Nonparametric cointegration analysis



6 Sn(x ) '

/ ʌ
x

n↑[n

Sn& z (x )

c(ɪ)ʃwty )dy

к c (1) W(x ) ,

√n
vv >


(A.4)


It follows from Lemma 9.6.3 in Bierens (1994, p.200) that


JL                     . c .

FX-(t/n)z, F F(1)S∙(1) - f(x)'∙.(x)dx,
t= 1                                             j


(A.5)


where fk is the derivative of Fk, and similarly for zt. Using (A.4), (A.5), and the straightforward
qualities


Fk(1)Jw(x)dx - ʃfk(xW(y)dydx = ʃFk(x) W(x)dx,

0

Var(jFk(x)W(x)dx ʃ ʃʃFk(x)Fk(y)min(x,y)dxdy ∙ Iq,


(A.6)

(A.7)


Var Fk(1) W(1) - ʃf (x) W(x)dj


(A.8)


= [f (1)2 - 2Fk(1) ʃxfk(x)dx + ʃʃfk(x)fk(y)min(x,y)dxdy1q = (ʃFk(x)2dxl


Cov


6.             ` l                           Ô

Fk(x) W(x)dx, (Fk(1) W(1)-ʃfk(x) W(x)dx}^


ʃʃFk(x )fk(y)min(x y ) dxdy^


(A.9)


ʃFk((x ) ʃFk(y ) dy dx

= ʌ(ʃFk(x)dxI ∙ 1q ,

it follows that (11) holds. The independence of the random vectors Xk and Yk over k follows
from:

38



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