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Abstract: We study the large-sample properties of the penalized maximum likelihood estimator of a multivariate stochastic regression model with contemporaneously correlated data. The penalty is in terms of the square norm of some (vector) linear function of the regression coefficients. The model subsumes the so-called common transfer function model useful for extracting common signals in a panel of short time series. We show that, under mild regularity conditions, the penalized maximum likelihood estimator is consistent and asymptotically normal. The asymptotic bias of the regression coefficient estimator is also derived. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to study the change point estimation problem in multi-phase regression models. This is a non-regular statistical estimation; thus the asymptotic distribution of the maximum likelihood estimator is verified by means of the weak convergence of the likelihood ratio process. These weak convergence results differ depending on the jump size of the regression function. [Copyright &y& Elsevier]