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Consider the following nonlinear regression model: š‘¦ š‘” = š›¼ š‘„ š‘” š›½ + šœ€ š‘” Assume i.i.d. data and š”¼ [ šœ€ š‘” | š‘„ š‘” ] = 0 . To estimate š›¼ and š›½ by GMM, we use the following moment conditions: š”¼ [ š‘¦ š‘” āˆ’ š›¼ š‘„ š‘” š›½ ] = 0 š”¼ [ ( š‘¦ š‘” āˆ’ š›¼ š‘„ š‘” š›½ ) š‘„ š‘” ] = 0 We have an i.i.d. sample with š‘‡ = 8000 observations, with āˆ‘ š‘” = 1 š‘‡ š‘„ š‘” = 2000 , āˆ‘ š‘” = 1 š‘‡ š‘„ š‘” 2 = 4000 and āˆ‘ š‘” = 1 š‘‡ š‘„ š‘” 3 = 8000 . We obtain point estimates š›¼ Ģ‚ = āˆ’ 5 and š›½ Ģ‚ = 3 . To compute the variance of the estimates, we need to estimate the matrix š›¤ 0 , š›¤ Ģ‚ 0 = [ š›¤ Ģ‚ 11 š›¤ Ģ‚ 12 š›¤ Ģ‚ 21 š›¤ Ģ‚ 22 ] Then, the value š›¤ Ģ‚ 11 is:

选锹
A.š›¤ Ģ‚ 11 = 4000
B.š›¤ Ģ‚ 11 = 0.5
C.š›¤ Ģ‚ 11 = āˆ’ 1
D.There is not enough information to compute š›¤ Ģ‚ 11 .
E.š›¤ Ģ‚ 11 = āˆ’ 0.25
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We start by restating the essential pieces of the problem and the candidate answers, so each option can be assessed in context. - The nonlinear regression model is y_t = α x_t^β, with i.i.d. data and E[ε_t|x_t] = 0. The GMM moment conditions given are: E[y_t āˆ’ α x_t^β] = 0 E[(y_t āˆ’ α x_t^β) x_t] = 0 - The problem provides a sample of size T = 8000, and the sample sums: āˆ‘ x_t = 2000, āˆ‘ x_t^2 = 4000, āˆ‘ x_t^3 = 8000. The point estimates obtained are α̂ = āˆ’5 and β̂ = 3. The task asks for the estimated value of Γ̂11, the (1,1) entry of the matrix Γ̂0, which is the expectation ......Login to view full explanation

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Consider the following nonlinear regression model: yi=α+eβxi+εi, Assume i.i.d. data and š”¼[εi|xi]=0. To estimate α and β by GMM, we use the two theoretical moment conditions š”¼[yiāˆ’Ī±āˆ’eβxi]=0 š”¼[(yiāˆ’Ī±āˆ’eβxi)xi]=0 To compute the variance of the GMM estimator we need the matrices Ī“0 and Φ0.

Consider the following nonlinear regression model: yi=α+eβxi+εi, Assume i.i.d. data and š”¼[εi|xi]=0. To estimate α and β by GMM, we need two moment conditions. Choose the best answer below:

Consider the following nonlinear regression model: š‘¦ š‘– = š›¼ + š›½ š‘„ š‘– + šœ€ š‘– , Assume i.i.d. data and š”¼ [ šœ€ š‘– | š‘„ š‘– ] = 0 . To estimate š›¼ and š›½ by GMM, we use the two theoretical moment conditions š”¼ [ š‘¦ š‘– āˆ’ š›¼ āˆ’ š›½ š‘„ š‘– ] = 0 š”¼ [ ( š‘¦ š‘– āˆ’ š›¼ āˆ’ š›½ š‘„ š‘– ) š‘„ š‘– ] = 0 To compute the variance of the GMM estimator we need the matrices š›¤ 0 and š›· 0 .

Consider the following linear regression model: š‘¦ š‘– = š›¼ + š›½ š‘„ š‘– + šœ€ š‘– , Assume i.i.d. data and š”¼ [ šœ€ š‘– | š‘„ š‘– ] = 0 . To estimate š›¼ and š›½ by GMM, we use the three theoretical moment conditions š”¼ [ š‘¦ š‘– āˆ’ š›¼ āˆ’ š›½ š‘„ š‘– ] = 0 š”¼ [ ( š‘¦ š‘– āˆ’ š›¼ āˆ’ š›½ š‘„ š‘– ) š‘„ š‘– ] = 0 š”¼ [ ( š‘¦ š‘– āˆ’ š›¼ āˆ’ š›½ š‘„ š‘– ) š‘„ š‘– 2 ] = 0 To compute the variance of the GMM estimator we need the matrices š›¤ 0 and š›· 0 .

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