Forecasting Irish Inflation: A Composite Leading Indicator

This paper presents the results of research into the construction of a composite leading indicator of the Irish rate of inflation, as measured by the annual percentage change in the Consumer Price Index (CPI). It follows the work of Fagan and Fell (1994) who applied the business cycle leading indicator methodology, initially established by Mitchell and Burns (1938,1946), to construct a composite leading indicator of the Irish business cycle.

Bayesian VAR Models for Forecasting Irish Inflation

In this paper we focus on the development of multiple time series models for forecasting Irish Inflation. The Bayesian approach to the estimation of vector autoregressive (VAR) models is employed. This allows the estimated models combine the evidence in the data with any prior information which may also be available. A large selection of inflation indicators are assessed as potential candidates for inclusion in a VAR. The results confirm the significant improvement in forecasting performance which can be obtained by the use of Bayesian techniques. In general, however, forecasts of inflation contain a high degree of uncertainty. The results are also consistent with previous research in the Central Bank of Ireland which stresses a strong role for the exchange rate and foreign prices as a determinant of Irish prices.Bayesian; BVAR; inflation forecasts; Ireland

Inflation Analysis: An Overview

The purpose of this article is to describe how inflation analysis and forecasting has been carried out in the Bank, with particular emphasis on recent research and the new challenges facing the Bank following the launch of the euro on 1 January 1999. Broadly speaking the approach adopted by the Bank over a number of years has been an eclectic one which combines judgement and a range of formal approaches. The latter include structural models which are strongly influenced by basic macroeconomic theories of the small open economy (SOE), indicator analysis, including a composite leading indicator, and time series methods such as autoregressive integrated moving average (ARIMA), vector autoregressive (VAR) and Bayesian VAR (BVAR) models. The emphasis on particular methodologies has evolved over time but in all cases judgement has played a central role.

Forecasting Irish inflation using ARIMA models

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models - the Box Jenkins approach and the objective penalty function methods. The emphasis is on forecast performance which suggests more focus on minimising out-of-sample forecast errors than on maximising in-sample 'goodness of fit'. Thus, the approach followed is unashamedly one of 'model mining' with the aim of optimising forecast performance. Practical issues in ARIMA time series forecasting are illustrated with reference to the harmonised index of consumer prices (HICP) and some of its major sub-components.

Inflation Analysis: An Overview

The purpose of this article is to describe how inflation analysis and forecasting has been carried out in the Bank, with particular emphasis on recent research and the new challenges facing the Bank following the launch of the euro on 1 January 1999. Broadly speaking the approach adopted by the Bank over a number of years has been an eclectic one which combines judgement and a range of formal approaches. The latter include structural models which are strongly influenced by basic macroeconomic theories of the small open economy (SOE), indicator analysis, including a composite leading indicator, and time series methods such as autoregressive integrated moving average (ARIMA), vector autoregressive (VAR) and Bayesian VAR (BVAR) models. The emphasis on particular methodologies has evolved over time but in all cases judgement has played a central role.inflation analysis and forecasting; judgement; small open economy; ARIMA; BVAR

Death in the Lowcountry: The Material Culture of Burial in Hampton County, South Carolina

This research examines the markers of five burial grounds situated in Hampton County, South Carolina from the nineteenth and twentieth centuries. These surveys, in tandem with the history of the region, contribute to the burial scholarship of the Southern United States. Hampton County’s burial landscape offers extended understandings of the culture of death in the South Carolina Lowcountry and the markers of the region offer a rich and varied burial landscape that further understandings of rural peoples in the South

‘Hearts and minds’: association, causation and implication of cognitive impairment in heart failure

The clinical syndrome of heart failure is one of the leading causes of hospitalisation and mortality in older adults. An association between cognitive impairment and heart failure is well described but our understanding of the relationship between the two conditions remains limited. In this review we provide a synthesis of available evidence, focussing on epidemiology, the potential pathogenesis, and treatment implications of cognitive decline in heart failure. Most evidence available relates to heart failure with reduced ejection fraction and the syndromes of chronic cognitive decline or dementia. These conditions are only part of a complex heart failure-cognition paradigm. Associations between cognition and heart failure with preserved ejection fraction and between acute delirium and heart failure also seem evident and where data are available we will discuss these syndromes. Many questions remain unanswered regarding heart failure and cognition. Much of the observational evidence on the association is confounded by study design, comorbidity and insensitive cognitive assessment tools. If a causal link exists, there are several potential pathophysiological explanations. Plausible underlying mechanisms relating to cerebral hypoperfusion or occult cerebrovascular disease have been described and it seems likely that these may coexist and exert synergistic effects. Despite the prevalence of the two conditions, when cognitive impairment coexists with heart failure there is no specific guidance on treatment. Institution of evidence-based heart failure therapies that reduce mortality and hospitalisations seems intuitive and there is no signal that these interventions have an adverse effect on cognition. However, cognitive impairment will present a further barrier to the often complex medication self-management that is required in contemporary heart failure treatment

