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book Introductory Econometrics 4th Edition by Jeffrey Wooldridge cover

Introductory Econometrics 4th Edition by Jeffrey Wooldridge

Edition 4ISBN: 978-0324660609
book Introductory Econometrics 4th Edition by Jeffrey Wooldridge cover

Introductory Econometrics 4th Edition by Jeffrey Wooldridge

Edition 4ISBN: 978-0324660609
Exercise 3
Let Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. denote the OLS estimate from a regression of y on Z.
(i) Show that Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. =A-1 Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. .
(ii) L et Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. = Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare
(iii) Show that the estimated variance matrix for Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. is Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. A1(X X)1A1 , where Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. is the usual variance estimate from regressing y on X.
(iv) L et the Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. and the Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical.
(v) Assuming the setup of part (iv), use part (iii) to show that se( Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. ) = se( Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. )/aj.
(vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. and Let   be the OLS estimate from the regression of y on X. Let A be a (k + 1) × (k + 1) nonsingular matrix and define zt = xtA, t = 1,….,n. Therefore, zt is 1 × (k + 1) and is a nonsingular linear combination of xt. Let Z be the n × (k + 1) matrix with rows zt. Let   denote the OLS estimate from a regression of y on Z. (i) Show that   =A-1   . (ii) L et   be the fitted values from the original regression and let. y t be the fitted values from regressing y on Z. Show that   =   , for all t =- 1, 2,…., n. How do the residuals from the two regressions compare  (iii) Show that the estimated variance matrix for   is   A1(X X)1A1 , where   is the usual variance estimate from regressing y on X. (iv) L et the   be the OLS estimates from regressing yt on 1, xt1,...,xtk, and let the   be the OLS estimates from the regression of yt on 1, a1xt1,…,ak xtk, where aj 0, j =1,…,k. Use the results from part (i) to find the relationship between the   and the    (v) Assuming the setup of part (iv), use part (iii) to show that se(   ) = se(   )/aj. (vi) Assuming the setup of part (iv), show that the absolute values of the t statistics for   and   are identical. are identical.
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Introductory Econometrics 4th Edition by Jeffrey Wooldridge
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