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Background Airway ecology is altered in asthma and chronic obstructive pulmonary disease (COPD). Anti‐microbial interventions might have benefit in subgroups of airway disease. Differences in sputum microbial profiles at acute exacerbation of airways disease are reflected by the γProteobacteria:Firmicutes (γP:F) ratio. We hypothesized that sputum microbiomic clusters exist in stable airways disease, which can be differentiated by the sputum γP:F ratio. Methods Sputum samples were collected from 63 subjects with severe asthma and 78 subjects with moderate‐to‐severe COPD in a prospective single centre trial. Microbial profiles were obtained through 16S rRNA gene sequencing. Topological data analysis was used to visualize the data set and cluster analysis performed at genus level. Clinical characteristics and sputum inflammatory mediators were compared across the clusters. Results Two ecological clusters were identified across the combined airways disease population. The smaller cluster was predominantly COPD and was characterized by dominance of Haemophilus at genus level (n = 20), high γP:F ratio, increased H influenzae, low diversity measures and increased pro‐inflammatory mediators when compared to the larger Haemophilus‐low cluster (n = 121), in which Streptococcus demonstrated the highest relative abundance at the genus level. Similar clusters were identified within disease groups individually and the γP:F ratio consistently differentiated between clusters. Conclusion Cluster analysis by airway ecology of asthma and COPD in stable state identified two subgroups differentiated according to dominance of Haemophilus. The γP:F ratio was able to distinguish the Haemophilus‐high versus Haemophilus‐low subgroups, whether the Haemophilus‐high group might benefit from treatment strategies to modulate the airway ecology warrants further investigation., Sputum microbiomic cluster analysis and TDA demonstrates 2 subgroups of stable severe asthma and moderate‐to‐severe COPD differentiated according to dominance of Haemophilus. The Haemophilus‐high group had no defining clinical characteristics but had lower microbial diversity and increased levels of sputum TNFα and IL1β. γProteobacteria:Firmicutes (γP:F) differentiated the clusters and was the most predictive biomarker of the Haemophilus‐high group.

Background - There is increasing interest in the ‘homing’ of neutrophils to bone marrow. The aim of this study was to measure the whole-body redistribution of 111In using a whole-body counter following the administration of ultra-small activities of 111 In-labelled neutrophils. Methods - The detectors of a dedicated whole-body counter were fitted with lead collimators. Whole-body 111 In distribution was recorded at 45 min, 24 h, and 2, 4, 7 and 10 days after administration of 111 In-labelled neutrophils (0.29–0.74 MBq) in eight healthy non-smokers, five healthy smokers, eight patients with inactive bronchiectasis, three with asthma and nine with chronic obstructive pulmonary disease (COPD). Results - Intravascular 45-min 111In-labelled neutrophil recovery was not significantly different between groups, ranging from 33 (SD 8%) in healthy smokers to 45 (14%) in healthy non-smokers (P > 0.05). Peaks were identified on the whole body count profile corresponding to the chest, upper abdomen (liver ⁄ spleen) and pelvis (bone marrow). 111 In distribution changed between 45 min and 24 h and then remained stable thereafter. Peak chest counts increased ~ 1.5-fold between 45 min and 24 h, whereas upper abdominal peak counts decreased by ~ 25% with no significant inter-group differences. The increment in pelvic counts (~2.7-fold) was similar between groups, except COPD patients, in whom it was 2.04 (0.35; P Conclusions - Assuming neutrophils are distributed only between blood, liver, spleen and bone marrow, the data suggest that marrow pools 25% and destroys 67% of circulating neutrophils, rising in COPD to 40% and 80%, respectively, possibly as a result of the effects on marrow of chronic hypoxaemia.

