To what extent do you agree with the following statements?
a) the best test of the performance of two different regression equations is their respective values of R^2
b) Time-series regressions should be run using as many years of data as possible; more data means more reliable coefficient estimates
c) Including additional variables (even if they lack individual significance) does no harm and might raise R
d) Equations that perform well in explaining past data are likely to generate accurate forecasts looking forward
a. True. R Squared is the best test of the performance of different equations as it shows the percentage of deviation on the values of the dependent variable that are explained by the independent variable.
b. True. The greater the sample size of the estimated data the greater is the accuracy of the data.
c. False. Including irrelevant variables as independent variable in the data set will reduce the value of R squared.
d. False. The statement is not always true. If there is change in expectations of the consumer related to some variable then the equations might not generate accurate forecasts looking forward.
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