Recent Monthly Research Articles

Forecast performance of noncausal autoregressions and the importance of unit root pretesting

AbstractBased on a large simulation study, this paper investigates which strategy to adopt in order to choose the most accurate forecasting model for mixed causal‐noncausal autoregressions (MAR) data generating processes: always differencing (D), never differencing (L), or unit root pretesting (P). Relying on recent econometric developments regarding forecasting and unit root testing in the MAR framework, the main results suggest that from a practitioner's point of view, the P strategy at the 10% level is a good compromise. In fact, it never departs too much from the best model in terms of forecast accuracy, unlike the L (respectively, D) strategy when the DGP becomes very persistent (respectively, less persistent). This approach is illustrated using recent monthly Brent crude oil price data.

Identifying the influence of climate policy uncertainty and oil prices on modern renewable energies: novel evidence from the United States

Abstract The policy uncertainty surrounding climate change can intensify the urgency of implementing climate policies and influence investment decisions, thereby serving as a catalyst for policy transformation. In this context, given its withdrawal from and subsequent re-commitment to the Paris Agreement, the United States (US) may experience policy uncertainty over climate change. Given this backdrop, it is of paramount importance to explore how the uncertainties caused by shifts in climate policy affect clean energy. This study delves into the impact of climate policy uncertainty and oil prices on modern renewable energy sources, such as solar, wind, geothermal, and biofuels. The most recent monthly data from 1989 to 2023 are used by conducting the Residual Augmented Least Squares (RALS) methodology. The empirical findings show that the uncertainty surrounding climate policy is leading to a shift in the US energy composition, with a focus on solar energy, wind energy, and biofuels. Moreover, the fluctuation in oil prices has a significant effect on the deployment of wind and biofuels, but no discernible effect on solar power. These findings provide significant insights for aligning climate policy in the US with Sustainable Development Goal (SDG) 7, highlighting the crucial importance of encouraging investments in solar energy, wind energy, and biofuels. Graphical abstract

On using ‘Advances in Scientific Knowledge’ to Preempt Deliberate Misinformation from Sceptics with a Personal Agenda

The strong “super El Niño” of 2023 will weaken in the summer of 2024, but that will not undermine the high likelihood that the past 18 months will have anchored a new floor from which new warming will reemerge with what has been remarkable persistence for more than a decade. I write this correspondence as a case study in how to use ‘advances in scientific knowledge’ to preempt claims by personally-motivated deniers, sceptics and opinion-makers. I strong expect that these important players in forming public opinion will assert with faux authenticity that the coming of a La Niña later this fall will be strong evidence that recent monthly record temperatures will turn out to be anomalies in the record from what is otherwise a benign climate.

Cross-domain dynamics: How geopolitical and climate policy uncertainties shape tourism patterns in the United States

Predicting tourism demand is crucial for policymakers and stakeholders in the tourism industry, enabling them to enhance promotional strategies, optimize marketing efforts, align investments, and achieve operational efficiency. While existing research has highlighted the negative impact of global risk factors such as geopolitical risks, economic policy uncertainty, and climate changes on tourism demand, the specific influence of climate policy changes and their differential impact on tourist arrivals has been less explored. Addressing this gap, our study employs bivariate, multiple, and partial wavelet methods to analyze the differential impact of climate policy uncertainty and geopolitical risk on tourism arrivals in the United States. Utilizing recent monthly data from January 1996 to August 2022, we uncover significant time-frequency varying dependencies between these factors and tourism demand. Our causality analysis reveals that both geopolitical risk and climate policy uncertainty are key determinants of tourism demand in the U.S. Furthermore, the Nonlinear Autoregressive Distributed Lag model corroborates the findings of the wavelet analysis, highlighting asymmetrical interactions in the long run. These findings offer valuable insights and have significant implications for policymakers and industry stakeholders, suggesting the need for adaptive and responsive tourism strategies that consider the evolving landscape of geopolitical and climate policy uncertainties.

