Identification and Lullback Information in the GLSEM

In this paper we derive very succinctly the necessary and sufficient (nas) conditions for identification in the general linear structural econometric model (GLSEM) by use of the Kullback information apparatus

Identification and Kullback Information in the GLSEM

In this paper we employ the Kullback Information apparatus in (a) obtaining the strong consistency of the maximum likelihood (ML) estimator in the standard version of the general linear structural econometric model (GLSEM); (b) deriving very succinctly the necessary and sufficient (nas) conditions for identification by the use of exclusion restrictions. The arguments given in (a), however, are equally applicable to a wide class of nonlinear models and the arguments in (b) are equally applicable in the context of more general types of restrictions

Identification and Kullback Information in the GLSEM

In this paper we employ the Kullback Information apparatus in (a) obtaining the strong consistency of the maximum likelihood (ML) estimator in the standard version of the general linear structural econometric model (GLSEM); (b) deriving very succinctly the necessary and sufficient (nas) conditions for identification by the use of exclusion restriction. The arguments given in (a), however, are equally applicable to a wide class of nonlinear models and the arguments in (b) are equally applicable in the context of more general types of restrictions

A Note on Heteroskedasticity Issues

The purpose of this paper is to clarify certain issues related to the incidence of heteroskadisticity in the General Linear Model (GLM); to provide simpler and more accessible proofs for a number of propositions, and to allow the results to stand under conditions considerably less stringent that those hitherto available in the literature

Testing for autocorrelation in systems of equations

This paper deals with the problem of testing for the presence of autocorrelation in a system of general linear models (Seemingly Unrelated Regressions, SUR) when the model is formulated as a vector autoregression (VAR) with exogenous variables. The solution presented in this paper is a generalization of the h-statistic for the single equation single parameter case given in Durbin (1970)

The Limiting Distribution of the Cointegration Test Statistic in VAR(n) Models

This paper obtains the limiting distribution of the trace test for cointegration in the context of the VAR (n) model dealt with in Johansen (1988), (1991). The limiting distribution in question turns out to be that of a linear combination of mutually independent chi-squared variables all with the same degree of freedom parameter. The coefficients of the linear combination are characteristic roots of a certain positive definite matrix which may be estimated consistently

On the Meaning of Certain Cointegration Test

In this paper we examine (some of) the cointegration test suggested in Engle and Granger (1987), henceforth EG, those suggested by Johansen (1988), (1991), henceforth J, and those suggested in Dhrymes (1994b), henceforth D. We also explore the relations among the models underlying the various procedures. We find that the test suggested by Johansen cannot possibly test either for the presence of cointegration vectors, if cointegration is known to hold and its rank is known as well

The Design of Monte Carlo Experiments for VAR Models

This paper deals with the design of Monte Carlo experiments in the context of cointegrated VAR models. Such experiments often seek to establish the applicability of asymptotic distributional results for sampled of size 100 to 200, which are typical of macroeconomic times series. Hithertofore, the design of such experiments has relied on certain simple models given in Bannerjee et al. (1986), Engle and Granger (1987), and Phillips (1991). Here we provide the framework for designing experiments based on much more general models, of which the designs above are special cases

A Conformity Test for Cointegration

This paper formulates a conformity test for cointegration in the context of a VAR specification for a multivariate I(1) process. The test statistic is a function of the characteristic roots of the sample covariance matrix of a linear transformation of the cointegral vector; the latter is obtained from unrestricted estimator of the underlying parameters of the VAR. It is further shown that this test procedure is also applicable to the case where the I(1) process is a MIMA(k), i.e. a multivariate integrated moving average process, the moving average being of order k < ∞. The test statistic, under the null of cointegration, has a normal limiting distribution