The importance of science fiction and other STEM-related mass media in young people's decisions to enrol in university STEM courses

This paper presents Australian results from the Interests and Recruitment in Science (IRIS) study with respect to the influence of STEM-related mass media, including science fiction, on students’ decisions to enrol in university STEM courses. The study found that across the full cohort (N=2999), students tended to attribute far greater influence to science-related documentaries/channels such as Life on Earth and the Discovery Channel, etc. than to science-fiction movies or STEM-related TV dramas. Males were more inclined than females to consider science fiction/fantasy books and films and popular science books/magazines as having been important in their decisions. Students taking physics/astronomy tended to rate the importance of science fiction/fantasy books and films higher than students in other courses. The implications of these results for our understanding of influences on STEM enrolments are discussed

Multimodal interventions to enhance adherence to secondary preventive medication after stroke: a systematic review and meta-analyses

Summary: Introduction: Nonadherence to secondary preventative medications after stroke is common and is associated with poor outcomes. Numerous strategies exist to promote adherence. We performed a systematic review and meta-analysis to describe the efficacy of strategies to improve adherence to stroke secondary prevention. Methods: We created a sensitive search strategy and searched multiple electronic databases (MEDLINE, EMBASE, CINAHL, PsycINFO, CENTRAL, and Web of Knowledge) for studies of interventions that aimed to enhance adherence to secondary preventative medication after stroke. We assessed quality of included studies using the Cochrane tool for assessing risk of bias. We performed narrative review and performed meta-analysis where data allowed. Results: From 12,237 titles, we included seventeen studies in our review. Eleven studies were considered to have high risk of bias, 3 with unclear risk, and 3 of low risk. Meta-analysis of available data suggested that these interventions improved adherence to individual medication classes (blood pressure-lowering drugs – OR, 2.21; 95% CI (1.63, 2.98), [P < 0.001], lipid-lowering drugs – OR, 2.11; 95% CI (1.00, 4.46), [P = 0.049], and antithrombotic drugs – OR, 2.32; 95% CI (1.18, 4.56, [P = 0.014]) but did not improve adherence to an overall secondary preventative medication regimen (OR, 1.96; 95% CI (0.50, 7.67), [P = 0.332]). Conclusion: Interventions can lead to improvement in adherence to secondary preventative medication after stroke. However, existing data is limited as several interventions, duration of follow-up, and various definitions were used. These findings need to be interpreted with caution

Are methodological quality and completeness of reporting associated with citation-based measures of publication impact? A secondary analysis of a systematic review of dementia biomarker studies

Objective: To determine whether methodological and reporting quality are associated with surrogate measures of publication impact in the field of dementia biomarker studies. Methods: We assessed dementia biomarker studies included in a previous systematic review in terms of methodological and reporting quality using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) and Standards for Reporting of Diagnostic Accuracy (STARD), respectively. We extracted additional study and journal-related data from each publication to account for factors shown to be associated with impact in previous research. We explored associations between potential determinants and measures of publication impact in univariable and stepwise multivariable linear regression analyses. Outcome measures: We aimed to collect data on four measures of publication impact: two traditional measures—average number of citations per year and 5-year impact factor of the publishing journal and two alternative measures—the Altmetric Attention Score and counts of electronic downloads. Results: The systematic review included 142 studies. Due to limited data, Altmetric Attention Scores and electronic downloads were excluded from the analysis, leaving traditional metrics as the only analysed outcome measures. We found no relationship between QUADAS and traditional metrics. Citation rates were independently associated with 5-year journal impact factor (β=0.42; p<0.001), journal subject area (β=0.39; p<0.001), number of years since publication (β=-0.29; p<0.001) and STARD (β=0.13; p<0.05). Independent determinants of 5-year journal impact factor were citation rates (β=0.45; p<0.001), statement on conflict of interest (β=0.22; p<0.01) and baseline sample size (β=0.15; p<0.05). Conclusions: Citation rates and 5-year journal impact factor appear to measure different dimensions of impact. Citation rates were weakly associated with completeness of reporting, while neither traditional metric was related to methodological rigour. Our results suggest that high publication usage and journal outlet is not a guarantee of quality and readers should critically appraise all papers regardless of presumed impact