Trends in real prices for food commodities are both important and controversial. Paying particular attention to issues of methodology, this paper assesses the evidence for a downward drift in the real prices of wheat and maize. It is found that the apparent strength of that evidence depends substantially on whether the time series generating models are taken to be trend-stationary or difference-stationary, and on whether allowance is made, through incorporation of dummy variables in the models, for events in one or two extreme years. Once dummy variables are incorporated, we find little evidence against difference-stationarity. The analysis then proceeds, through tests for cointegration, to the construction of error-correction models linking the two prices and to the estimation of persistence of shocks in this bivariate framework. The paper presents modest evidence for downward drift in real grain prices of about 1 to 1.5 per cent per annum, shows that wheat and maize prices cointegrate and estimates that direct and cross-persistence measures take values of less than unity.

What is already known about this subject • When this study was conducted it was already known that the ‘standard’ methods of correcting the QT interval for heart rate were not optimal and that bias could result in their use in certain circumstances, in particular when drugs or conditions also induced wide fluctuations in heart rate. • The use of subject specific correction methods had been advocated and whilst these had been shown to apparently correct QT interval over usual physiological heart rates, they had not been tested under conditions where drugs could potentially increase heart rate and ‘standard’ correction methods gave apparent increases in QT interval. Furthermore, subject specific correction methods had usually been generated over a normal physiological heart rate range and made the assumption that the relationship remained linear outside of this usual range. What this study adds • A subject specific correction factor for QT correction can be calculated from a wide range of heart rates using exercise testing and the relationships between QT and RR are unique for a given individual. • The generation of subject specific correction factors from ECGs collected under resting conditions is not a robust method. • That subject specific correction factors derived from exercise testing are not appropriate to apply to drugs (with no evidence to suggest effects on the QT interval) that induce increases in heart rate and that the relationship between the QT and RR interval during exercise is clearly different to the QT/RR relationship of a drug that increases heart rate. Aims We sought to investigate subject specific QT interval correction factors (SSCF) determined at rest and after exercise and to determine the validity of these factors after the administration of a probe drug known to increase heart rate without directly affecting cardiac repolarization. Methods Thirty-two healthy volunteers underwent graded exercise, multiple recordings of electrocardiogram during rest over a day and a treatment phase administering inhaled placebo or sibenadet (a β2-adrenoceptor/dopamine D2-receptor agonist) at 250, 500 or one of 750 or 1000 µg. SSCF were determined from linear regression of plots of log RR interval vs. log QT after exercise (QTcX), rest (QTcR), and combined data (QTcC). The SSCFs along with Bazett & Fridericia corrections were applied to the ECGs after inhalation of sibenadet. Results SSCFs obtained from the combination of the exercise and resting day (mean QTcC = 0.41) and exercise alone (mean QTcX = 0.40) were similar with a good fit to the data (mean r2 = 0.92 and 0.93, respectively) while data at rest resulted in a less pronounced slope (mean QTcR = 0.27) and poorer fit (mean r2 = 0.52). After the administration of sibenadet, none of the SSCFs, Bazett or Fridericia corrections adequately corrected QT for heart rate induced changes. Conclusions Neither a SSCF from exercise, Bazett’s or Fridericia’s correction factors, adequately corrected the QT interval after the administration of a sympathomimetic agonist drug to increase heart rate in healthy volunteers demonstrating the potential need for QT/RR correction factors to be tailored for each drug studied.

A desirable property of a forecast is that it encompasses competing predictions, in the sense that the accuracy of the preferred forecast cannot be improved through linear combination with a rival prediction. In this paper, we investigate the impact of the uncertainty associated with estimating model parameters in-sample on the encompassing properties of out-of-sample forecasts. Specifically, using examples of non-nested econometric models, we show that forecasts from the true (but estimated) data generating process (DGP) do not encompass forecasts from competing mis-specified models in general, particularly when the number of in-sample observations is small. Following this result, we also examine the scope for achieving gains in accuracy by combining the forecasts from the DGP and mis-specified models.

Although the t-ratio variant of the Dickey–Fuller test is the most commonly applied unit-root test in practical applications, it has been known for some time that readily implementable, more powerful modifications are available. We explore the large-sample properties of five of these modified tests, and the small-sample properties of these five plus six hybrids. As a result of this study we recommend two particular test procedures.

