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4. Question 4: Consider the following equation for U.S. per capita consumption of beef: CB--330.3+ 49.1InY, 0.34 PB,+0.33 PRP 15.4 D (se 7.4 t-6.6) (se-0.13 -2.6) (se-0.12 t-2.7) (se-4.1 -3.7) RP = 0.7 N 28 DW = 0.94 where CB. the annual per capita pounds of beef consumed in the U.S. in year t, In Yǐ : the log of per capita disposable real income in the U.S. in year t, PBt Ξ , PRI average annualized real wholesale price of pork in year t (in cents per pound) average annualized real wholesale price of beef in year t (in cents per pound) a dummy variable equal to 1 for vears in which there was a health scare about the dangers of red meat, 0 otherwise. (a) Develop and test your own hypotheses with respect to the individual estimated slope (b) Calculate the elasticities of CB with respect to Y and PB respectively. Explain the (c) What econometric problems appear to be present in this equation? Support your answer coefficients. obtained numbers.

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Answer #1

Solution :-

Given data :

Consider the following equation for U.S. per capita consumption of beef:

N = 28

DW = 0.94

(a) Develop and test your own hypotheses with respect to the individual estimated slope coefficients.

Here consider the critical t-value. i.e,

Here,

and df = N - p ( p= number of regression coefficient ( including constant ) = 5 )

df = 28 - 5

df = 23

Then,

  

  

Here the critical t-value. i.e,   is less than the absolute value( neglect -ve symbol ) of the t of each predictor, so each of the predictor , , and   is significant in the given multiple linear regression.

(c) What econometric problems appear to be present in this equation? Support your answer.

Since the and , have negative sign so it has negative effect on the ward or reaction variable .

Also, and have positive sign so it has positive effect on the ward or reaction variable .

Note :-

As HOMEWORKLIB RULES rule, i have little bit doubt about the 'bit-b'. so i didn't answered the 'bit-b'. If you want that bit also please upload that bit as another question. please don't give thumbs down.

Thank you.

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