DSE 2012 Question 32: Goodness of fit and model selection
An analyst trying to estimate the demand for rice has estimated the following two models. Model 1: D=50+0.3Y+0.1P+12N;R2=0.7, where D is the demand for rice per household, Y is income per household, P is the price of rice and N is household size. The standard errors associated with the coefficient Y is 0.1, that associated with P is 0.05, and of N is 3. Model 2: D/N=50+0.2Y/N−0.5P;R2=0.9, where the standard errors of the estimated slope coefficients are 0.1 and 0.2 respectively. Which of the following statements is true?
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An analyst trying to estimate the demand for rice has estimated the following two models. Model 1: D=50+0.3Y+0.1P+12N; R²=0.7, where D is the demand for rice per household, Y is income per household, P is the price of rice and N is household size. The standard errors associated with the coefficient Y is 0.1, that associated with P is 0.05, and of N is 3. Model 2: D/N=50+0.2Y/N-0.5P; R²=0.9, where the standard errors of the estimated slope coefficients are 0.1 and 0.2 respectively. Which of the following statements is true? (A) Model 2 is preferred to Model 1 because it has a higher R². (B) Model 1 is preferred to Model 2 because the coefficients are all significant (C) The two models are not comparable in terms of fit (D) None of the above
Also written as: An analyst trying to estimate the demand for rice has estimated the following two models. Model 1: D=50+0.3Y+0.1P+12N; R^2=0.7, where D is the demand for rice per household, Y is income per household, P is the price of rice and N is household size. The standard errors associated with the coefficient Y is 0.1, that associated with P is 0.05, and of N is 3. Model 2: D/N=50+0.2Y/N-0.5P; R^2=0.9, where the standard errors of the estimated slope coefficients are 0.1 and 0.2 respectively. Which of the following statements is true?
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What this question tests
This question is on Adjusted R-squared and model comparison, part of Goodness of fit and model selection in Econometrics.
Penalising extra regressors and when R-squared can be compared across models.
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