Various studies have investigated trends in commodity prices in the context of the Prebisch Singer Hypothesis. This paper applies new evidence on significance tests for trends in differenced and correlated stationary processes, to individual price series. It is also investigated how well trends are suited for price forecasts and what these findings imply for developing countries. Few commodity price series are well characterized by a trend and trends are useful for forecasting in even fewer cases. Commodity specialization is still problematic for LDCs but the main issue is a lack of predictability rather than a secular decline. Copyright © 2005 John Wiley & Sons, Ltd.

We analyse the case where a unit-root test is based on a Dickey–Fuller regression the only deterministic term of which is a fixed intercept. Suppose, however, as could well be the case, that the actual data-generating process includes a broken linear trend. It is shown theoretically, and verified empirically, that under the I(1) null and I(0) alternative hypotheses the Dickey–Fuller test can display a wide range of different characteristics depending on the nature and location of the break.

Assume that a time series is generated by an autoregression which has atmost one unit root. A correctly specified model, including linear time trend, is estimated by ordinary least squares, but no allowance is made for any unit root in the generating process. We investigate the impact of estimation error on the mean-squared error of forecasts calculated from the fitted model.

In this paper, we consider tests for a break in the level of a series at an unknown point in time. It is often the case that uncertainty exists concerning the order of integration of the series; consequently, we focus on tests that are applicable when the order of integration is not known. The size and power of existing tests are analysed, and a modification to one of the established sets of tests is proposed which offers improved performance in certain circumstances.

Much research has been devoted to assessing the evidence for linear trend in a time series. We discuss the statistical implications of some recent developments, with specific application to 24 time series of relative primary commodities prices.

We show that a standard unit root test that permits an endogenously determined break in level can generate spurious rejections in practically interesting sample sizes when a large break occurs under the null hypothesis. This problem, which occurs for breaks of the innovational outlier type, can be corrected through a simple modification of the test procedure.

In this paper, we concentrate on the case of an exogeneously chosen break date, but entertain the possibility that an incorrect choice is made. In fact, the Perron test statistics considered are invariant to any break in the generating process at the assumed break date. Our results therefore apply equally to the case of a generating process with two breaks, only one of which is specifically accounted for in the analysis. As in Leybourne et al. (1998), we find that a neglected relatively early break can lead to spurious rejections of the unit root null hypothesis. Moreover, for all but one of the tests analyzed, spurious rejections now also arise if a true break occurs relatively soon after the assumed break date.

In the evaluation of economic forecasts, it is frequently the case that comparisons are made between a number of competing predictors. A natural question to ask in such contexts is whether one forecast encompasses its competitors, in the sense that they contain no useful information not present in the superior forecast. We develop tests for this notion of multiple forecast encompassing which are robust to properties expected in the forecast errors, and apply the tests to forecasts of UK growth and inflation. Copyright © 2000 John Wiley & Sons, Ltd.

It has been found useful, as an alternative to a difference-stationary generating model, to consider the possibility of stationarity around a function that permits a smooth transition from one linear trend to another. Previous research has concentrated on functions that are symmetric around the point at which 50% of the transition is completed. Here we permit the possibility of asymmetry in the transition, allowing for example slow entry to and rapid exit from this evolutionary phase. It is shown through simulation and an interesting real data set that this can lead to the detection of further departures from difference stationarity.

We consider tests for the equality of prediction mean squared errors and for forecast encompassing. It is shown that, if forecast errors exhibit ARCH, size distortions are induced in the usual tests. Adjusted test statistics are suggested to alleviate this problem. Copyright © 1999 John Wiley & Sons, Ltd.

