Question

Can someone please help solve this, its econ with stats

Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with the dependent variable. In this case, O the coefficients on the included variables will be unbiased if the included variables are not correlated with the omitted variable. O the coefficients on the included variables will always be biased. Othere is no effect on the coefficients of the included variables since the omitted variable has been omitted. O the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. Question 15 3 pts Suppose that you estimate a multiple regression model using OLS using a sample of 60 observations. The skewness of the residuals is 0.5 and the excess kurtosis is 1. As a result, the value of the Jarque- Bera test is __and we_reject the null hypothesis of normally distributed disturbances at the 5-percent level of significance. O 10; cannot O 5; cannot 010; can O 5; cannot

Answer 1

Answer (14):

When you omit a expalnatory variable which is correlated with the dependnet variable, you commit a problem of ommision of relevant variables. Suppose the true model is:

Y = B₁ + B₂X₂ + B₃X₃ + u

But you estimate the below model instead:

Y = A₁ + A₂X₂ + u

Here you ommited X₃, thus omitting a relevant variable.

The consequences of this is that the estimators will be biased i.e.

E(a₁) = A₁ ≠ B₁ and E(a₂) = A₂ ≠ B₂

Thus, the correct answer is the coefficients of the included variables will always be biased.

Answer (15):

The hypothesis for jarque bera test is:

H₀: The residuals are normal

H₁: The residuals are not normal

The test statistic for Jarque Bera test is:

JB = (n/6) * [S² + (K-3)²/4 ]

where, n = sample size = 60

S = skewness = 0.5

K-3 = excess kurtosis = 1

So, JB = (60/6) * [0.5² + (1)²/4] = 10 * [0.25 + 0.25] = 5

This distribution follows chi-square distribution with 2 degrees of freedom.

At 5% level of significance, chi-square = 5.99

As JB(=5) < 5.99, we cannot Reject H₀ at % level of significance.

Thus, the answers are 5 and cannot.