Econometrics Assignment
ECON2206 Assignment 1
[pic]
Question A
[pic] in the equation above describes the relationship between price of the house and the size of the lot. The coefficient of “log(lotsize)” is the estimated elasticity of “price” with respect to “lotsize”. From this number, we learn that in this model, an additional square feet increase to the lotsize would increase the house price on average by[pic] %, assuming that the other independent variable in this model are held constant.
Question B
I would expect [pic] to be greater than 0, this is because under normal circumstances the increase in the size of the land on which the house has been constructed would incur more costs on acquiring the land in the first place, thus the house price would also increase to cover this increase. If [pic] is less than 0, it would imply that houses built on small lot of land would cost more than house built on large lot of land, this would not occur in the normal market unless in extreme outlying circumstances.
Question C
Zero Conditional Mean Assumption is “a key assumption used in multiple regression analysis that states that, given any values of the explanatory variables, the expected value of the error equals zero” (Wooldridge, … ) . Within the context of the equation listed above, the error term[pic]has no relation with any of the explanatory variables “log(lotsize)” and “log(sqrft), in other words [pic]is mean independent of “log(lotsize)” and “log(sqrft). This also indicates that the [pic] accounts for the variation between the predicted value of a house price and the actual value of a house price.
Question D
Part i. • “price” measures house prices in values of $1000s • “lotsize” is the size of the lot in square feet (size of the land) • “sqrft” is the size of the house in square feet • “bdrms” is the number of bedrooms in the house
Part ii.
|Variables |Sample Mean
[pic]
Question A
[pic] in the equation above describes the relationship between price of the house and the size of the lot. The coefficient of “log(lotsize)” is the estimated elasticity of “price” with respect to “lotsize”. From this number, we learn that in this model, an additional square feet increase to the lotsize would increase the house price on average by[pic] %, assuming that the other independent variable in this model are held constant.
Question B
I would expect [pic] to be greater than 0, this is because under normal circumstances the increase in the size of the land on which the house has been constructed would incur more costs on acquiring the land in the first place, thus the house price would also increase to cover this increase. If [pic] is less than 0, it would imply that houses built on small lot of land would cost more than house built on large lot of land, this would not occur in the normal market unless in extreme outlying circumstances.
Question C
Zero Conditional Mean Assumption is “a key assumption used in multiple regression analysis that states that, given any values of the explanatory variables, the expected value of the error equals zero” (Wooldridge, … ) . Within the context of the equation listed above, the error term[pic]has no relation with any of the explanatory variables “log(lotsize)” and “log(sqrft), in other words [pic]is mean independent of “log(lotsize)” and “log(sqrft). This also indicates that the [pic] accounts for the variation between the predicted value of a house price and the actual value of a house price.
Question D
Part i. • “price” measures house prices in values of $1000s • “lotsize” is the size of the lot in square feet (size of the land) • “sqrft” is the size of the house in square feet • “bdrms” is the number of bedrooms in the house
Part ii.
|Variables |Sample Mean