AN ARDL ANALYSIS ON THE EFFECT OF OIL PRICE INSTABILITY ON FOREIGN RESERVE IN NIGERIA (2015-2022)

The paper employed auto regressive distributive lag (ARDL) model to examine the effect of oil price instability on foreign reserve in Nigeria using recent monthly time series data over the period of 2015-2022. The results of ARDL Bound cointegration indicated a presence of cointegration in the model which confirms an existence of long run relationship between oil price instability and foreign reserve. The findings of ARDL long run analysis show that oil price instability have negative and significant effect on foreign reserve. Likewise, inflation rate has a negative and significant effect on foreign reserve. On the other hand, oil revenue is found to have a positive and significant effect on foreign reserve. Furthermore, the ARDL short run estimates indicate that an existence of a short run relationship between oil prices and foreign reserve. From the findings, the coefficient of error correction term indicates that about 30% of the discrepancy between the actual and the long run or equilibrium value of foreign reserve is corrected or eliminated each month. Furthermore, the results of granger causality test indicate a unidirectional causality between oil price instability and foreign reserve. Based on the empirical findings, the paper suggests the need for the policy makers to adopt policy framework that will get rid of corruption and ensure macroeconomic stability. Policymakers should also take steps to mitigate the negative effects of oil price fluctuations on foreign reserve in Nigeria.

Can German vocational training combat skill shortages in developing countries? Evidence from dual training system in the Philippines

AbstractThis paper examines the labor market impacts of the Philippine Dual Training System (DTS), a vocational training program that combines firm‐based on‐the‐job training (OJT) and conventional institution‐based training, using a recent survey that tracked graduates from DTS and regular institution‐based programs. The estimation results using fuzzy regression discontinuity design show a significantly positive impact on the most recent monthly earnings. The impact significantly increases with the OJT intensity, measured by the number of hours a week in OJT. The above results imply that vocational training has to be aligned with actual production technologies and work practices to narrow skill gaps and create positive returns.

Reagent Lot Variation Assessment: Establishing the Suitable Acceptability Criteria and Implementing an Efficient and Cost-effective Approach

Abstract Introduction/Objective When reagent lot number change for a particular assay it is hoped that the results obtained from the new lot are comparable with those of the old lot and so will not lead to misleading clinical conclusions based on either the reference intervals or previous results on the same patient. This study has criticality evaluated and compared various methods of establishing reagent lot-to-lot verification acceptability criteria to determine the most suitable approach in Sidra Medicine Clinical Biochemistry Department in order to implement an efficient and cost-effective method of verifying new reagent lot numbers. Methods/Case Report Several methods and approach for establishing in-house acceptable criteria for determining reagent variations were critically evaluated. These methods include the use of CLSI EP26-A guidelines, proficiency testing or external quality assessment (EQA) total allowable errors (TeA) and internal quality control (IQC) in-house target SD and CV reflecting the current assay performance. A user friendly statistical template was also produced to be able to improve fhe current workflow, data entry, presentation and documentation of each test with a new reagent lot number approved and implemented for patient testing. Results (if a Case Study enter NA) A full version of Reagent Lot Variation Acceptance Table was produced showing comprehensive established criteria based from methods mentioned above. IQC and patient comparability results are entered in an excel template which automatically calculates the differences and shows if the new lot number passed or failed based on the in-house acceptability criteria established. The reagent lot-to-lot verification template is updated each month using complex excel rules to keep the acceptability limits in accordance with the most current in-house IQC target mean and SD based from the most recent monthly IQC reviews. Conclusion The comparison of different reagent lot variation acceptability criteria indeed raised awareness of the efficiency and practicality of performing reagent lot-to-lot verification. CLSI EP26-A approach was observed to have the least sample number requirement. However, one patient sample for comparison study seems not enough to justify the approval and implementation of the new reagent lot number to consider the assays AMR. The IQC SD and CAP Total Allowable Error (TeA) acceptability criteria are more effective since there is more number of patient samples used for comparability across the assays AMR.

