A classic application of instrumental variables regression is estimating the elasticity of demand for a product. In our case, the product of interest is cigaretts. In economics, the elasticity of demand is the ratio of the percentage change in quantity demanded to the percentage change in price of a commodity. To express percentage change, we transform the variables using natural logs, so the relationship can be written as follows: lnQ = α + βlnP + ε, where β is the estimate of the elasticity (percentage change in quantity for a 1% change in price). We have observations on price and quantity of cigaretts, and it seems like we could run an OLS regression of lnQ on lnP and obtain an estimate of the elasticity.However, there is a problem. Quantity demanded, apparently depends on price, but price is also determined by market demand. When customers have a high demand, the price tends to go higher. Therefore, because of the causaility going both ways, the elasticity of demand cannot be estimated by an OLS regression of log quantity on log price.

Question

Which of the following best describes the problem as mentioned above?
1.Obmitted variabe bias
2.selection bias
3.endogeneity

Answer 1

Endogeneity is the correct answer. endogeneity refers to the mutual relationship between the variables.