Suppose MLR.1 through MLR.5 are satisfied in the model wageie = Bo + B, educje + Uije) where the index i stands for fir...
Exercise 4.11 Consider the regression model Y Po PX+u Suppose that you know Bo 1. Derive the formula for the least squares estimator of p The least squares objective function is OA. n (v2-bo-bx?) i-1 Ов. O B. n (M-bo-bX) /# 1 n Click to select your answer and then click Check Answer. Exercise 4.11 OA n Σ (--B,χ?) O B. E (Y-bo-b,X)2 j= 1 n Σ (Υ-Βo-bΧ) 3. j= 1 D. n Σ (Υ-0-b,) i- 1 Click to select...
2. Consider the simple linear regression model: where e1, .. . , es, are i.i.d. N (0, o2), for i= 1,2,... , n. Suppose that we would like to estimate the mean response at x = x*, that is we want to estimate lyx=* = Bo + B1 x*. The least squares estimator for /uyx* is = bo bi x*, where bo, b1 are the least squares estimators for Bo, Bi. ayx= (a) Show that the least squares estimator for...
Exercise 11.3 Consider the following duopoly model. There are two firms sup- plying a market where demand is given by p(Q)- a-bQ. Firm i produces qi units of output and so the total level of production is q1q2. Both firms face the same constant marginal cost, so the cost of producing qi for firm i įs cqỉ. Thus the profit functions of firms 1 and 2 respectively, are given by: (a) Suppose that each firm takes the output of the...
3. Consider the multiple linear regression model iid where Xi, . . . ,Xp-1 ,i are observed covariate values for observation i, and Ei ~N(0,ơ2) (a) What is the interpretation of B1 in this model? (b) Write the matrix form of the model. Label the response vector, design matrix, coefficient vector, and error vector, and specify the dimensions and elements for each. (c) Write the likelihood, log-likelihood, and in matrix form. aB (d) Solve : 0 for β, the MLE...
5. (20 pts) Suppose that we have a dataset {(yi, x, Tt2, X;3), i,1,... ,n} together with some general belief on the data that higher (lower) value of each covariate x; (j = 1,2,3) will tend to result in higher (lower) y. In this study, we are interested in predicting y; from the total set of the regressors x;i, X;2, xt3. So, we apply the multiple linear regression yi = Bo+B1x1 +B2x52 + B3x43 + t to the data and...
5. For ridge regression, we choose parameter estimators b which minimise i-1 j-0 where λ is a constant penalty parameter. (a) Show that these estimators are given by (b) Calculate the ridge regression estimates for the data from Q2 with penalty parameter 0.5. In order to avoid penalising some parameters unfairly, we must first scale every predictor variable so that it is standardised (mean 0, variance 1), and centre the response variable (mean 0), in which case an intercept parameter...
Problem 5.10.10 Suppose you have n suitcases and suitcase i holds Xi dollars where X1, X2, …, Xn are iid continuous uniform (0, m) random variables. (Think of a number like one million for the symbol m.) Unfortunately, you don’t know Xi until you open suitcase i. Suppose you can open the suitcases one by one, starting with suitcase n and going down to suitcase 1. After opening suitcase i, you can either accept or reject Xi dollars. If...
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3. The scrap rate for a manufacturing firm is the number of defective items-products that must be discarded -out of every 100 produced. Thus, for a given number of items produced, a decrease in the scrap rate reflects higher worker productivity. Suppose we are interested in one hand about the efficiency of worker training on productivity and on the other hand whether firm size, mea- sured in terms of the number...
Problem 5.10.10 Suppose you have n suitcases and suitcase i holds Xi dollars where X1, X2, …, Xn are iid continuous uniform (0, m) random variables. (Think of a number like one million for the symbol m.) Unfortunately, you don’t know Xi until you open suitcase i. Suppose you can open the suitcases one by one, starting with suitcase n and going down to suitcase 1. After opening suitcase i, you can either accept or reject Xi dollars. If you...
Question 1 (50 pts): Suppose that a client of yours measure the heights (in inches) of n - 30 wheats grown at locations of various elevations (measured as meters above sea levels). Af- ter some discussion, you decided to fit a linear regression of wheat heights (denoted as yi) on the elevations of the locations (denoted as zi) as follows where ei, E2, . . . , En are i.i.d. errors with Elei] 0 and var(G) σ2. You calculated some...