Planning for Maternity Waiting Home Bed Capacity: Lessons from Rural Zambia.

Background:Maternity waiting homes (MWH) allow pregnant women to stay in a residential facility close to a health center while awaiting delivery. This approach can improve health outcomes for women and children. Health planners need to consider many factors in deciding the number of beds needed for an MWH.Objective:The objective of the study is to review experience in Zambia in planning and implementing MWHs, and consider lessons learned in determining optimal capacity.Methods:We conducted a study of 10 newly built MWH in Zambia over 12 months. For this case study analysis, data on beds, service volume, and catchment area population were examined, including women staying at the homes, bed occupancy, and average length of stay. We analyzed bed occupancy by location and health facility catchment area size, and categorized occupancy by month from very low to very high.Findings:Most study sites were rural, with 3 of the 10 study sites rural-remote. Four sites served small catchment areas (<9 000), 3 had medium (9 000–11 000), and 3 had large (>11 000) size populations. Annual occupancy was variable among the sites, ranging from 13% (a medium rural site) to 151% (a large rural-remote site). Occupancy higher than 100% was accommodated by repurposing the MWH postnatal beds and using extra mattresses. Most sites had between 26–69% annual occupancy, but monthly occupancy was highly variable for reasons that seem unrelated to catchment area size, rural or rural-remote location.Conclusion:Planning for MWH capacity is difficult due to high variability. Our analysis suggests planners should try to gather actual recent monthly birth data and estimate capacity using the highest expected utilization months, anticipating that facility-based deliveries may increase with introduction of a MWH. Further research is needed to document and share data on MWH operations, including utilization statistics like number of beds, mattresses, occupancy rates and average length of stay.

MO811: A Closed Loop Audit of CKD-MBD Parameters in Patients Receiving Home Haemodialysis in North West London Centre

Abstract BACKGROUND AND AIMS Hyperphosphatemia is common in end-stage renal disease and increases the risk of fractures, cardiovascular disease and all-cause mortality. Achieving normal phosphate levels is dependent on optimization of dialysis, diet and phosphate medication. Home haemodialysis (HHD) offers exciting opportunities for individualised approaches to management, allowing for a more liberal, lifestyle-centred patient experience. Our aim was to determine, in line with the UK Renal Association 2015 standards for Chronic Kidney Disease—Mineral Bone Disease (CKD-MBD), the percentage of patients in the North West London HHD program with: METHOD We conducted a retrospective review of the six most recent monthly biochemistry results for serum phosphate, corrected calcium and PTH levels of all patients actively receiving HHD under the care of Hammersmith Hospital (n = 33) between January and September 2020. Subsequently, a monthly multidisciplinary review of blood tests (with nephrologists, specialist nurses and dieticians) focusing on CKD-MBD parameters was implemented to facilitate timely prescription changes. Additionally, two questionnaires were conducted: (1) an adapted Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS) questionnaire assessed adherence with phosphate binders and (2) a diet questionnaire assessed dietary phosphate knowledge. At the end of 12 months, a re-audit was conducted in the patients from the original cohort (n = 23; following 4 transplants, 3 in-centre transitions and 3 deaths). RESULTS The initial audit revealed that of the 33 included patients, 14 (42%) recorded all bone parameters within the target range. Examining CKD-MBD parameters individually, 27 (82%) had adjusted calcium within target range [4 (12%) &amp;lt; 2.2 mmol/L and 1 (3%) &amp;gt; 2.6 mmol/L], 22 (66%) had a serum PTH in target range [9 (27%) &amp;gt; 9× ULN and 2 (8%) &amp;lt; 2× ULN, excluding those with parathyroidectomies] and 17 (51%) recorded a median serum phosphate within target [11 (33%) &amp;gt; 1.7 mmol/L and 5 (15%) &amp;lt;1.1 mmol/L]. Table 1 summarizes baseline demographics of the final audit group (n = 23). Re-audit after 12 months revealed 10 (43%) recorded all bone parameters within the target range. Examining CKD-MBD parameters individually, PTH targets had improved (from 66% to 83%). This was associated with increased use of calcimimetics, with 3 (17%) recording PTH &amp;gt; 9× ULN [from 9 (27%) at baseline]. However, there was no significant change in phosphate [12 (54%) within the target range, 7 (29%) above the range] and calcium [18 (78%) within the target range]. Of the 23 patients included for final analysis, 15 (65%) were actively prescribed phosphate binders. The BAASIS questionnaire revealed notable non-adherence, with 8 (53%) admitting to having missed doses in the last month, 5 (33%) consistently missing &amp;gt;4 doses and 6 (40%) not consuming alongside meals. Additionally, the dietary questionnaire revealed a lack of knowledge of hidden sources of dietary phosphate in all surveyed patients (n = 11 responses). CONCLUSION Based on UK Renal Association 2015 standards for CKD-MBD, in patients undergoing HHD:

