Zero-non-zero patterned vector error correction modeling for I(2) cointegrated time series with applications in testing PPP and stock market relationships

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

Morphology of breast cancer as a means of triage of patients for BRCA1 genetic testing

Background: Women who have germline mutations in the BRCA1 gene are at substantially increased lifetime risk of developing breast and ovarian cancer but are otherwise normal. Currently. early age of onset of cancer and a strong family history are relied upon as the chief clues as to who should be offered genetic testing. Certain morphologic and immunohistochemical features are overrepresented in BRCA1-associated breast cancers but these differences have not been incorporated into the current selection criteria for genetic testing. Design: Each of the 4 pathologists studied 30 known cases of BRCA1- and BRCA2-associated breast cancer from kConFab families. After reviewing the literature, we agreed on a semiquantitative scoring system for estimating the chances of presence of an underlying BRCA1 mutation, based on the number of the reported prototypic features present. After a time lag of 12 months, we each examined a series of 62 deidentified cases of breast cancer, inclusive of cases of BRCA1-associated breast cancer and controls. The controls included cases of BRCA2-associated breast cancer and sporadic cases. Results: Our predictions had a sensitivity of 92%, specificity of 86%, positive predictive value of 61%, and negative predictive value of 98%. For comparison the sensitivity of currently used selection criteria are in the range of 25% to 30%. Conclusion: The inclusion of morphologic and immunohistochemical features of breast cancers in algorithms to predict the likelihood of presence of germline mutations in the BRCA1 gene improves the accuracy of the selection process.

The role of BNP testing in heart failure

Brain natriuretic peptide (BNP) levels are simple and objective measures of cardiac function. These measurements can be used to diagnose heart failure, including diastolic dysfunction, and using them has been shown to save money in the emergency department setting. The high negative predictive value of BNP tests is particularly helpful for ruling out heart failure. Treatment with angiotensin-converting enzyme inhibitors, angiotensin-II receptor blockers, spironolactone, and diuretics reduces BNP levels, suggesting that BNP testing may have a role in monitoring patients with heart failure. However, patients with treated chronic stable heart failure may have levels in the normal range (i.e., BNP less than 100 pg per mL and N-terminal proBNP less than 125 pg per mL in patients younger than 75 years). Increases in BNP levels may be caused by intrinsic cardiac dysfunction or may be secondary to other causes such as pulmonary or renal diseases (e.g., chronic hypoxia). BNP tests are correlated with other measures of cardiac status such as New York Heart Association classification. BNP level is a strong predictor of risk of death and cardiovascular events in patients previously diagnosed with heart failure or cardiac dysfunction.

Dealing with non-determinism in testing concurrent java components

The testing of concurrent software components can be difficult due to the inherent non-determinism present in these components. For example, if the same test case is run multiple times, it may produce different results. This non-determinism may lead to problems with determining expected outputs. In this paper, we present and discuss several possible solutions to this problem in the context of testing concurrent Java components using the ConAn testing tool. We then present a recent extension to the tool that provides a general solution to this problem that is sufficient to deal with the level of non-determinism that we have encountered in testing over 20 components with ConAn. © 2005 IEEE

On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

In this paper, we address some issue related to evaluating and testing evolutionary algorithms. A landscape generator based on Gaussian functions is proposed for generating a variety of continuous landscapes as fitness functions. Through some initial experiments, we illustrate the usefulness of this landscape generator in testing evolutionary algorithms.

Improving wheat simulation capabilities in Australia from a cropping systems perspective - II. Testing simulation capabilities of wheat growth

