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Set Identified Linear Models. (2009). Magnac, Thierry ; Maurin, Eric ; Bontemps, Christian.
In: TSE Working Papers.
RePEc:tse:wpaper:22272.

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  1. + ε, x ∈ {−1, 1} Support function δ(q) for q = (0, 1)T True unknown value: 0.375 n Mean Q1 Q2 Q3 100 0.374 0.360 0.375 0.390 500 0.375 0.369 0.375 0.382 1000 0.375 0.371 0.375 0.380 2500 0.375 0.372 0.375 0.378 Table 7: Percentage of rejections for the test H0 : βr ∈ B. Non-smooth set.
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  2. Also, under the conditions of Proposition 9, we have: E max( z> i yi , z> i yi )2 < ∞ so that F = {fθ; θ ∈ Θ} is a Donsker class (for instance, van der Vaart, 1998, page 271). By the definition of such a class, the empirical process, √ nτn(q) = √ n( 1 n n X i=1 zqiwqi − E(zqiwqi)), converges in distribution, uniformly in Θ, to a Gaussian process with zero mean and covariance function: E(zqiwqizriwri) − E(zqiwqi)E(zriwri).
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  15. Define the estimate of δ∗ (q | B) as the value at the near minimizer: ˆ δ∗ n(q | B) = ˆ δ∗ n(sn | BU ). (C.24) First, standard arguments employed for Z-estimators (see van der Vaart, 1998, for instance) imply that: plimn→∞λn = λ0.
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  43. Standard arguments employed for Z-estimators (e.g. van der Vaart, 1998) when the objective function has a unique well separated minimum, imply that: plimn→∞qn = q0.
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  46. Using Andrews (1994, p2251), the three steps prove that τn(q) is a consistent and asymptotically Gaussian random process. Step 1: According to what was developed above, the empirical stochastic process τU n (.), defined for s = (q, λ) ∈ Sm, the unit sphere in Rm , as, τU n (s) = √ n ˆ δ∗ n(s | BU ) − δ∗ (s | BU ) ,
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