The extended least squares assumptions are 1. E(u; X;) = 0 (u, has conditional mean zero); 2. (X;, Y;), i = 1, . .. , n, are independently and identically distributed (i.i.d.) draws from their joint distribution; 3. X; and u; have nonzero finite fourth moments; 4. X has full column rank (there is no perfect multicollinearity); 5. var(u;|X;) = oi (homoskedasticity); and 6. The conditional distribution of u; given X, is normal (normal errors). dependent variable and two regressors Sample Covariances Sample Means Y X, х, 6.39 0.26 0.22 0.32 7.24 0.80 0.28 х, 4.00 2.40

MATLAB: An Introduction with Applications
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Chapter1: Starting With Matlab
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Suppose that a sample of n = 20 households has the sample means and
sample covariances Attached for a dependent variable and two regressors:
a. Calculate the OLS estimates of β0, β1, and β2. Calculate s2u. Calculate
the R2 of the regression.
b. Suppose that all six assumptions in Key Concept 18.1 hold. Test the
hypothesis that β1 = 0 at the 5% significance level.

The extended least squares assumptions are
1. E(u; X;) = 0 (u, has conditional mean zero);
2. (X;, Y;), i = 1, . .. , n, are independently and identically distributed (i.i.d.)
draws from their joint distribution;
3. X; and u; have nonzero finite fourth moments;
4. X has full column rank (there is no perfect multicollinearity);
5. var(u;|X;) = oi (homoskedasticity); and
6. The conditional distribution of u; given X, is normal (normal errors).
Transcribed Image Text:The extended least squares assumptions are 1. E(u; X;) = 0 (u, has conditional mean zero); 2. (X;, Y;), i = 1, . .. , n, are independently and identically distributed (i.i.d.) draws from their joint distribution; 3. X; and u; have nonzero finite fourth moments; 4. X has full column rank (there is no perfect multicollinearity); 5. var(u;|X;) = oi (homoskedasticity); and 6. The conditional distribution of u; given X, is normal (normal errors).
dependent variable and
two regressors
Sample Covariances
Sample Means
Y
X,
х,
6.39
0.26
0.22
0.32
7.24
0.80
0.28
х,
4.00
2.40
Transcribed Image Text:dependent variable and two regressors Sample Covariances Sample Means Y X, х, 6.39 0.26 0.22 0.32 7.24 0.80 0.28 х, 4.00 2.40
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