Dhrymes (1994, Econometric Theory, 10, 254-285) demonstrates the arising identification and estimation problems in singular equation systems when the error vector obeys an autoregressive scheme, as an extension of restricted least squares. Unfortunately, his main theorem concerning the...

This paper presents a comprehensive approach to estimation and hypothesis testing under a set of full restrictions, some of these arising from adding-up conditions on the endogenous variable. In contrast to the existing statistical literature, this paper uses an argumentation style familiar from...

For both deterministic or stochastic regressors, as well as parametric nonlinear or linear regression functions, we prove the weak consistency of the coefficient estimators for the Type I censored quantile regression model under different censoring mechanisms with censoring points depending on...

This paper studies the asymptotic behaviour of the unconditional quantile estimator for dependent random variables. Our proof is based on results from convex stochastic optimization and a mixing process which is specific to quantile estimation and requires only a small part of the...

This paper derives the asymptotic normality of the nonlinear quantile regression estimator with dependent errors. The required assumptions are weak, and it is neither assumed that the error process is stationary nor that it is mixing. In fact, the notion of weak dependence introduced in this...

Dhrymes (1994, Econometric Theory, 10, 254-285) demonstrates the arising identification and estimation problems in singular equation systems when the error vector obeys an autoregressive scheme, as an extension of restricted least squares. Unfortunately, his main theorem concerning the...

This paper considers the implementation of prior stochastic information on unknown outcomes of the response variables into estimation and forecasting of systems of linear regression equations in the context of time series, cross sections, pooled and longitudinal data models. The established...

This article completes and simplifies earlier results on the derivation of best linear, or affine, unbiased estimates in the general Gauss-Markov model with a singular dispersion matrix and additional restrictions under very general conditions. We provide the class of all linear representations...

The topic of this paper is the problem of a singular disturbance covariance matrix in (seemingly unrelated) systems of linear regression equations. This singularity is considered as being caused by exact linear restrictions on the endogenous variables, adding-up to a predetermined aggregate. It...