- Consider The Following Simple Regression Model Y B1 B2x Ei Where E Ei X 0 Var Ei X O2 And Cov Ei Ej 1 (194.8 KiB) Viewed 88 times
Consider the following simple regression model: = y; = B1 + B2x, + ei where E(ei | x) = 0, var(ei | x) o2 and cov(ei, ej | x) = ( for all i + j. Suppose application of least squares rule to this equation with number of observations N = 35 yields bj = 10, b2 = 2, and that we obtain SSE = 12, R2 = 0.7 and Page 2 of 3 cov(bi, be ! x) = { 4 -0.1 -0.1 0.25 (3) Are the following statements true or false? Clearly indicate if each statement is true or false and support your choice with a brief explanation. You will get zero for that question if you only state true or false and do not give a brief explanation. Part 1 for ETF2100 and ETF5910 (10 Marks) (a) bị and b2 are called least squares estimates because they minimize the squares (bi – B12 and (62 – B2). (2 Marks) (b) The coefficient estimate b2 is significantly different from zero at 5% significance level. (2 Marks) (c) The total sum of squares (SST) is 125 (2 Marks) (d) The error terms are heteroskedastic because the two variances on the diagonal of cov(b1,b2 | x) are not equal to each other. (2 Marks) (e) If economic theory suggests that x has a positive relationship with y, it makes sense to use a right-tail test when testing significance of B2. (2 Marks)