Abstract: | In this paper a form of the Lindeberg condition appropriate for martingale differences is used to obtain asymptotic normality of statistics for regression and autoregression. The regression model is yt = Bzt + vt. The unobserved error sequence {vt} is a sequence of martingale differences with conditional covariance matrices {Σt} and satisfying supt=1,…, n
{v′tvtI(v′tvt>a) |zt, vt−1, zt−1, …}
0 as a → ∞. The sample covariance of the independent variables z1, …, zn, is assumed to have a probability limit M, constant and nonsingular; maxt=1,…,nz′tzt/n
0. If (1/n)Σt=1nΣt
Σ, constant, then √nvec(
n−B)
N(0,M−1Σ) and
n
Σ. The autoregression model is xt = Bxt − 1 + vt with the maximum absolute value of the characteristic roots of B less than one, the above conditions on {vt}, and (1/n)Σt=max(r,s)+1(Σtvt−1−rv′t−1−s)
δrs(ΣΣ), where δrs is the Kronecker delta. Then √nvec(
n−B)
N(0,Γ−1Σ), where Γ = Σs = 0∞BsΣ(B′)s. |