculating price elasticity and since linear regression cannot handle the phenomenon of endogeneity if objective of the model is to find causality, instrumental variable regression is used. 

Since theory backs the idea of using IV regression, does this happens in real world? I mean corporations and governments use IV regression for price elasticity or do they use linear regression despite it's shortcomings in it's inablity to handle endogeneity?
They use all types of methods. 

Often, papers will use a very basic linear regression model and submit to a low prestige journal so one can justify ones grant and boost the H index as much as possible when meta analyses cite your article to clean up the mess.

It's up to the meta analysis to take into account that not all models have correct methodology and create a meta-analytical model to correct for all these inaccuracies. 

Fortunately, there are sometimes dynamic lag models and the like to compare to and guess at an appropriate correction term for the others. 

Here is an example for gas prices.

https://www.google.com/url?sa=t&amp;source=web&amp;rct=j&amp;url=http://papers.tinbergen.nl/06106.pdf&amp;ved=0ahUKEwia2Me-85LLAhUUzmMKHWvjAd0QFgghMAE&amp;usg=AFQjCNHi6RH_xX7h9NLPcVRCFAMP0OtERQ&amp;sig2=3ChFSPGwukN_WC4IlhitGA

Edit: A meta analytic model is a model to explain why studies obtained the results that they did. Different samples, methodologies, and so forth will produce different results, and a metamodel expresses this formally.

A dynamic lag model (also called a distributed lag model) is a model to predict the current value of a dependent variable based on past values of independent variables. For example, if a change in price causes a change in demand, you can model this by correlating price at time t-1 to demand at time t.