Testing the Information Matrix Equality with Robust Estimators



wherefrom, using (4),

ξ ( Y ; θ )= 1

σ2


u2 - 1

u3 - 3u
u
4 - 5u2 +2


2 / IF( Y ; ^; Fθ )

⅛ ( θ

σ ∖ IF( Y ; ^; Fθ )


Note that ξ ( Y ; θ ) does not depend on IF( Y ; /; Fθ ). Take ^ to be equivariant,
i.e.
σ ( aY1 + b, , aYn+b ) = ασ ( Y1, ∙ ∙ ∙ , Yn ), so IF( Y ; ^; Fθ ) = σ IF( u ; σ; Φ)
and

1 /    u2 - 1 - 2IF(u; ^; Φ)

ξ ( Y ; θ ) = — I           u3 3 u

(6)


σ \—4 5u2 + 2 2IF(u; ^; Φ)

A straightforward calculation shows that V = σ-4B , where B is a 3 × 3
matrix with elements
Bij given by

B u = 2 + 4ASV( ^),

B22 =6,

B33 = 46 + 4ASV( ^) 4 E φ ( u 4IF),

B13 = 10 + 4ASV( ^) 2 E φ ( u 4IF) = B 31,

B12 = B21 = B23 = B32 =0,

with IF = IF(u; ^; Φ) and ASV(σ) = Eφ(IF2), the asymptotic variance of
σ when σ = 1. Note that V does not depend on the estimator β that is

chosen. For a given estimator θ = (β, σ)1, let

ʌ .

u i = ( Yi — β)/σ,


n

-1 j
μj = n X"i,

i=1


. ) ʌ

and write M as σ 2 N with

/ μ2 1

I          .ʌ          n .ʌ

I μ3 3 μ1

ξ μ4 — 5μ2 + 2

Taking V+ equal to V+ with σ replaced by ^ yields the test statistic

T = nM '1V+MM = nN^ B+N^.

(7)




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