You have a sample of size n = 1 with data y₁ = 2 and ₁= 1. You are interested in the value of 3 in the regression Y = XB+ u. (Note there is no intercept.) (a) Plot the sum of squared residuals (y₁ - bx₁)² as function of b. Use the range x₁ € [−2,5] for your plot, and combine all plots of this exercise in one figure. (Use any software you prefer. Excel is one option.) BOLS = 2. (b) Show that the least squares estimate of 3 is 3 (c) Using ALasso = 1, plot the Lasso penalty term XLasso [b] as a function of b.

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Problem 3. [SW 14.10]
You have a sample of size n = 1 with data y₁ = 2 and ₁= 1. You are interested in the value
of 3 in the regression Y = XB+ u. (Note there is no intercept.)
(a) Plot the sum of squared residuals (y₁ – br₁)² as function of b. Use the range x₁ € [−2,5] for
your plot, and combine all plots of this exercise in one figure. (Use any software you prefer.
Excel is one option.)
(b) Show that the least squares estimate of ß is BOLS = 2.
(c) Using ›Lasso = 1, plot the Lasso penalty term XLasso b as a function of b.
(d) Using Lasso = 1, plot the Lasso penalized sum of squared residuals (y₁ - bx₁)² + XLasso |b|.
(e) Find the value of
Lasso
Las
Lasso
(f) Using Lasso = 0.5, repeat (c) and (d). Find the value of B
(g) Using Lasso = 5, repeat (c) and (d). Find the value of Lasso
(h) Use the graphs that you produced in (a)-(d) for the various values of ALasso to explain why a
larger value of Lasso results in more shrinkage of the OLS estimate.
Transcribed Image Text:Problem 3. [SW 14.10] You have a sample of size n = 1 with data y₁ = 2 and ₁= 1. You are interested in the value of 3 in the regression Y = XB+ u. (Note there is no intercept.) (a) Plot the sum of squared residuals (y₁ – br₁)² as function of b. Use the range x₁ € [−2,5] for your plot, and combine all plots of this exercise in one figure. (Use any software you prefer. Excel is one option.) (b) Show that the least squares estimate of ß is BOLS = 2. (c) Using ›Lasso = 1, plot the Lasso penalty term XLasso b as a function of b. (d) Using Lasso = 1, plot the Lasso penalized sum of squared residuals (y₁ - bx₁)² + XLasso |b|. (e) Find the value of Lasso Las Lasso (f) Using Lasso = 0.5, repeat (c) and (d). Find the value of B (g) Using Lasso = 5, repeat (c) and (d). Find the value of Lasso (h) Use the graphs that you produced in (a)-(d) for the various values of ALasso to explain why a larger value of Lasso results in more shrinkage of the OLS estimate.
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