To simulate cropping systems, crop models must not only give reliable predictions of yield across a wide range of environmental conditions, they must also quantify water and nutrient use well, so that the status of the soil at maturity is a good representation of the starting conditions for the next cropping sequence. To assess the suitability for this task a range of crop models, currently used in Australia, were tested. The models differed in their design objectives, complexity and structure and were (i) tested on diverse, independent data sets from a wide range of environments and (ii) model components were further evaluated with one detailed data set from a semi-arid environment. All models were coded into the cropping systems shell APSIM, which provides a common soil water and nitrogen balance. Crop development was input, thus differences between simulations were caused entirely by difference in simulating crop growth. Under nitrogen non-limiting conditions between 73 and 85% of the observed kernel yield variation across environments was explained by the models. This ranged from 51 to 77% under varying nitrogen supply. Water and nitrogen effects on leaf area index were predicted poorly by all models resulting in erroneous predictions of dry matter accumulation and water use. When measured light interception was used as input, most models improved in their prediction of dry matter and yield. This test highlighted a range of compensating errors in all modelling approaches. Time course and final amount of water extraction was simulated well by two models, while others left up to 25% of potentially available soil water in the profile. Kernel nitrogen percentage was predicted poorly by all models due to its sensitivity to small dry matter changes. Yield and dry matter could be estimated adequately for a range of environmental conditions using the general concepts of radiation use efficiency and transpiration efficiency. However, leaf area and kernel nitrogen dynamics need to be improved to achieve better estimates of water and nitrogen use if such models are to be use to evaluate cropping systems. (C) 1998 Elsevier Science B.V.

The Swiss heroin trials: testing alternative approaches

Over half a million heroin misusers receive oral methadone maintenance treatment world-wide1 but the maintenance prescription of injectable opioid drugs, like heroin, remains controversial. In 1992 Switzerland began a large scale evaluation of heroin and other injectable opiate prescribing that eventually involved 1035 misusers. 2 3 The results of the evaluation have recently been reported.4 These show that it was feasible to provide heroin by intravenous injection at a clinic, up to three times a day, for seven days a week. This was done while maintaining good drug control, good order, client safety, and staff morale. Patients were stabilised on 500 to 600 mg heroin daily without evidence of increasing tolerance. Retention in treatment was 89% at six months and 69% at 18 months.4 The self reported use of non-prescribed heroin fell signifianctly, but other drug use was minimally affected. The death rate was 1% per year, and there were no deaths from overdose among participants . . . [Full text of this article]

Formal derivation of finite state machines for class testing

Previous work on generating state machines for the purpose of class testing has not been formally based. There has also been work on deriving state machines from formal specifications for testing non-object-oriented software. We build on this work by presenting a method for deriving a state machine for testing purposes from a formal specification of the class under test. We also show how the resulting state machine can be used as the basis for a test suite developed and executed using an existing framework for class testing. To derive the state machine, we identify the states and possible interactions of the operations of the class under test. The Test Template Framework is used to formally derive the states from the Object-Z specification of the class under test. The transitions of the finite state machine are calculated from the derived states and the class's operations. The formally derived finite state machine is transformed to a ClassBench testgraph, which is used as input to the ClassBench framework to test a C++ implementation of the class. The method is illustrated using a simple bounded queue example.

Weathering of hydrocarbons in mangrove sediments: testing the effects of using dispersants to treat oil spills

This field study was a combined chemical and biological investigation of the relative effects of using dispersants to treat oil spills impacting mangrove habitats. The aim of the chemistry was to determine whether dispersant affected the short- or long-term composition of a medium range crude oil (Gippsland) stranded in a tropical mangrove environment in Queensland, Australia. Sediment cores from three replicate plots of each treatment (oil only and oil plus dispersant) were analyzed for total hydrocarbons and for individual molecular markers (alkanes, aromatics, triterpanes, and steranes). Sediments were collected at 2 days, then 1, 7, 13 and 22 months post-spill. Over this time, oil in the six treated plots decreased exponentially from 36.6 +/- 16.5 to 1.2 +/- 0.8 mg/g dry wt. There was no statistical difference in initial oil concentrations, penetration of oil to depth, or in the rates of oil dissipation between oiled or dispersed oil plots. At 13 months, alkanes were >50% degraded, aromatics were similar to 30% degraded based upon ratios of labile to resistant markers. However, there was no change in the triterpane or sterane biomarker signatures of the retained oil. This is of general forensic interest for pollution events. The predominant removal processes were evaporation (less than or equal to 27%) and dissolution (greater than or equal to 56%), with a lag-phase of 1 month before the start of significant microbial degradation (less than or equal to 7%). The most resistant fraction of the oil that remained after 7 months (the higher molecular weight hydrocarbons) correlated with the initial total organic carbon content of the soil. Removal rate in the Queensland mangroves was significantly faster than that observed in the Caribbean and was related to tidal flushing. (C) 1999 Elsevier Science Ltd. All rights reserved.