Estimation of the scale matrix of a multivariate t-model under entropy loss

This paper deals with the estimation of the scale matrix of a multivariatet-model with unknown location vector and scale matrix to improve upon the usual estimators based on the sample sum of product matrix. The well-known results of the estimation of the scale matrix of the multivariate normal model under the assumption of entropy loss function have been generalized to that of a multivariatet-model. The paper is based on the first author’s unpublished Ph.D. dissertation ‘Estimation of the Scale Matrix of a Multivariate T-model’, University of Western Ontario, Canada. Present address: School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia.     

Nonparametric covariance estimation in multivariate distributions

The estimation problem of the unknown covariance matrix of a multivariate distribution with the known mean is studied under a matrix-valued quadratic loss function. The conditions on the sample sizes for the best unbiased estimator to have a smaller risk than the sample covariance matrix is established. The former estimator is completely (without exceptional sets of Lebesgue measure zero) characterized by its expectation in the class of all multivariate distributions with zero mean and finite fourth moments. Received: November 1998     

Testing constancy of the error covariance matrix in vector models

In this paper a Lagrange multiplier test of the hypothesis that the covariance matrix of a multivariate time series model is constant over time is considered. It is assumed that under the alternative, the error variances are time-varying, whereas the correlations remain constant over time. Under the parameterized alternative hypothesis the variances may change continuously as a function of time or some observable stochastic variables. Small-sample properties of the test statistic are investigated by simulation. The assumption of constant correlations does not appear overly restrictive.     

The emperor's new clothes: a critique of the multivariate t regression model

Zellner (1976) proposed a regression model in which the data vector (or the error vector) is represented as a realization from the multivariate Student t distribution. This model has attracted considerable attention because it seems to broaden the usual Gaussian assumption to allow for heavier-tailed error distributions. A number of results in the literature indicate that the standard inference procedures for the Gaussian model remain appropriate under the broader distributional assumption, leading to claims of robustness of the standard methods. We show that, although mathematically the two models are different, for purposes of statistical inference they are indistinguishable. The empirical implications of the multivariate t model are precisely the same as those of the Gaussian model. Hence the suggestion of a broader distributional representation of the data is spurious, and the claims of robustness are misleading. These conclusions are reached from both frequentist and Bayesian perspectives.     

Labour adjustment costs: Estimation of a dynamic discrete choice model using panel data for Greek manufacturing firms

In this paper we estimate a dynamic structural model of employment at firm level. Our dataset consists of a balanced panel of 2790 Greek manufacturing firms. The empirical evidence of this dataset stresses three important stylized facts: (a) there are periods in which firms decide not to change their labour input, (b) there are periods of large employment changes (lumpy nature of labour adjustment) and (c) the commonality is employment spikes to be followed by smooth and low employment growth periods. Following Cooper and Haltiwanger [Cooper, R.W. and Haltiwanger, J. “On the Nature of Capital Adjustment Costs”, Review of Economic Studies, 2006; 73(3); 611–633], we consider a dynamic discrete choice model of a general specification of adjustment costs including convex, non-convex and “disruption of production” components. We use a method of simulated moments procedure to estimate the structural parameters. Our results indicate considerable fixed costs in the Greek employment adjustment.     

Estimating the impact of enterprise resource planning project management decisions on post-implementation maintenance costs: a case study using simulation modelling

Organisations often make implementation decisions with little consideration for the maintenance phase of an enterprise resource planning (ERP) system, resulting in significant recurring maintenance costs. Poor cost estimations are likely related to the lack of an appropriate framework for enterprise-wide pre-packaged software maintenance, which requires an ongoing relationship with the software vendor (Markus, M.L., Tanis, C., and Fenema, P.C., 2000 Markus, M. L., Tanis, C. and Fenema, P. C. 2000. Multisite ERP implementation. CACM, 43(4): 42–46. [Crossref], [Web of Science ®] , [Google Scholar]. Multisite ERP implementation. CACM, 43 (4), 42–46). The end result is that critical project decisions are made with little empirical data, resulting in substantial long-term cost impacts. The product of this research is a formal dynamic simulation model that enables theory testing, scenario exploration and policy analysis. The simulation model ERPMAINT1 was developed by combining and extending existing frameworks in several research domains, and by incorporating quantitative and qualitative case study data. The ERPMAINT1 model evaluates tradeoffs between different ERP project management decisions and their impact on post-implementation total cost of ownership (TCO). Through model simulations a variety of dynamic insights were revealed that could assist ERP project managers. Major findings from the simulation show that upfront investments in mentoring and system exposure translate to long-term cost savings. The findings also indicate that in addition to customisations, add-ons have a significant impact on TCO.