The ability of futures markets to predict subsequent spot prices has been a controversial topic for a number of years. Empirical evidence to date is mixed; for any given market, some studies find evidence of efficiency, others of inefficiency. In part, these apparently conflicting findings reflect differences in the time periods analyzed and the methods chosen for testing. A limitation of existing tests is the classification of markets as either efficient or inefficient with no assessment of the degree to which efficiency is present. This article presents tests for unbiasedness and efficiency across a range of commodity and financial futures markets, using a cointegration methodology, and develops a measure of relative efficiency. In general, the findings suggest that spot and futures prices are cointegrated with a slope coefficient that is close to unity, so that the postulated long-run relationship is accepted. However, there is evidence that the long-run relationship does not hold in the short run; specifically, changes in the spot price are explained by lagged differences in spot and futures prices as well as by the basis. This suggests that market inefficiencies exist in the sense that past information can be used by agents to predict spot price movements. A measure of the relative degree of inefficiency (based on forecast error variances) is then used to compare the performance of different markets. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 413–432, 1999

The unit root tests of Phillips and Perron (Testing for a unit root in time series regression. Biometrika 75 (1988), 335–46) are frequently employed in applied econometric research. The chief attraction of these tests is that they are non-parametric, so it is unnecessary to specify a model, even as an approximation to the underlying generating process. The continued popularity of these tests is perhaps surprising since, beginning with Schwert (Tests for unit roots: a Monte Carlo investigation. J. Bus. Econ. Stat. 7 (1989), 147–59), simulation evidence has frequently suggested severe size distortions in specific low persistence generating models. It has been assumed that this size distortion is due to the requirement to estimate short- and long-run variances in practical implementation of the tests, and various alternative estimators have been analysed. However, the analysis of this paper suggests that part of the cause of the size distortion is more fundamental. Even in the ‘idealistic case’ where actual values of the true variances are used, serious size distortions remain for two simple generating models in sample sizes common in economic applications. We explore in detail the sources of this phenomenon for the ARIMA(0, 1, 1) generating model.

Summary Time series analysts have long been concerned with distinguishing stationary "generating processes" from processes for which differencing is required to induce stationarity. In practical applications, this issue is addressed almost invariably through formal hypothesis testing. In this paper, we explore some aspects of the Bayesian approach to the problem, leading to the calculation of posterior odds ratios. Interesting features arise in the simplest possible variant of the problem, where a choice has to be made between a random walk and a stationary first order autoregressive model. We discuss in detail the analysis of this case, and also indicate how our approach extends to the more general comparison of an ARIMA model with a stationary competitor.

It is common practice in time series econometrics to test the null hypothesis that the generating function is integrated—i.e. that a series is stationary only after differencing—against the alternative of stationarity about either a fixed mean or a linear trend. However, there has been considerable recent interest in the possibility of stationarity around a linear trend with an abrupt break. Here we broaden this class of alternatives to allow for a smooth transition from one trend function to another. Dickey–Fuller type tests against this alternative are developed, and their properties are explored.

Nelson and Plosser (1982), in a classic paper, failed to find strong evidence against the null hypothesis of a generating process with a unit autoregressive root for thirteen US macroeconomic time series. Perron (1989) claimed that such evidence was available for a majority of these series if the alternative hypothesis was of trend stationarity with a break in 1929. Zivot and Andrews (1992) treated the break date as endogenous, then finding strong evidence agcainst the null for a minority of these series. Our own analysis extends theirs by permitting a break under the null as well as the alternative hypothesis, and allowing for the sequential nature of the testing. Our empirical findings complete the circle. We find no strong evidence against the unit root hypothesis for any of the thirteen Nelson–Plosser series.

Beveridge and Nelson (A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. J. Monet. Econ. 7 (1981), 151–74) introduced a decomposition into trend plus irregular components for time series generated by models that are integrated of order one. The components are functions of current and past, but not future, values of the series. Therefore, these components can be viewed as estimates available to an agent at the time. Moreover, the decomposition exists whenever the generating process is stationary after first differencing. In this paper we extend the decomposition to generating processes that are integrated of order two, and to the seasonal models of Box and Jenkins (Time Series Analysis, Forecasting and Control. San Francisco:Holden Day, 1970). The analysis leads to the estimation of stochastic growth rates, as well as component series. The methodology is applied to monthly UK industrial production data.

