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1)(a)Write down the definition of heteroscedasticity. What problem doesthe presence of it cause for the OLS estimator?[5 marks] (b)Explain whatismulticollinearity. What problem does thepresenceof it...

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1)(a)Write down the definition of heteroscedasticity. What problem doesthe presence of it cause for the OLS estimator?[5 marks]

(b)Explain whatismulticollinearity. What problem does thepresenceof it cause for the OLS estimator?[5 marks]

2)(a)Explain the difference between cross-sectional data and time series data.

Give examples of each.[5 marks]

(b)Explain the difference between linear in the variables and linear in theparameters. Give examples of each.[5 marks]

3)(a) Give four reasons why we introduce a stochastic disturbance term.[4 marks]

(b)Which four assumptions does the classical linear regression model makeabout the random error term u? Explain.[4 marks]

(c)Which other six assumptions does the classical linear regression modelmake?[12 marks]

4)The following regression output represents food expenditure in Morocco:

FoodExpi = XXXXXXXXXX4368TotalExpi

Se = XXXXXXXXXX)

t = XXXXXXXXXX)

p = XXXXXXXXXX)*

r2 = XXXXXXXXXXdf = 53

F1.53 = XXXXXXXXXXp value = 0.0000)*

Where * denotes extremely small.

(a)How do you interpret this regression?(2 marks)(b)Test the null hypothesis that there is no relationship between food expenditure and total expenditure. Which test do you use and why?(8 marks)
Answered Same Day Dec 26, 2021

Solution

David answered on Dec 26 2021
127 Votes
1. (a) Though the word heteroscedasticity is very difficult pronounce but very easy understand
the meaning. Hetrocedacity refers to a situation in which variation of variables in question in
unequal across the range of values of a second variable that predicts it.When heteroscedasticity is
present, the problems with larger distu
ances have more “pull” than other data. When
heteroscedasticity is present then weighted least squares regression would be more appropriate
ecause weighted least squares down-weights those observations with larger variations.
(b). Multicollinearity also known as colinearity is a statistical concept. Multicollinearity is a...
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