Two methods of identifying cointegrating vectors are commonly used: linear restrictions and the nonlinear method of Johansenos maximum likelihood procedure. That linear method can produce invalid estimates while the Johansen approach always produces valid estimates has been recognised in several...

In this paper we have demonstrated the implications of incorrectly normalising the parameters of a reduced rank regression model to achieve global identification, and presented a method for estimating this model without using the ordering restrictions imposed in previous Bayesian and frequentist...

Estimation of the reduced rank regression model requires restrictions be imposed upon the model. Two forms of restrictions are commonly used. Earlier Bayesian work relied on the triangular method of identification which imposes an a priori ordering on the variables in the system, however,...

In this paper we propose a new methodology for selecting the window length in Singular Spectral Analysis in which the window length is determined from the data prior to the commencement of modeling. The selection procedure is based on statistical tests designed to test the convergence of the...

In Singular Spectrum Analysis (SSA) window length is a critical tuning parameter that must be assigned by the practitioner. This paper provides a theoretical analysis of signal-noise separation and reconstruction in SSA that can serve as a guide to optimal window choice. We establish numerical...

This research proposes that, in cases where threshold covariates are either unavailable or difficult to observe, practitioners should treat these characteristics as latent, and use simulated maximum likelihood techniques to control for them. Two econometric frameworks for doing so in a more...

In this paper we study a statistical method of implementing quasi-Bayes estimators for nonlinear and nonseparable GMM models, that is motivated by the ideas proposed in Chernozhukov and Hong (2003) and Creel and Kristensen (2011) and that combines simulation with nonparametric regression in the...

Statistical models can play a crucial role in decision making. Traditional model validation tests typically make restrictive parametric assumptions about the model under the null and the alternative hypotheses. The majority of these tests examine one type of change at a time. This paper presents...

In this article we investigate the theoretical behaviour of finite lag VAR(n) models fitted to time series that in truth come from an infinite order VAR(?) data generating mechanism. We show that overall error can be broken down into two basic components, an estimation error that stems from the...