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Question 15 3 pts Suppose that you estimate a multiple regression model using OLS using a...
Question 15 3 pts Suppose that you estimate a multiple regression model using OLS using a sample of 120 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. 5; cannot O 10; cannot 10; can 5; cannot
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. 10; cannot 10; can 5; cannot 5; cannot
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, the coefficients on the included variables will always be biased. the coefficients on the included variables will always be unbiased, but the standard errors and test statistics will be biased. there is no effect on the coefficients of the included variables since the omitted variable has been omitted. the coefficients...
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...
Question 13 3 pts Consider three data series, each a random sample of seven observations (n = 7): Series 1: {1, 1, 1, 3, 5, 5, 5} Series 2: {1, 1, 3, 3, 3, 5, 5} Series 3: {1, 3, 3, 3, 3, 3, 5} The interquartile range of Series 3 is: 4 0 3 2 Question 14 3 pts Suppose that you estimate a multiple regression model, but that you inadvertently omit an explanatory variable that is correlated with...
Imagine that you regressed the earnings of individuals on a constant, a binary variable (“Male”) which takes on the value of 1 for males and is 0 otherwise, and another binary variable (“Female”) which takes on the value of 1 for female and is 0 otherwise. Because females typically earn less than males, you would expect: Group of answer choices autocorrelation or serial correlation to be a serious problem. the estimated coefficient for Male to have a positive sign, and...
12) 12) We run a regression with OLS and wish to check that the standardized residuals follow a normal distribution. There are 801 standardized residuals; the kurtosis-0.605; and the skewness is 0.188. Test for normality using a Jarque-Bera test at a level of significance of 5%; A) The critical value is 1.96, and we conclude that the distribution of the residuals is not normal. B) The critical value is 5.99, and we conclude that the distribution of the residuals is...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: O multicollinearity. omitted variable bias. O serial correlation. spurious regression. 3 pts Question 9
Question 12 3 pts Consider the estimated multiple regression model using OLS, with the standard errors in parentheses below each estimated coefficient. There are 1,576 observations in the sample: Ỹ = 10 + 2x - 5X36 (3) (1.5) (2) Suppose the null hypothesis is that the true coefficient (population parameter) for X3 is equal to 1. The test statistic associated with this null hypothesis is: -3 0-2 O 2
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: omitted variable bias. o serial correlation. spurious regression. o multicollinearity.