A note on asymptotic testing theory for nonhomogeneous observations
This note shows, for ergodic and nonergodic models, how previous results on the limit distributions of the likelihood ratio, score and Wald statistics can be extended under full matrix normalization. Compared to \(n^{1/2}\)- or diagonal norming this allows, just as in asymptotic estimation theory, for more heterogeneity of the data. As a key tool the Cholesky square root is used instead of the common symmetric square root.
scientific article; zbMATH DE number 4052792