Loot box engagement: relationships with educational attainment, employment status and earnings in a cohort of 16 000 United Kingdom gamers.

Background and AimsLoot boxes are purchasable randomised rewards in video games that share structural and psychological similarities with gambling. Systematic review evidence has established reproducible associations between loot box purchasing and both problem gambling and problem video gaming. We aimed to measure the association between loot box engagement and socioeconomic correlates.DesignThe study was a cross‐sectional online survey using the recruitment platform, Prolific.SettingUnited Kingdom (UK).ParticipantsA cohort of 16 196 UK adults (18 + years) self‐reporting as video gamers.MeasurementsRespondents were asked about their game‐related purchasing behaviour (including loot boxes), recent monthly spend on loot boxes and gambling engagement (gambling in any form; gambling online; playing ‘social casino’ games). A range of demographic variables were simultaneously captured, including age, sex, ethnicity, earnings, employment and educational attainment.FindingsOverall, 17.16% of gamers in our cohort purchased loot boxes, with a mean self‐reported monthly spend of £29.12. These loot box purchasers are more likely to gamble (45.97% gamble) than people who make other types of game‐related purchases (on aggregate, 28.13% of non‐loot box purchasers gamble), and even greater still than those who do not make any game related purchases (24.38% gamble P < 0.001). Loot box engagement (as binary yes/no or as monthly spend normalised to earnings) was significantly associated with younger age (P < 0.001 and P < 0.001; respectively, for binary yes/no and monthly spend, adjusted for false discovery rate correction), male sex (P < 0.001 and P = 0.025), non‐university educational attainment (P < 0.001 and P < 0.001) and unemployment (P = 0.003 and P = < 0.001). Lower earners spent a higher proportion of monthly earnings on loot boxes (P < 0.001).ConclusionsThe demographic associations of video game loot box engagement (younger age, male sex, non‐university educational attainment and unemployment) mirror those of other addictive and problematic behaviours, including disordered gambling, drug and alcohol misuse.

Modeling Dependent Structure Among Micro-Economics Variables Through COPAR (1)-Model in Pakistan.

A great fluctuations in oil price due to COVID-19 has been observed worldwide. Expertise of complicated relationships among economic indicators has considerable significance for consumers, specialists and strategy producers the same. This exploration work is devoted to investigating the impact of oil price fluctuations due to corona virus pandemic on inflation rate, interest rate and industrial production during lock-down using recent monthly data of Pakistan economic system starting from 2008-01 to 2020-04. At analysis stage, we generally tend to contemplate a novel autoregressive model approach to model non-linear dependence structure amongst a couple of time series. Having gain from the flexibleness of R-vine copulas, the copula autoregression with efficiency investigates the have an impact on of one-time series onto some other: it really is, one-time arrangement normally plays a vital role. Through these qualities of the model, we tend to investigate fuel price effects on industrial production, expansion rate and interest rate in my homeland. One in every of the key finding of this analysis is that there’s a weak tail asymmetry, however some tail dependence, that COPAR-model with efficiency absorbs to account. Furthermore, the fashions monitor lagged reactions of interest rate and industrial production on adjustments in fuel prices inside Pakistan. The oil price result on the inflation rate; on the other hand, is quite rapid.

