Econometrics 13) Consider the classical linear regression model y = XB + E, EN(0,021) The data...
a. Consider the multiple regression model y = XB + €, with E(e) = 0 and var(e linear function c'3 of B. Show that the change in the estimate d'3 when the ith observation is deleted is d'B-d'B 021. Consider a = d'Ce re C = (X'X)-1x{. ii a. Consider the multiple regression model y = XB + €, with E(e) = 0 and var(e linear function c'3 of B. Show that the change in the estimate d'3 when the...
2. Consider the following model: y = XB + u where y is a (nx1) vector containing observations on the dependent variable, B = Bi , B X is a (n x 3) matrix. The first column of X is a column of ones whilst the second and third columns contain observations on two explanatory variables (x and x2 respectively). u is (n x 1) vector of error terms. The following are obtained: 1234.7181 1682.376 7345.581 192.0 259.6 1153.1) X'X...
Consider the simple linear regression model y - e, where the errors €1, ,en are iid. random variables with Eki-0, var(G)-σ2, i-1, .. . ,n. Solve either one of the questions below. 1. Let Bi be the least squares estimator for B. Show that B is the best linear unbiased estimator for B1. (Note: you can read the proof in wikipedia, but you cannot use the matrix notation in this proof.) 2. Consider a new loss function Lx(A,%) 71 where...
Econometrics Question: Consider the data generating process Y= β1+ β2Xi+β3Zi+β4Wi+β5Pi+β6Ti+e e~N(0, σ^2) and the null hypothesis Ho: β4=5 and β2+β3=0 and 2β5-4β6=0. Discuss how you would test the null hypothesis Ho: β2/β3=4 against Ha: β2/β3≠4.
Q4.. [40 points] Consider the multiple linear regression model given by y - XB -+ s, where y and e are vectors of size 8 × 1, X ls a matrix of size 8 x 3 and Disa vector of sze 3 × 1. Also, the following information are available e = 22 y -2 and XTy 3 1. [10 points) Estimate the regression coefficients in the model given above? 2. [4 points] Estimate the variance of the error term...
Due: Jan 22 ECN 702 Econometrics II 1. Given data on (x) for i,n, consider the following least square problem for a simple linear regression. bobi We assume the four linear regression model assumptions dicussed in class hold. 6) Compute the partial derivatives of the objective function. (ii) Put the derived partial derivatives in (i) equal to zeros. Explain why the resulting equa- tions are called ‘normal equation. (Hint: two n-dimesional vectors (voi-i and (wi)-1 are normal(-orthogonal) if Σ(-1 uiv.-0.)...
1. Consider the simple linear regression model: Ү, — Во + B а; + Ei, where 1, . . , En are i.i.d. N(0,02), for i1,2,... ,n. Let b1 = s^y/8r and bo = Y - b1 t be the least squared estimators of B1 and Bo, respectively. We showed in class, that N(B; 02/) Y~N(BoB1 T;o2/n) and bi ~ are uncorrelated, i.e. o{Y;b} We also showed in class that bi and Y 0. = (a) Show that bo is...
Consider the multiple regression with three independent variables under the classical linear model assumptions: y Bo+BBx,+B,x, +u 1. You would like to test the hypothesis: H0: B-3B, 1 What is the standard error of B-3B,? (i Write the t-statistic of B-3B ( Define 0,= B-3B.. Write a regression equation that allows you to directly obtain 0, and its standard error.
1. In order to test whether the multiple linear regression model y bo +b,x1 + b2X2 is better than the average model (lazy model), which of the following null hypotheses is correct: a. Ho' b1 = b2 = 0 Но: B1 B2-0 с. We have a dataset Company with three variables: Sales, employees and stores. To build a multiple linear regression model using Sales as dependent variable, number of stores and number of employees as independent variables, which of the...
Consider the multiple linear regression (MLR) model that satisfies the classical assumptions: Yi = Bo + B1Xil +...+Bkxik + Ui estimated by OLS/MOM. Let the estimators beßo, Ŝ1,..., ØK. Question 1 (1 point) The p-value for undertaking a hypothesis test is the smallest significance level for which we reject a null hypothesis that is correct. True False Question 2 (1 point) To test Ho: B3 = 34 vs H1 : B3 – B4 > 0, we form the test statistic...