32 research outputs found
The Local Power of the Tests of Overidentifying Restrictions.
No full textThe 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
No full textThis 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
No full textUnder 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.
A UNIFIED THEORY FOR ARMA MODELS WITH VARYING COEFFICIENTS: ONE SOLUTION FITS ALL
No full text......Alessandra Canepa acknowledges financial support under the National Recovery and Resilience Plan
(NRRP), Mission 4, Component 2, Investment 1.1, Call for tender No. 104 published on 2.2.2022 by the
Italian Ministry of University and Research (MUR), funded by the European Union – NextGenerationEU–
Project Title 20223725WE - Methodological and computational issues in large-scale time series models for
economics and finance – CUP J53D23003960006- Grant Assignment Decree No 967 adopted on 30/06/2023
by the Italian Ministry of Ministry of University and Research (MUR)
Context Monitoring Optimization in Autonomic Networks
No full textThe 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
