It is well-know that estimation by reduced rank regression is given by the solution to a generalized eigenvalue problem. This paper presents a new proof to establish this result and provides additional insight into the structure of the estimation problem. The proof is a direct algebraic proof...

In this paper, we propose a new least-squares based procedure to extract exogenous and endogenous latent variables in formative-reflective structural equation models. The procedure is a valuable alternative to PLS-PM and Lisrel; it is fully consistent with the causal structure of...

The paper studies the effect of High Yielding Variety (HYV) of seeds on agricultural yield for the state of West Bengal and, in turn, goes ahead and prepares a postmortem of why, if so, the Òmiracle seedsÓ failed to deliver in West Bengal, as is so often claimed. The results indicate that the...

This paper proposes new iterative reduced-rank regression procedures for seasonal cointegration analysis. The suggested methods are motivated by the idea that modelling the cointegration restrictions jointly at different frequencies may increase efficiency in finite samples. Monte Carlo...

This paper proposes a reduced rank regression framework for constructing coincident and leading indexes. Based on a formal definition that requires that the first differences of the leading index are the best linear predictor of the first differences of the coincident index, it is shown that the...

In this paper we propose a new methodology to build composite coincident and leading indexes. Based on a formal definition which requires that the first difference of the leading index is the best linear predictor of the first difference of the coincident index, we show that the notion of...

The current practice for determining the number of cointegrating vectors, or the cointegrating rank, in a vector autoregression (VAR) requires the investigator to perform a sequence of cointegration tests. However, as was shown in Johansen (1992), this type of sequential procedure does not lead...

Estimation of the I(2) cointegrated vector autoregressive (CVAR) model is considered. Without further restrictions, estimation of the I(1) model is by reduced-rank regression (Anderson (1951)). Maximum likelihood estimation of I(2) models, on the other hand, always requires iteration. This paper...

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and...

For modelling economic and financial time series, multivariate linear and nonlinear systems of equations have become a standard tool. These models can also be applied to non-stationary processes. However, the resulting finite-sample estimates may depend strongly on the specification of the model...