Local Stock Market Integration with International Gold and Oil Price

We look for the integration of Bangladesh Stock Market with international gold and oil price using most recent monthly data set from January 2003 to December 2020 (2003m1-2020m12). We employ the bounds-testing approach to cointegration between stock market index (DSEX) and international gold and oil price and eventually find an integration and dynamic significant impact of international gold and oil price on DSEX in the long and short-run. We discuss the important policy implications of the dynamic impact of international gold and oil price on stock market index.

The Fama-French Five-Factor Model Plus Momentum: Evidence for the German Market

We implement the Fama-French five-factor model and enhance it with a momentum factor for the German market using recent monthly data from 2002 to 2019. We construct the factors associated with the market, size, value, profitability, investment, and momentum for the CDAX constituents and examine to what extent this six-factor model captures the return premia in the German market. Our preliminary analysis does not document any significant evidence on the profitability or investment premium.The results on the six-factor model compared with the three-factor model reveal that the additional factors do not add significant explanatory power to the analysis. We conclude that the relevance of the profitability and investment factors within the context of international asset pricing studies cannot be transferred to the country- specific case of the German market.

World Economic Prospects Monthly

Overview: Recent economic strength will not last ▀ Recent data confirm that the initial post‐lockdown phase of the recovery has been robust and points to a record‐breaking rise in GDP in Q3. Nonetheless, there are already signs that some sectors are beginning to lose momentum. Given this, and the lack of tangible medical progress towards overcoming the pandemic, we have cut our forecast for global GDP growth in 2021 to 5.4% (from 5.8% seen last month) after an expected 4.4% drop this year. ▀ Recent monthly economic data have surprised to the upside, so we now expect the huge Q2 fall in global GDP to be broadly reversed in Q3. Even so, the level of GDP this quarter will still be over 4% below the Q4 2019 level, a bigger fall than the peak‐to‐trough decline in global GDP during the global financial crisis. ▀ Beyond Q3, growth looks set to slow notably. After initially bouncing strongly, retail sales growth has eased, perhaps even starting to contract. This may partly reflect compositional changed to households spending patterns as some services re‐open. ▀ Even so, our aggregate advanced economy consumer sentiment measure fell for a second month running in August, suggesting some caution among households. This will be exacerbated by the ongoing unwinding of emergency fiscal support measures for households. With banks already expecting to tighten credit standards and some firms beginning to repay some of the money that they borrowed during lockdown, we expect the investment recovery to be quite weak. ▀ Although we still expect average q/q global GDP growth to broadly match that seen during 2010 – the best year since the global financial crisis – we now see GDP expanding by 5.4% in 2021, 0.4pp lower than a month ago. This partly reflects the fact that we now assume a Covid‐19 vaccine will not be in circulation until the middle of next year, about six months later than we had previously assumed, resulting in a more prolonged period of social distancing measures.

Decimal Selection Functions

When we take a decimal form of a number in the unit interval , we can form a new number by selecting certain digits and discarding the others. We analyze the properties of this process, particularly in relationship to a recent Monthly article.

Implementing the Fama-French Five-Factor Model for the German Stock Market

We implement the Fama-French five-factor model for the German market using recent monthly data from 2002 to 2017. We construct the five factors associated with the market, size, value, profitability, and investment for the CDAX constituents and examine to which extent the five-factor model captures the return premia in the German market. The results show that in comparison with the three factor model, the five factor model does not add significant explanatory power to the analysis. We conclude that, the validity of the profitability and investment factors within the context of international asset pricing studies, cannot be transferred to the country specific case of the German market.

