1T
= -∑ S(Qθ (xt, Zt))CtS(Qθ(xs))fz (zt)
T t=1
1T
+-∑ CrS(Qθ(xt))S(Qθ(xs, Zs))f(Zt)
T t=1
1r
- - ∑ CTS(Qθ (xt ))S(Qθ (Xs )) f z (Zt ), (A.36)
r t=1
where Qθ(xs,Zs) is between Qθ(xs) and Qθ(Zs). Then by using the same procedures as
in (A.27), we have
J3(Qθ)-J3(Qθ-CT)=O(CT). (A.37)
Now we have the result of Step 1 for the proof of Theorem (iii). □
Step 2: Show that JT=J+op(1) under the alternative hypothesis.
Using (7) and uniform convergence rate of kernel regression estimator under β-mixing
process, we have
1 TT
T =------∑ ∑ Kεεs
Tm tsts
T(T- 1)h t=1 s≠t
1
= -∑ E(st | z)fz(zε
T t=1
= ⅛ ∑ e(ε | z ) fz(z )ε
T t=1
+1 ∑ {EE:- | zt )f (zt )- e(ε | zt ) fz(zt )} εt
T t=1
= ⅛ ∑ e(ε | zt ) fz(zt )εt+op(1)
T t=1
=e [e(ε | zt ) fz(zt ε ]+op(1)
=J+op (1)
(A.38)
□
21
More intriguing information
1. The name is absent2. The name is absent
3. The name is absent
4. Knowledge and Learning in Complex Urban Renewal Projects; Towards a Process Design
5. The name is absent
6. The name is absent
7. Stakeholder Activism, Managerial Entrenchment, and the Congruence of Interests between Shareholders and Stakeholders
8. Knowledge, Innovation and Agglomeration - regionalized multiple indicators and evidence from Brazil
9. Explaining Growth in Dutch Agriculture: Prices, Public R&D, and Technological Change
10. The name is absent