Computationally Efficient Bootstrap Prediction Intervals for Returns and Volatilities in ARCH and GARCH Processes

We propose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box–Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the nonlinear GARCH framework. Our simulation studies indicate that the new re-sampling method provides sharp and well calibrated prediction intervals for both returns and volatilities while reducing computational costs by up to 100 times, compared to other available re-sampling techniques for ARCH/GARCH models. The proposed procedure is illustrated by an application to Yen/U.S. dollar daily exchange rate data.

Bootstrapping prediction intervals for autoregressive models

Methods of improving the coverage of Box–Jenkins prediction intervals for linear autoregressive models are explored. These methods use bootstrap techniques to allow for parameter estimation uncertainty and to reduce the small-sample bias in the estimator of the models’ parameters. In addition, we also consider a method of bias-correcting the non-linear functions of the parameter estimates that are used to generate conditional multi-step predictions.

Bootstrap prediction intervals for autoregressive time series

The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on the bootstrap is considered. Three methods are considered for countering the small-sample bias of least-squares estimation for processes which have roots close to the unit circle: a bootstrap bias-corrected OLS estimator; the use of the Roy–Fuller estimator in place of OLS; and the use of the Andrews–Chen estimator in place of OLS. All three methods of bias correction yield superior results to the bootstrap in the absence of bias correction. Of the three correction methods, the bootstrap prediction intervals based on the Roy–Fuller estimator are generally superior to the other two. The small-sample performance of bootstrap prediction intervals based on the Roy–Fuller estimator are investigated when the order of the AR model is unknown, and has to be determined using an information criterion.

Intervals for the assessment of measurement agreement: Similarities, differences, and consequences of incorrect interpretations

Several intervals have been proposed to quantify the agreement of two methods intended to measure the same quantity in the situation where only one measurement per method and subject is available. The limits of agreement are probably the most well-known among these intervals, which are all based on the differences between the two measurement methods. The different meanings of the intervals are not always properly recognized in applications. However, at least for small-to-moderate sample sizes, the differences will be substantial. This is illustrated both using the width of the intervals and on probabilistic scales related to the definitions of the intervals. In particular, for small-to-moderate sample sizes, it is shown that limits of agreement and prediction intervals should not be used to make statements about the distribution of the differences between the two measurement methods or about a plausible range for all future differences. Care should therefore be taken to ensure the correct choice of the interval for the intended interpretation.

Gestão da cadeia de abastecimento de bens de consumo baseada em modelos de previsão lineares

Dissertação apresentada ao Instituto Politécnico do Porto para obtenção do Grau de Mestre em Logística

Forecasting time series combining Holt-Winters and bootstrap approaches

Publicado em "AIP Conference Proceedings", Vol. 1648

Extent of non-publication in cohorts of studies approved by research ethics committees or included in trial registries

BACKGROUND: The synthesis of published research in systematic reviews is essential when providing evidence to inform clinical and health policy decision-making. However, the validity of systematic reviews is threatened if journal publications represent a biased selection of all studies that have been conducted (dissemination bias). To investigate the extent of dissemination bias we conducted a systematic review that determined the proportion of studies published as peer-reviewed journal articles and investigated factors associated with full publication in cohorts of studies (i) approved by research ethics committees (RECs) or (ii) included in trial registries. METHODS AND FINDINGS: Four bibliographic databases were searched for methodological research projects (MRPs) without limitations for publication year, language or study location. The searches were supplemented by handsearching the references of included MRPs. We estimated the proportion of studies published using prediction intervals (PI) and a random effects meta-analysis. Pooled odds ratios (OR) were used to express associations between study characteristics and journal publication. Seventeen MRPs (23 publications) evaluated cohorts of studies approved by RECs; the proportion of published studies had a PI between 22% and 72% and the weighted pooled proportion when combining estimates would be 46.2% (95% CI 40.2%-52.4%, I2 = 94.4%). Twenty-two MRPs (22 publications) evaluated cohorts of studies included in trial registries; the PI of the proportion published ranged from 13% to 90% and the weighted pooled proportion would be 54.2% (95% CI 42.0%-65.9%, I2 = 98.9%). REC-approved studies with statistically significant results (compared with those without statistically significant results) were more likely to be published (pooled OR 2.8; 95% CI 2.2-3.5). Phase-III trials were also more likely to be published than phase II trials (pooled OR 2.0; 95% CI 1.6-2.5). The probability of publication within two years after study completion ranged from 7% to 30%. CONCLUSIONS: A substantial part of the studies approved by RECs or included in trial registries remains unpublished. Due to the large heterogeneity a prediction of the publication probability for a future study is very uncertain. Non-publication of research is not a random process, e.g., it is associated with the direction of study findings. Our findings suggest that the dissemination of research findings is biased.

Assessing heterogeneity of effects on blood pressure in a meta-analysis of pharmacist interventions trials

Background: Recent reviews of randomized control trials have shown that pharmacist interventions improve cardiovascular diseases (CVD) risk factors in outpatients. Various interventions were evaluated in different settings, and a substantial heterogeneity was observed in the effect estimates. To better express uncertainties in the effect estimates, prediction intervals (PI) have been proposed but are, however, rarely reported. Objective: Pooling data from two systematic reviews, we estimated the effect of pharmacist interventions on systolic blood pressure (BP), computed PI, and evaluated potential causes of heterogeneity. Methods: Data were pooled from systematic reviews assessing the effect of pharmacist interventions on CVD risk factors in patients with or without diabetes, respectively. Effects were estimated using random effect models. Results: Systolic BP was the outcome in 31 trials including 12 373 patients. Pharmacist interventions included patient educational interventions, patient-reminder systems, measurement of BP, medication management and feedback to physician, or educational intervention to health care professionals. Pharmacist interventions were associated with a large reduction in systolic BP (-7.5 mmHg; 95% CI: -9.0 to -5.9). There was a substantial heterogeneity (I2: 66%). The 95% PI ranged from -13.9 to -1.0 mmHg. The effect tended to be larger if the intervention was conducted in a community pharmacy and if the pharmacist intervened at least monthly. Conclusion: On average, the effect of pharmacist interventions on BP was substantial. However, the wide PI suggests that the effect differed between interventions, with some having modest effects and others very large effects on BP. Part of the heterogeneity could be due to differences in the setting and in the frequency of the interventions.

