economics simple linear regression equation

Technically, of course, the percentage change in demand from a price increase is a decline in demand thus price elasticity is a negative number. This comes about because there are few if any real substitutes for gasoline perhaps public transportation, a bicycle or walking. This is called an inelastic demand meaning a small response to the price change. A 16 percent increase in price has generated only a 4 percent decrease in demand: 16% price change → 4% quantity change or. The price elasticity is the percentage change in quantity resulting from some percentage change in price. As an example, if the price of gasoline increased say 50 cents from an initial price of $3.00 and generated a decline in monthly consumption for a consumer from 50 gallons to 48 gallons we calculate the elasticity to be 0.25. The value of measuring in percentage terms is that the units of measurement do not play a role in the value of the measurement and thus allows direct comparison between elasticities.

Elasticity is measured as a percentage change/response in both engineering applications and in economics. In economics, the force in question is some market force such as a change in price or income. It is from here that we get the term an “elastic” band. The concept of elasticity is borrowed from engineering and physics where it is used to measure a material’s responsiveness to a force, typically a physical force such as a stretching/pulling force. Here we wish to explore the concept of elasticity and how we can use a regression analysis to estimate the various elasticities in which economists have an interest. As we saw in the case of dummy variables, this can show up as a parallel shift in the estimated line or even a change in the slope of the line through an interactive variable. From the calculus, the slope of the line is the first derivative and tells us the magnitude of the impact of a one unit change in the X X variable upon the value of the Y Y variable measured in the units of the Y Y variable. As we have seen, the coefficient of an equation estimated using OLS regression analysis provides an estimate of the slope of a straight line that is assumed be the relationship between the dependent variable and at least one independent variable.

Economics simple linear regression equation