The variance ratio test is often used as a check of the hypothesis that a time series is generated by a random walk. A natural extension of the test is developed to cover the case where the assumed model is ARIMA(p, 1, q), with unknown parameters. Small sample properties of the generalized test are investigated, and the test is applied to a frequently analysed data set on US quarterly real gross national product. In effect, we are testing for low frequency misspecification in assumed autoregressive moving-average (ARMA) models for a differenced series.

This paper develops Lagrange multiplier tests of ARMA(p, q) models against fractional ARIMA(p, d, q) alternatives. The performance of the tests is investigated for moderate-sized samples. It is concluded that fractional difference will be difficult to detect when the orders (p, q) are over-specified in an autoregressive moving-average (ARMA) analysis. The importance of distinguishing between the mean known and mean estimated cases in fractional difference models is illustrated in the context of these tests.

An estimator of the difference parameter in a class of long-memory time series models is examined. It is shown that, in particular circumstances, the estimator can be badly biased, and tests based on it consequently seriously misleading. The source of this bias is identified, and it is shown that its magnitude can readily be predicted through straightforward analytical arguments.

The empirical performance of tests of the Dickey–Fuller type for unit autoregressive roots in the generating model of a time series is studied. In particular, the case where the true generating model structure is unknown and may involve a substantial moving-average component is examined.

In the last few years there has been considerable interest in the accounting literature in time series methods. This paper briefly surveys those areas of accounting in which time series analysis has proved useful and discusses the analytical procedures that have been employed.

This paper examines, with reference to some well known data, possible difficulties in the fitting of simple non-seasonal models to seasonally adjusted time series data.

Since 1978, there has been a significant change in new bond offerings with a substantive increase in the number of nonconvertible high risk bonds. This study uses an n-chotomous multivariate probit model with cash-based funds flow components and financial ratios to predict industrial bond ratings. The n-chotomous probit model provides superior information for evaluating the bond classification process. The model determines the probabilities of a bond being rated in one of three risk classes. The distribution of the probabilities for each predicted bond rating provides a wealth of new information for evaluating the accuracy of the actual rating. New and reclassified bond ratings by Moody's in 1983 provide the information base for the model that is used to predict 1984 ratings. Initially the classification and predictive results were slightly lower than previous studies. A careful analysis of the probability distributions showed that results were close to being correct in over 90 percent of the cases. The analysis found five cash flow components to be significant in predicting the bond ratings of reclassified issues. The significant components were inventories, other current liabilities, dividends, long-term financing, and fixed coverage charges. The likelihood tests indicated that both ratios and funds flow components contributed information that significantly improved the ability of the n-chotomous multivariate probit model to classify new and revised bond ratings. The study provides valuable insight and nuances concerning the bond-rating process.

In this paper we discuss procedures for overcoming some of the problems involved in fitting autoregressive integrated moving average forecasting models to time series data, when the possibility of incorporating an instantaneous power transformation of the data into the analysis is contemplated. The procedures are illustrated using series of quarterly observations on corporate earnings per share.

Previous studies attempting to assess economies of scale in banking firms have largely relied upon cost functions. The profit-function approach enjoys several important advantages over the cost-function method, consequently that is the approach used in this study. A risk-adjusted profit function estimates economies of scale of unit and branch banks; and the effects of bank holding-company affiliation on the level of bank profits is also examined. The results indicate larger economies of scale for branch banks than for unit banks; and bank holding-company affiliates were found to be more efficient than independent banks.

Suppose that a forecasting model is available for the process Xt but that interest centres on the instantaneous transformation Yt = T(Xt). On the assumption that Xt is Gaussian and stationary, or can be reduced to stationarity by differencing, this paper examines the autocovariance structure of and methods for forecasting the transformed series. The development employs the Hermite polynomial expansion, thus allowing results to be derived for a very general class of instantaneous transformations.

In attempting to develop a procedure for fitting linear multiple autoregressive-moving average models to observed data, perhaps the most difficult problem is to achieve a reasonable initial model selection. A recent paper by Jenkins and Alavi suggests, as one possibility, the examination of so-called q-conditioned partial correlations. We show that the sampling properties of these statistics are such as to render them of dubious value for this purpose.