Your textbook gives the following example of simultaneous causality bias of a two equation system:
Yi = β0 + β1Xi + ui
Xi = + Yi + vi
In microeconomics,you studied the demand and supply of goods in a single market.Let the demand ( )and supply ( )for the i-th good be determined as follows, = β0 - β1Pi + ui, = - Pi + vi,
where P is the price of the good.In addition,you typically assume that the market clears.
Explain how the simultaneous causality bias applies in this situation.The textbook explained a positive correlation between Xi and ui for 0 through an argument that started from "imagine that ui is negative." Repeat this exercise here.

Question

Your textbook gives the following example of simultaneous causality bias of a two equation system:
Yi = β0 + β1Xi + ui
Xi =  +  Yi + vi
In microeconomics,you studied the demand and supply of goods in a single market.Let the demand (  )and supply (  )for the i-th good be determined as follows,  = β0 - β1Pi + ui,  =  -  Pi + vi,
where P is the price of the good.In addition,you typically assume that the market clears.
Explain how the simultaneous causality bias applies in this situation.The textbook explained a positive correlation between Xi and ui for  > 0 through an argument that started from "imagine that ui is negative." Repeat this exercise here.

Quizplus · Accepted Answer

The Answer of Your textbook gives the following example of simultaneous causality bias of a two equation system:
Yi = β0 + β1Xi + ui
Xi =  +  Yi + vi
In microeconomics,you studied the demand and supply of goods in a single market.Let the demand (  )and supply (  )for the i-th good be determined as follows,  = β0 - β1Pi + ui,  =  -  Pi + vi,
where P is the price of the good.In addition,you typically assume that the market clears.
Explain how the simultaneous causality bias applies in this situation.The textbook explained a positive correlation between Xi and ui for  > 0 through an argument that started from "imagine that ui is negative." Repeat this exercise here.

Your Textbook Gives the Following Example of Simultaneous Causality Bias