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Question 1 Consider the following model Yi = Bx; +ui (a) Derive the OLS estimator of...
Question 1 Consider the following model Yi = B.z; + u (a) Derive the OLS estimator...
Question 1 Consider the following model Yi = B.z; + u (a) Derive the OLS estimator of B, B. (6 marks] (b) Show that is unbiased. [9 marks] (c) Find the variance of B. [7 marks]
Derive the OLS estimator β₀ in the regression model yi=β₀+ui.
Derive the OLS estimator \hat{β}₀ in the regression model yi=β₀+ui. Show all of the steps in your derivation.
Consider a simple linear regression model with nonstochastic regressor: Yi = β1 + β2Xi + ui....
Consider a simple linear regression model with nonstochastic regressor: Yi = β1 + β2Xi + ui. 1. [3 points] What are the assumptions of this model so that the OLS estimators are BLUE (best linear unbiased estimates)? 2. [4 points] Let βˆ and βˆ be the OLS estimators of β and β . Derive βˆ and βˆ. 12 1212 3. [2 points] Show that βˆ is an unbiased estimator of β .22
Consider the linear model: Yi = α0 + α1(Xi − X̄) + ui. Find the OLS...
Consider the linear model: Yi = α0 + α1(Xi − X̄) + ui.
Find the OLS estimators of α0 and α1. Compare with the OLS
estimators of β0 and β1 in the standard model discussed in class
(Yi = β0 + β1Xi + ui).
Consider the linear model: Yį = ao + Q1(X; - X) + Ui. Find the OLS estimators of do and a1. Compare with the OLS estimators of Bo and B1 in the standard model discussed in...
4. Consider the model yi-β +82i + ei. Find the OLS estimator for β.
4. Consider the model yi-β +82i + ei. Find the OLS estimator for β.
Prove that the OLS estimator As for β in the linear regression model is consistent Let's first sh...
Taking the yellow parts below as a model to solve the
question above. Thank you!!!!!!!!
Prove that the OLS estimator As for β in the linear regression model is consistent Let's first show that the OLS estimator is consistent Recall the result for β LS-(Lil Xix;厂E-1 xīYi Using Yi = X(B* + ui By the WLLN Assuming that E(X,X is non-negative definite (so that its inverse exists) and using Slutsky's theorem It follows In words: ßOLs converges in probability to...
Consider the following slope estimator: b=2i=1 Yi Suppose the true model is ki + Bo +...
Consider the following slope estimator: b=2i=1 Yi Suppose the true model is ki + Bo + Bicite and the model satisfies the Gauss-Markov conditions. Answer the following questions: (a) What assumption in addition to the Gauss-Markov assumptions is required to estimate the model? (b) Show that in general, b is a biased estimator of B1. (c) Outline the special condition(s) under which b is an unbiased estimator of B1.
Problem 3: Absence of Intercept Consider the regression model Y, = BX,+", where , and X,...
Problem 3: Absence of Intercept Consider the regression model Y, = BX,+", where , and X, satisfy Assumptions SLR1-SLR5. Y (i) Let B denote an estimator of B that is constructed as P where Y and X as are the sample means of Y,and X,, respectively. Show that B is conditionally unbiased. Derive the least squares estimator of B. Show that the estimator is conditionally unbiased. Derive the conditional variance of the estimator. (ii) (iii) (iv) 2
1. Consider a regression model Yi = x;ß +ei, i = 1,...,n. You estimate this model...
1. Consider a regression model Yi = x;ß +ei, i = 1,...,n. You estimate this model using the OLS estimator. (a) Present and discuss assumptions for the OLS estimation.
Problem 2. (Regression without intercept, 50 pts) Suppose you are given the model: Y; = BX;...
Problem 2. (Regression without intercept, 50 pts) Suppose you are given the model: Y; = BX; + Ui, E[u;|Xį] = 0. A) Derive the OLS estimator ß. B) After you estimate B, you can obtain the residual û; = Y; – ĢXį. Does 21-1 Ûi = 0? Explain why and show your derivation.
Question 1 Consider the following model Yi = B.z; + u (a) Derive the OLS estimator of B, B. (6 marks] (b) Show that is unbiased. [9 marks] (c) Find the variance of B. [7 marks]
Consider the linear model: Yi = α0 + α1(Xi − X̄) + ui.
Find the OLS estimators of α0 and α1. Compare with the OLS
estimators of β0 and β1 in the standard model discussed in class
(Yi = β0 + β1Xi + ui).
Consider the linear model: Yį = ao + Q1(X; - X) + Ui. Find the OLS estimators of do and a1. Compare with the OLS estimators of Bo and B1 in the standard model discussed in...
4. Consider the model yi-β +82i + ei. Find the OLS estimator for β.
Taking the yellow parts below as a model to solve the
question above. Thank you!!!!!!!!
Prove that the OLS estimator As for β in the linear regression model is consistent Let's first show that the OLS estimator is consistent Recall the result for β LS-(Lil Xix;厂E-1 xīYi Using Yi = X(B* + ui By the WLLN Assuming that E(X,X is non-negative definite (so that its inverse exists) and using Slutsky's theorem It follows In words: ßOLs converges in probability to...
Consider the following slope estimator: b=2i=1 Yi Suppose the true model is ki + Bo + Bicite and the model satisfies the Gauss-Markov conditions. Answer the following questions: (a) What assumption in addition to the Gauss-Markov assumptions is required to estimate the model? (b) Show that in general, b is a biased estimator of B1. (c) Outline the special condition(s) under which b is an unbiased estimator of B1.
Problem 3: Absence of Intercept Consider the regression model Y, = BX,+", where , and X, satisfy Assumptions SLR1-SLR5. Y (i) Let B denote an estimator of B that is constructed as P where Y and X as are the sample means of Y,and X,, respectively. Show that B is conditionally unbiased. Derive the least squares estimator of B. Show that the estimator is conditionally unbiased. Derive the conditional variance of the estimator. (ii) (iii) (iv) 2
1. Consider a regression model Yi = x;ß +ei, i = 1,...,n. You estimate this model using the OLS estimator. (a) Present and discuss assumptions for the OLS estimation.
Problem 2. (Regression without intercept, 50 pts) Suppose you are given the model: Y; = BX; + Ui, E[u;|Xį] = 0. A) Derive the OLS estimator ß. B) After you estimate B, you can obtain the residual û; = Y; – ĢXį. Does 21-1 Ûi = 0? Explain why and show your derivation.
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