Consider the following nonlinear regression model:
𝑦
𝑡
=
𝛼
𝑥
𝑡
𝛽
+
𝜀
𝑡
Assume i.i.d. data and
𝔼
[
𝜀
𝑡
|
𝑥
𝑡
]
=
0
. To estimate
𝛼
and
𝛽
by GMM, we use the following moment conditions:
𝔼
[
𝑦
𝑡
−
𝛼
𝑥
𝑡
𝛽
]
=
0
𝔼
[
(
𝑦
𝑡
−
𝛼
𝑥
𝑡
𝛽
)
𝑥
𝑡
]
=
0
To compute the variance of the estimates, we need to estimate the matrices
𝛤
0
and
𝛷
0
.

Question

Consider the following nonlinear regression model:
𝑦
𝑡
=
𝛼
𝑥
𝑡
𝛽
+
𝜀
𝑡
Assume i.i.d. data and 
𝔼
[
𝜀
𝑡
|
𝑥
𝑡
]
=
0
. To estimate 
𝛼
 and 
𝛽
 by GMM, we use the following moment conditions:
𝔼
[
𝑦
𝑡
−
𝛼
𝑥
𝑡
𝛽
]
=
0
𝔼
[
(
𝑦
𝑡
−
𝛼
𝑥
𝑡
𝛽
)
𝑥
𝑡
]
=
0
To compute the variance of the estimates, we need to estimate the matrices 
𝛤
0
 and 
𝛷
0
.

BlackTom AI · Accepted Answer

The problem statement asks to analyze the provided multiple-choice options for how to estimate the variance matrices Γ0 and Φ0 in a GMM framework for the nonlinear regression model y_t = α x_t^β + ε_t (with E[ε_t | x_t] = 0). However, there is a structural issue with the data you supplied: the answer options are missing. The field answer_options is an empty list, and while there is an item in the answer field, it appears to be one possible candidate expression for Φ0hat, it is not presented as a separate, clearly labeled option. Because the question relies on selecting among discrete options, the absence of listed options prevents a definitive choice from being made here. I’ll proceed by outlining the logic you would typically follow to evaluate any such candidate expressions, and then describe what the correct form of the estimators should look like in this GMM setup.

Step 1: Restate the model and the moment conditions to ensure clarity. The model is y_t = α x_t^β + ε_t with E[ε_t | x_t] = 0. The GMM moment conditions given are:
- E[y_t − α x_t^β] = 0
- E[(y_t − α x_t^β) x_t] = 0
These are standard moment conditions for estimating a scalar α and a nonlinear function of x_t (through β) in a single equation context. In practice, one would form the sample analogs using instruments that are exogenous (here, the regressors themselves are used as instruments under standard assumptions).

Step 2: What Γ0 and Φ0 represent in GMM. In GMM, Φ0 typically denotes the variance-covariance matrix of the vector of moment conditions g_t(θ) = [y_t − α x_t^β, (y_t − α x_t^β) x_t]′ evaluated at the true parameter θ0. Γ0 (often denoted as D or G in some texts) is the Jacobian (derivative) of the expectation of the moment conditions with respect to θ, evaluated at θ0. Specifically:
- Φ0 = E[g_t(θ0) g_t(θ0)′]
- Γ0 = ∂E[g_t(θ)]/∂θ′|_{θ=θ0}
In practice, you estimate Φ0 with a consistent sample analog Φ̂0 = (1/T) Σ_t g_t(ˆθ) g_t(ˆθ)′, where ˆθ is a consistent estimator of θ0 (often the method-of-moments or nonlinear least squares estimate is used as ˆθ). Γ̂0 is estimated via the Jacobian evaluated at ˆθ, e.g., Γ̂0 = (1/T) Σ_t ∂g_t(θ)/∂θ′|_{θ=ˆθ}.

Step 3: Evaluate the proposed candidate expression for Φ0hat (the one item listed in answer). A correct Φ̂0 should be a 2×2 (since there are two moment conditions) or in some treatments a 2×2 block matrix capturing Var[(y_t − α x_t^β)] and Cov((y_t − α x_t^β), (y_t − α x_t^β) x_t) and Var((y_t − α x_t^β) x_t). The expression provided in the single option seems to attempt to form a covariance-like matrix using terms like (y_t − α x_t^β)^2, and multiples of x_t, but the formatting is garbled and the line breaks/indices are incorrect for a well-defined 2×2 matrix. Moreover, a valid Φ̂0 in this context would depend on the vector g_t(ˆθ) = [e_t, e_t x_t]′ with e_t = y_t − α x_t^β, leading to Φ̂0 = (1/T) Σ_t [e_t^2, e_t^2 x_t; e_t^2 x_t, e_t^2 x_t^2]. Any expression that does not align with this structure or is missing the cross-term e_t^2 x_t^2 would be suspect. The garbled form in the option cannot be deemed correct without a clean, properly dimensioned 2×2 matrix with clearly defined terms.

Step 4: Why the other (nonexistent) options would be wrong in this kind of problem. Generally, incorrect choices for Φ0hat might include:
- Using only the variance of e_t (a 1×1 object) when you actually need a 2×2 matrix that captures both e_t^2 and e_t^2 x_t components.
- Mixing in sample means or using T in a way that changes the scaling (e.g., 1/T versus 1/(T−k)) without justification.
- Omitting the e_t^2 x_t^2 term, which would ignore the variability contributed by the interaction with x_t and thus mis-specify the (2,2) cell of Φ0.
- Using e_t^2 x_t or e_t^2 x_t^2 incorrectly in a manner that violates the required symmetry of a variance-covariance matrix.

Step 5: How you would judge a correct answer if options were present. A correct Φ̂0 would be formed by computing the vector g_t(ˆθ) = [e_t, e_t x_t]′ with e_t = y_t − α̂ x_t^β, and then setting Φ̂0 = (1/T) Σ_t g_t(ˆθ) g_t(ˆθ)′. If the problem also asks for Γ̂0, you would compute the gradient of g_t with respect to θ = [α, β] at θ = ˆθ and take the average over t to obtain Γ̂0. The answer that presents exactly this 2×2 matrix structure with e_t^2 on the (1,1) entry, e_t^2 x_t on the (1,2) and (2,1) entries, and e_t^2 x_t^2 on the (2,2) entry would be correct in standard formulations.

Step 6: Overall takeaway. Because the provided data lacks the explicit list of answer choices, it is not possible to select or verify which option is correct. When evaluating such questions, ensure the proposed Φ̂0 matches the standard expression for the variance of the moment conditions g_t(ˆθ) and that Γ̂0 corresponds to the Jacobian of E[g_t(θ)] with respect to θ, all evaluated at the estimated parameter ˆθ. If you can share the actual list of answer options (labeled A, B, C, ...), I can walk through each one and identify which aligns with the correct GMM construction and why the others fail.

BU.232.630.F3.SP25 Quiz 2 2025

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Similar Questions

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