Evaluating Specification Tests for Markov-Switching Time-Series Models

We evaluate the performance of several specification tests for Markov regime-switching time-series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung–Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung–Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov-switching generalized ARCH (GARCH) model fitted to the US dollar–British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data.

A Reliable Residual Based A Posteriori Error Estimator for a Quadratic Finite Element Method for the Elliptic Obstacle Problem

A residual based a posteriori error estimator is derived for a quadratic finite element method (FEM) for the elliptic obstacle problem. The error estimator involves various residuals consisting of the data of the problem, discrete solution and a Lagrange multiplier related to the obstacle constraint. The choice of the discrete Lagrange multiplier yields an error estimator that is comparable with the error estimator in the case of linear FEM. Further, an a priori error estimate is derived to show that the discrete Lagrange multiplier converges at the same rate as that of the discrete solution of the obstacle problem. The numerical experiments of adaptive FEM show optimal order convergence. This demonstrates that the quadratic FEM for obstacle problem exhibits optimal performance.

Cytokine gene polymorphisms in ischaemic heart disease:Investigation using family based tests of association

Atherosclerosis has an inflammatory basis, with cytokines, cellular adhesion molecules and pro-inflammatory cells having important roles in the initiation and progression of this process. Interleukin (IL) 6, IL-10 and transforming growth factor (TGF) β have been proposed as important modulators of the atherosclerotic process, with IL-6 having a pro-inflammatory, atherogenic effect and IL-10 and TGF-β having anti-inflammatory, protective roles. The possible role of functional polymorphisms in the promoter regions of the IL-6, IL-10 and TGF-β genes in the susceptibility to ischaemic heart disease (IHD) was investigated in a well-defined Irish population using two recently described family-based tests of association. We genotyped 1,012 individuals from 386 families with at least one member prematurely affected with IHD. Using the combined transmission disequilibrium test (TDT)/sib-TDT and the pedigree disequilibrium test, no association between any of the IL-6 -174G/C, IL-10 -1082G/A and TGF-β -509C/T polymorphisms and IHD was found. Our data demonstrate that, in an Irish population, these polymorphisms are not associated with IHD. © Springer-Verlag 2004.

Residual-based test for Nonlinear Cointegration with application in PPPs

Nested by linear cointegration first provided in Granger (1981), the definition of nonlinear cointegration is presented in this paper. Sequentially, a nonlinear cointegrated economic system is introduced. What we mainly study is testing no nonlinear cointegration against nonlinear cointegration by residual-based test, which is ready for detecting stochastic trend in nonlinear autoregression models. We construct cointegrating regression along with smooth transition components from smooth transition autoregression model. Some properties are analyzed and discussed during the estimation procedure for cointegrating regression, including description of transition variable. Autoregression of order one is considered as the model of estimated residuals for residual-based test, from which the teststatistic is obtained. Critical values and asymptotic distribution of the test statistic that we request for different cointegrating regressions with different sample sizes are derived based on Monte Carlo simulation. The proposed theoretical methods and models are illustrated by an empirical example, comparing the results with linear cointegration application in Hamilton (1994). It is concluded that there exists nonlinear cointegration in our system in the final results.

Residual-based Inference for Common Nonlinear Features

This paper investigates common nonlinear features in multivariate nonlinear autore-gressive models via testing the estimated residuals. A Wald-type test is proposed and itis asymptotically Chi-squared distributed. Simulation studies are given to examine thefinite-sample properties of the proposed test.

New improved tests for cointegration with structural breaks

This article proposes Lagrange multiplier-based tests for the null hypothesis of no cointegration. The tests are general enough to allow for heteroskedastic and serially correlated errors, deterministic trends, and a structural break of unknown timing in both the intercept and slope. The limiting distributions of the test statistics are derived, and are found to be invariant not only with respect to the trend and structural break, but also with respect to the regressors. A small Monte Carlo study is also conducted to investigate the small-sample properties of the tests. The results reveal that the tests have small size distortions and good power relative to other tests. © 2007 The Authors Journal compilation 2007 Blackwell Publishing Ltd.

New simple tests for panel cointegration

In this paper, two new simple residual-based panel data tests are proposed for the null of no cointegration. The tests are simple because they do not require any correction for the temporal dependencies of the data. Yet they are able to accommodate individual specific short-run dynamics, individual specific intercept and trend terms, and individual specific slope parameters. The limiting distributions of the tests are derived and are shown to be free of nuisance parameters. The Monte Carlo results in this paper suggest that the asymptotic results are borne out well even in very small samples. Copyright © Taylor & Francis, Inc.

Nonparametric entropy-based tests of independence between stochastic processes

This paper develops nonparametric tests of independence between two stationary stochastic processes. The testing strategy boils down to gauging the closeness between the joint and the product of the marginal stationary densities. For that purpose, I take advantage of a generalized entropic measure so as to build a class of nonparametric tests of independence. Asymptotic normality and local power are derived using the functional delta method for kernels, whereas finite sample properties are investigated through Monte Carlo simulations.

Validation of assembled nucleic acid-based tests in diagnostic microbiology laboratories

Medical microbiology and virology laboratories use nucleic acid tests (NAT) to detect genomic material of infectious organisms in clinical samples. Laboratories choose to perform assembled (or in-house) NAT if commercial assays are not available or if assembled NAT are more economical or accurate. One reason commercial assays are more expensive is because extensive validation is necessary before the kit is marketed, as manufacturers must accept liability for the performance of their assays, assuming their instructions are followed. On the other hand, it is a particular laboratory's responsibility to validate an assembled NAT prior to using it for testing and reporting results on human samples. There are few published guidelines for the validation of assembled NAT. One procedure that laboratories can use to establish a validation process for an assay is detailed in this document. Before validating a method, laboratories must optimise it and then document the protocol. All instruments must be calibrated and maintained throughout the testing process. The validation process involves a series of steps including: (i) testing of dilution series of positive samples to determine the limits of detection of the assay and their linearity over concentrations to be measured in quantitative NAT; (ii) establishing the day-to-day variation of the assay's performance; (iii) evaluating the sensitivity and specificity of the assay as far as practicable, along with the extent of cross-reactivity with other genomic material; and (iv) assuring the quality of assembled assays using quality control procedures that monitor the performance of reagent batches before introducing new lots of reagent for testing.

Assessing Methods for Assigning SNPs to Genes in Gene-Based Tests of Association Using Common Variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

Modelling the linkages between the Australian and G7 stock markets : common stochastic trends and regime shifts

This paper examines whether the Australian equity market is integrated with the equity markets of the G7 economies by applying both the Johansen (Statistical analysis of conintegrating vectors, Journal of Economic Dynamics and Control, 12, 231-54, 1988) and Gregory and Hansen (Residual-based tests for cointegration in models with regime shifts, Journal of Econometrics, 70, 99-126, 1996) approaches to cointegration. Some evidence of a pairwise long-run relationship between the Australian stock market and the stock markets of Canada, Italy, Japan and the United Kingdom is found, but the Australian equity market is not pairwise cointegrated with the equity markets of France, Germany or the USA.

Testing for panel cointegration with a level break

This paper proposes four simple tests for the null hypothesis of no cointegration in the presence of a level break. The tests are general enough to allow for endogenous regressors, serial correlation and heterogeneous breaks of unknown timing. The limiting distributions of the tests are derived and critical values are provided. We also conduct a small Monte Carlo study to investigate their finite sample properties. © 2005 Elsevier B.V. All rights reserved.