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- Anderson and Rubin (1956) establish consistency in Theorem 12.1 (see beginning of the proof, page 145) within a Gaussian ML framework. Anderson and Amemiya (1988) provide a version of this result in their Theorem 1 for generic distribution of the data, dispensing for compacity of the parameter set but using a more restrictive identification condition. C.3 Local analysis of the first-order conditions of FA estimators Consider the criterion L(θ) = â1 2 log |Σ(θ)| â 1 2
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- diagonal elements of Z via term diag(Z) in the asymptotic distribution of VÌε. In Theorem 2 in Anderson and Amemiya (1988), this term does not appear because in their results the asymptotic distribution of VÌε is centered around diag(1 n εεⲠ) instead of VÌε. Our recentering around VÌε avoids a random bias term. Finally, by applying the CLT to (C.9), the asymptotic distribution of vector Î¸Ì is: â n(Î¸Ì â θÌ) â 1 2 B0 (Bâ² 0J0B0) â1 Bâ² 0 âvec(Σ(θ0)) âθⲠⲠV â1 y â V â1 y
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- Figure 1: The upper panel displays the p-values for the statistic LR(k) for the subperiods from January 1963 to December 2021, stopping at the smallest k such that H0(k) is not rejected at level αn. If no such k is found then p-values are displayed up to kmax. We use rolling windows of T = 20 months moving forward by 12 months each time. The first bar of p-values covers the whole 20 months. Other bars cover the last 12 months of the 20 months subperiod. We flag bear market phases with grey shaded vertical bars. The five lower panels display VÌ y 1/2 for total cross-sectional volatility, FÌâ²FÌ 1/2 for systematic volatility, VÌ Îµ 1/2 for idiosyncratic volatility, as well as RÌ2 and RÌ2 under a single-factor model.
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- m(m+1), AmAâ² m = Im2 + Km,m, and Km,mAm = Am, where Km,m is the commutation matrix (see also Magnus, Neudecker (2007) Theorem 12 in Chapter 2.8). Then, we have: vech(Qâ² ZnQ) = 1 2 Aâ² Tâkvec(Qâ² ZnQ) = 1 2 Aâ² Tâk(Qâ² âQâ² )vec(Zn) = 1 2 Aâ² Tâk(Qâ² âQâ² )AT vech(Zn) = Râ² vech(Zn), where R := 1 2 Aâ² T (QâQ)ATâk is a 1 2 T(T +1)Ãp matrix. Its columns are orthonormal: Râ² R = 1 4 Aâ² Tâk(Qâ² âQâ² )AT Aâ² T (QâQ)ATâk = 1 4 Aâ² Tâk(Qâ² âQâ² )(IT2 +KT,T )(QâQ)ATâk = 1 4 Aâ² Tâk(I(Tâk)2 + KTâk,Tâk)ATâk = 1 2 Aâ² TâkATâk = Ip, since Qâ² Q = ITâk. 30 Furthermore, Dn = 1 n Pn i=1 V [vech(ZÌi)], where ZÌi is the T Ã T matrix having diagonal elements [w2 it â 1]Ïii and off-diagonal elements witwisÏii.
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- The asymptotic expansions (C.10)-(C.11) characterize explicitly the matrices C1(θ) and C2(θ) that appear in Theorem 2 in Anderson and Amemiya (1988). Their derivation is based on an asymptotic normality argument treating Î¸Ì as a M-estimator, see Section C.2. However, neither the asymptotic variance nor a feasible CLT are given in Anderson and Amemiya (1988).
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