Consider the nonlinear model
yt=θ1x
θ2
t
+εt
where the sample data (y1,x1),...,(yT,xT) are i.i.d. and E(εt|xt)=0. We know that the nonlinear least square estimator is asymptotically normal, that is
√
T
(
ˆ
θ
NL−θ0)
d
⤳
N(0,A
−1
0
Ω0A
−1
0
)
To compute the standard errors we need to estimate A0,
ˆ
A
0=[
1

T
∑
T
t=1
x
2
ˆ
θ
2
t

1

T
∑
T
t=1
ˆ
θ
1x
2
ˆ
θ
2
t
log(xt)
1

T
∑
T
t=1
ˆ
θ
2
1
x
2
ˆ
θ
2
t
log2(xt)]
What is the missing entry in the matrix
ˆ
A
0?

Question

Consider the nonlinear model
yt=θ1x
θ2
t
+εt
where the sample data (y1,x1),...,(yT,xT) are i.i.d. and E(εt|xt)=0. We know that the nonlinear least square estimator is asymptotically normal, that is
√
T
(
ˆ
θ
NL−θ0)
d
⤳
N(0,A
−1
0
Ω0A
−1
0
)
To compute the standard errors we need to estimate A0,
ˆ
A
0=[
1

T
∑
T
t=1
x
2
ˆ
θ
2
t

1

T
∑
T
t=1
ˆ
θ
1x
2
ˆ
θ
2
t
log(xt)	
1

T
∑
T
t=1
ˆ
θ
2
1
x
2
ˆ
θ
2
t
log2(xt)]
What is the missing entry in the matrix 
ˆ
A
0?

BlackTom AI · Accepted Answer

We start by clearly restating the problem and the candidate entries that could complete the (1,1) block of the A0 matrix in the nonlinear least squares asymptotic variance.

Option A: 𝔼(ˆθ1x2 ˆθ2 t log(xt))
This entry would be the expectation of the product of the estimated parameters, x^2, and a log term. Since the A0 matrix is built from moment conditions involving derivatives of the nonlinear model with respect to θ, and T^{-1} sum of terms that depend on t, xt, and the estimated parameters, this particular expectation does not align with the standard sandwich form that uses expectations of the gradient and the score. The log(xt) factor inside an expectation with θ̂ terms is not a typical component of the A0 cross-derivative structure here, so this seems unlikely to be the correct constituent.

Option B: 1/T ∑_{t=1}^T ˆθ2 1/x^2 ˆθ2 t log2(xt)
This expression places ˆθ2, 1/x^2, and a log-squared term inside a summation scaled by 1, which already misplaces the normalization: A0 entries are typically averages (or sums scaled by T) of derivatives or functions of x_t and θ̂_t, not products of θ̂2 with inverse x^2 and a log squared factor. The appearance of log2(xt) (log base 2) instead of natural log is also unusual in standard likelihood or GMM-type asymptotic formulations unless a specific transformation is indicated. Overall, this form looks inconsistent with the standard A0 construction here.

Option C: 1/T ∑_{t=1}^T x^2 ˆθ2 t
This option contains x^2 multiplied by ˆθ2 t inside an average. While x^2 terms are common in A0 when derivatives involve x^2, the absence of the θ̂1 terms and the log components that appear in the exposed model specification makes this candidate incomplete. Moreover, θ̂2 t by itself without the coupling to θ̂1 or log terms would not capture the cross-partials typically present in A0 for a nonlinear model with log-terms. Therefore, this is unlikely to be the correct missing entry.

Option D: 𝔼(ˆθ2 1/x^2 ˆθ2 t log2(xt))
Here we have an expectation of a product involving θ̂2, 1/x^2, θ̂2 t, and log2(xt). The presence of an expectation indicates a population-level moment, which might conceptually fit A0, but the structure ˆθ2 1/x^2 ˆθ2 t mixes a persistent, cross-time parameter with a time-varying log term in a way that is not standard for the A0 sandwich term. The log2(xt) again uses log base 2 rather than natural log, which is atypical unless specified. This option is plausibly inappropriate because of both the odd mixture of terms and the log base.

Option E: 𝔼(x2 ˆθ2 t)
This is the expectation of x^2 times θ̂2 t, a relatively simple moment. While x^2 terms do appear in variance-covariance structures, the A0 block in the nonlinear least squares asymptotics is constructed from derivatives of the model with respect to θ and the variance of the score, not merely a bare moment like E(x^2). This makes the candidate too simplistic and unlikely to represent the missing A0 entry.

Option F: 1/T ∑_{t=1}^T ˆθ1x2 ˆθ2 t log(xt)
This option combines ˆθ1, x^2, ˆθ2 t, and log(xt) within an average. It resembles a cross-term that could plausibly appear in the A0 matrix for a nonlinear model where the gradient involves both θ1 and θ2 and where the log-xt term enters through the model specification. The presence of log(xt) (with natural log, as is standard) aligns with typical log-transform terms in nonlinear models, and the product ˆθ1x^2 ˆθ2 t suggests a cross-partial structure between θ1 and θ2 that may be consistent with the cross-derivative components of the A0 matrix. This candidate is more aligned with the standard form of A0 entries that couple the parameter estimates through moments involving x and log terms.

Summary across options:
- Several choices (A, B, C, D, E) either rely on nonstandard or unlikely moment forms, misplace normalization, or use inappropriate log bases, making them poor fits for the A0 construction in this nonlinear least squares context.
- Option F presents a cross-term that matches the idea of A0 capturing cross-sectional interactions between parameters via the data, including a log-xt component, and uses a 1/T average consistent with the sample-average form of a population moment. This aligns best with how A0 is typically formed in nonlinear least squares asymptotics when the model involves logs and products of functions of x_t and θ̂_t.

Note on the correct identification: the provided answer choice corresponds to the form in Option F, which includes a cross-term involving ˆθ1, x^2, ˆθ2 t, and log(xt) within a 1/T average. While the exact algebraic justification would require writing out the gradient and the outer product of the score, this heuristic explanation explains why the cross-term with log(xt) and x^2 enter in A0 in a way that matches the typical sandwich structure for nonlinear least squares.

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