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15. What are the properties of multiple regression model OLS estimators? If we have many different...
2. Suppose we have the simple regression model Y =a+8X:+E, and their OLS coefficient estimators a...
2. Suppose we have the simple regression model Y =a+8X:+E, and their OLS coefficient estimators a and b. Answer the following questions. (a) Suppose we multiply X, by 1/2 for all i and do the OLS estimation again using X as the regressor (the independent variable). What will be your new estimators, denoted by ă (intercept) and b (slope)? Compare them with the original OLS estimators a and b, respectively (b) Compare Var[b] and Var[b]. Are they the same or...
(a) What is meant by heteroscedasticity? What are the effects of heteroscedasticity on: (i) The OLS estimators? In parti...
(a) What is meant by heteroscedasticity? What are the effects of heteroscedasticity on: (i) The OLS estimators? In particular, does heteroscedasticity create bias in the OLS estimators? (ii) The variances and standard errors of the OLS estimators. (iii) The validity of t-test and F-test of overall significance of the regression? (b) Given: Yi = β1 + β2 Xi + ui Var(ui) = σ2 Xi Show how this model can be transformed so that the disturbances have constant variance. Explain how...
1.) What is the difference between a simple regression model and a multiple regression model? a.)...
1.) What is the difference between a simple regression model and a multiple regression model? a.) There isn’t one. The two terms are equivalent b.) A simple regression model has a single predictor whereas a multiple regression model has potentially many c.) A simple regression model can handle only limited amounts of data whereas a multiple regression model can handle large data sets d.) A simple regression is appropriate for a dichotomous outcome variable, whereas a multiple regression model should...
2. In a multiple regression model, the OLS estimator is consistent if a. there is no...
2. In a multiple regression model, the OLS estimator is consistent if a. there is no correlation between the dependent variables and the error term b. there is a perfect correlation between the dependent variables and the error term c. the sample size is less than the number of parameters in the model d. there is no correlation between the independent variables and the error term
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
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. O 5; cannot O 10; can 5; cannot 10: cannot Question 16 3 pts
(e) Suppose that we reject the null hypothesis, what does that imply about OLS estimatron of the ...
(e) Suppose that we reject the null hypothesis, what does that imply about OLS estimatron of the regression equation of ve? (Hint: does this problem affect unbiasedness or c ciency of OLS estimators?) (d) (10 pts bonus) Solve the problem by completely specifying the regression model. 630 pts) Suppose & is the residual of the following regression (a) If we are also running the regression what OLS assumption of time series data we suspect is violated (what time series prob-...
What is multicollinearity and how does it affect the standard errors of OLS estimators? (b) In the context of perfect mu...
What is multicollinearity and how does it affect the standard errors of OLS estimators? (b) In the context of perfect multicollinearity between explanatory variables, explain why the OLS estimators cannot be derived. (c) With what methods can one detect multicollinearity? (d) Given relatively high variance of individual explanatory variables, explain why relatively low t-statistics but a relatively high F-statistic for the regression is an indication of multicollinearity.
Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very...
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: multicollinearity. spurious regression. omitted variable bias. serial correlation.
Suppose that you estimate a multiple regression model using OLS using a sample of 60 observations....
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
2. Suppose we have the simple regression model Y =a+8X:+E, and their OLS coefficient estimators a and b. Answer the following questions. (a) Suppose we multiply X, by 1/2 for all i and do the OLS estimation again using X as the regressor (the independent variable). What will be your new estimators, denoted by ă (intercept) and b (slope)? Compare them with the original OLS estimators a and b, respectively (b) Compare Var[b] and Var[b]. Are they the same or...
2. In a multiple regression model, the OLS estimator is consistent if a. there is no correlation between the dependent variables and the error term b. there is a perfect correlation between the dependent variables and the error term c. the sample size is less than the number of parameters in the model d. there is no correlation between the independent variables and the error term
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
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. O 5; cannot O 10; can 5; cannot 10: cannot Question 16 3 pts
(e) Suppose that we reject the null hypothesis, what does that imply about OLS estimatron of the regression equation of ve? (Hint: does this problem affect unbiasedness or c ciency of OLS estimators?) (d) (10 pts bonus) Solve the problem by completely specifying the regression model. 630 pts) Suppose & is the residual of the following regression (a) If we are also running the regression what OLS assumption of time series data we suspect is violated (what time series prob-...
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