The Local Power of the Tests of Overidentifying Restrictions.

The local power function of the size-corrected likelihood ratio, linearized likelihood ratio, and Lagrange multiplier tests of overidentifying restrictions on a structural equation is the same to the order 1/T. Moreover, this local power function doe s not depend on the k-class estimator used in the calculation of the test statistic. When the author does not use size-corrected tests, a degrees of freedom corrected likelihood ratio test seems to have the best size and power properties. Finally, the implicit null hypothesis of these tests indicates that they can be interpreted as testing the validity of the structural specification of the equation against any other identified structural equation that encompasses the original e quation. Copyright 1988 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Improving Some Instrumental Variables Test Procedures

This paper is concerned with Cornish–Fisher corrections of some instrumental variables test statistics. The tests based on the corrected statistics have size with error of a smaller order of magnitude than the original tests. Symmetric Edgeworth-corrected confidence regions are also defined for the structural parameters. All these corrections are given as analytic formulas that require only limited information, so their implementation is a relatively easy task.

Stochastic Expansions and Asymptotic Approximations

Under general conditions the distribution function of the first few terms in a stochastic expansion of an econometric estimator or test statistic provides an asymptotic approximation to the distribution function of the original estimator or test statistic with an error of order less than that of the limiting normal or chi-square approximation. This can be used to establish the validity of several refined asymptotic methods, including the comparison of Nagar-type moments and the use of formal Edgeworth or Edgeworth-type approximations.

Context Monitoring Optimization in Autonomic Networks

The recent advances in network management systems suggest the adoption of autonomic mechanisms in order to minimize the need for human intervention while handling complex heterogeneous networks. Data acquisition performed by monitoring processes is an essential part of autonomic mechanisms. The rate of sampling is a crucial factor since it is related to (1) the successful/unsuccessful detection of events, (2) the processing power needed to perform the sampling and (3) the energy that a node consumes during such actions. In order to address these issues we designed a simple and efficient mechanism that dynamically adapts the sampling rate of the context monitoring procedure. The merits of the mechanism are quantified by means of an analytical model as well as through extensive simulations that validated the theoretic outcomes. Finally, we experimentally assessed the effectiveness and efficiency of our approach through two real-world experiments. Overall results showcase that our mechanism achieves high detection rates while in parallel minimizes significantly the number of monitoring loops thus, emerges as a viable approach for context monitoring optimization in autonomic networks. © 2014, Springer Science+Business Media New York