variance σ12(z) is o(1) with i.i.d data. We have
E% [H1T(s,t,Qθ)-H1T(s,t,Qθ-CT)]2
≤ Λ E% { [1t(Qθ) - Ft(Qθ)][1s(Qθ) - Fs(Qθ)]
-[1t(Qθ-CT)-Ft(Qθ-CT)][1s(Qθ-CT)-Fs(Qθ-CT)] }2
≤Λ E%{Ft(Qθ)[1-Ft(Qθ)]Fs(Qθ)[1-Fs(Qθ)]}
+E%{Ft(Qθ-CT)[1-Ft(Qθ-CT)]Fs(Qθ-CT)[1-Fs(Qθ-CT)]}
-2E{ [Ft(min(Qθ,Qθ-CT)-Ft(Qθ)Ft(Qθ-CT)]
×[Fs(min(Qθ,Qθ-CT)-Fs(Qθ)Fs(Qθ-CT)] }
=Λ E%{[Ft(Qθ)-Ft(Qθ)Ft(Qθ)][Fs(Qθ)-Fs(Qθ)Fs(Qθ)]}
-Λ E%{ [Ft(min(Qθ,Qθ-CT)-Ft(Qθ)Ft(Qθ-CT)]
×[Fs(min(Qθ,Qθ-CT)-Fs(Qθ)Fs(Qθ-CT)] }
+Λ E%{ [Ft(Qθ-CT)-Ft(Qθ-CT)Ft(Qθ-CT)]
×[Fs(Qθ-CT)-Fs(Qθ-CT)Fs(Qθ-CT)] }
-Λ E%{ [Ft(min(Qθ,Qθ-CT)-Ft(Qθ)Ft(Qθ-CT)]
×[Fs(min(Qθ,Qθ-CT)-Fs(Qθ)Fs(Qθ-CT)] }
≤Λ ∣Ct∣ = o (1). (A.20)
where the last equality holds by the smoothness of conditional distribution function and its
bounded first derivative due to Assumption (A.8). Thus we have
Thm,[ J,( Qθ ) - Jɪ( Qθ - C )] = Op (1) (A.21)
[2] Thm'2[J2(Qβ)- J2(Qβ -C)] = Op(1):
Noting that H2t (5, t, Qθ ) = 0 because of Fyz (Qθ (xs ) | zs ) - θ = 0, we have
J2(Qθ)-J2(Qθ-CT)
15
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