Improving blood pressure control through pharmacist interventions: a meta-analysis of randomized controlled trials.

BACKGROUND: Control of blood pressure (BP) remains a major challenge in primary care. Innovative interventions to improve BP control are therefore needed. By updating and combining data from 2 previous systematic reviews, we assess the effect of pharmacist interventions on BP and identify potential determinants of heterogeneity. METHODS AND RESULTS: Randomized controlled trials (RCTs) assessing the effect of pharmacist interventions on BP among outpatients with or without diabetes were identified from MEDLINE, EMBASE, CINAHL, and CENTRAL databases. Weighted mean differences in BP were estimated using random effect models. Prediction intervals (PI) were computed to better express uncertainties in the effect estimates. Thirty-nine RCTs were included with 14 224 patients. Pharmacist interventions mainly included patient education, feedback to physician, and medication management. Compared with usual care, pharmacist interventions showed greater reduction in systolic BP (-7.6 mm Hg, 95% CI: -9.0 to -6.3; I(2)=67%) and diastolic BP (-3.9 mm Hg, 95% CI: -5.1 to -2.8; I(2)=83%). The 95% PI ranged from -13.9 to -1.4 mm Hg for systolic BP and from -9.9 to +2.0 mm Hg for diastolic BP. The effect tended to be larger if the intervention was led by the pharmacist and was done at least monthly. CONCLUSIONS: Pharmacist interventions - alone or in collaboration with other healthcare professionals - improved BP management. Nevertheless, pharmacist interventions had differential effects on BP, from very large to modest or no effect; and determinants of heterogeneity could not be identified. Determining the most efficient, cost-effective, and least time-consuming intervention should be addressed with further research.

Methods for Estimating Annual Exceedance-Probability Discharges for Streams in Iowa, Based on Data through Water Year 2010, TR-519, 2013

A statewide study was performed to develop regional regression equations for estimating selected annual exceedance- probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedanceprobability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage. Regional regression analysis, using generalized leastsquares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions. Average standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized leastsquares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations. These regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage.

An historical perspective on forecast errors

Using annual observations on industrial production over the last three centuries, and on GDP over a 100-year period, we seek an historical perspective on the forecastability of these UK output measures. The series are dominated by strong upward trends, so we consider various specifications of this, including the local linear trend structural time-series model, which allows the level and slope of the trend to vary. Our results are not unduly sensitive to how the trend in the series is modelled: the average sizes of the forecast errors of all models, and the wide span of prediction intervals, attests to a great deal of uncertainty in the economic environment. It appears that, from an historical perspective, the postwar period has been relatively more forecastable.

Assessing macro uncertainty in real-time when data are subject to revision

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.

Curing lights for orthodontic bonding: a systematic review and meta-analysis

INTRODUCTION Light cure of resin-based adhesives is the mainstay of orthodontic bonding. In recent years, alternatives to conventional halogen lights offering reduced curing time and the potential for lower attachment failure rates have emerged. The relative merits of curing lights in current use, including halogen-based lamps, light-emitting diodes (LEDs), and plasma arc lights, have not been analyzed systematically. In this study, we reviewed randomized controlled trials and controlled clinical trials to assess the risks of attachment failure and bonding time in orthodontic patients in whom brackets were cured with halogen lights, LEDs, or plasma arc systems. METHODS Multiple electronic database searches were undertaken, including MEDLINE, EMBASE, and the Cochrane Oral Health Group's Trials Register, CENTRAL. Language restrictions were not applied. Unpublished literature was searched on ClinicalTrials.gov, the National Research Register, Pro-Quest Dissertation Abstracts, and Thesis database. Search terms included randomized controlled trial, controlled clinical trial, random allocation, double blind method, single blind method, orthodontics, LED, halogen, bond, and bracket. Authors of primary studies were contacted as required, and reference lists of the included studies were screened. RESULTS Randomized controlled trials and clinical controlled trials directly comparing conventional halogen lights, LEDs, or plasma arc systems involving patients with full arch, fixed, or bonded orthodontic appliances (not banded) with follow-up periods of a minimum of 6 months were included. Using predefined forms, 2 authors undertook independent extraction of articles; disagreements were resolved by discussion. The assessment of the risk of bias of the randomized controlled trials was based on the Cochrane Risk of Bias tool. Ten studies met the inclusion criteria; 2 were excluded because of high risk of bias. In the comparison of bond failure risk with halogen lights and plasma arc lights, 1851 brackets were included in both groups. Little statistical heterogeneity was observed in this analysis (I(2) = 4.8%; P = 0.379). There was no statistical difference in bond failure risk between the groups (OR, 0.92; 95% CI, 0.68-1.23; prediction intervals, 0.54, 1.56). Similarly, no statistical difference in bond failure risk was observed in the meta-analysis comparing halogen lights and LEDs (OR, 0.96; 95% CI, 0.64-1.44; prediction intervals, 0.07, 13.32). The pooled estimates from both comparisons were OR, 0.93; 95% CI, 0.74-1.17; and prediction intervals, 0.69, 1.17. CONCLUSIONS There is no evidence to support the use of 1 light cure type over another based on risk of attachment failure.