Purchasing power parity for the MAVINS

This paper utilizes several tests to examine the PPP theory for MAVINS (Mexico, Australia, Vietnam, Indonesia, Nigeria and South Africa) using recent monthly datasets from January 2003 to May 2016. We test for the stationarity of the variables and found that two of the variables are I(1) while one of the variables is an I(0). Since the variables are a mixture of I(1) and I(0), the paper applied the Mean Group (MG) and the Pooled Mean Group (PMG) ARDL approaches to examine the PPP theory. In choosing between the two estimators, results of the Hausman test indicates that the Mean Group estimator is preferred. Therefore, the results of the Mean Group estimator show that the error correction term which is used to measure the speed of adjustment (from short-run deviation) of the independent variables in converging to long-run equilibrium is negative and significant indicating the presence of cointegration among the variables. This implies that PPP is valid in the long-run in the MAVINS countries. Furthermore, the domestic prices make the nominal exchange rates to appreciate in the long-run and the foreign prices make the nominal exchange rates to appreciate in the short-run. Consequently, appreciation of the nominal exchange rate will cause export to be more expensive, imports cheaper and thereby reducing inflation in the MAVINS based on the data.

Comparison of Intravitreal Aflibercept and Ranibizumab following Initial Treatment with Ranibizumab in Persistent Diabetic Macular Edema.

Purpose To compare the visual and anatomic outcomes in patients with persistent diabetic macular edema (DME) who switched from ranibizumab to aflibercept with those who continued with previous ranibizumab therapy. Methods In this retrospective comparative study, medical records of consecutive patients with center-involved DME ≥ 350 μm who had at least three recent consecutive monthly ranibizumab injections followed by as-needed therapy with either aflibercept or ranibizumab were reviewed. Data were collected at presentation (preinjection), at the intermediary visit, and at the last visit (at the end of the follow-up period). Results Forty-three eyes of 43 patients were divided into two groups: the switch group (n = 20) and the ranibizumab group (n = 23). Though no significant improvement was found in the mean BCVA from the intermediary visit to the last visit, there was a difference in the mean CMT in the switch group and the ranibizumab group (p < 0.001 and p = 0.03, resp.). The mean CMT decreased after the intermediary visit by 188.6 ± 120.5 μm in the switch group and by 60.3 ± 117.1 μm in the ranibizumab group (p = 0.003). Conclusions Both aflibercept and ranibizumab decreased CMT in patients with persistent DME who showed a poor response to ranibizumab injections. However, switching to aflibercept provided only morphologic improvement.

Towards a Universal “Baseline” Characterisation of Air Masses for High- and Low-Altitude Observing Stations Using Radon-222

We demonstrate the ability of atmospheric radon concentrations to reliably and unambiguously identify local and remote terrestrial influences on an air mass, and thereby the potential for alteration of trace gas composition by anthropogenic and biogenic processes. Based on high accuracy (lower limit of detection 10-40 mBq m^(-3)), high temporal resolution (hourly) measurements of atmospheric radon concentration we describe, apply and evaluate a simple two-step method for identifying and characterising constituent mole fractions in baseline air. The technique involves selecting a radon-based threshold concentration to identify the ＂cleanest＂ (least terrestrially influenced) air masses, and then performing an outlier removal step based on the distribution of constituent mole fractions in the identified clean air masses. The efficacy of this baseline selection technique is tested at three contrasting WMO GAW stations: Cape Grim (a coastal low-altitude site), Mauna Loa (a remote high-altitude island site), and Jungfraujoch (a continental high-altitude site). At Cape Grim and Mauna Loa the two-step method is at least as effective as more complicated methods employed to characterise baseline conditions, some involving up to nine steps. While it is demonstrated that Jungfraujoch air masses rarely meet the baseline criteria of the more remote sites, a selection method based on a variable monthly radon threshold is shown to produce credible ＂near baseline＂ characteristics. The seasonal peak-to-peak amplitude of recent monthly baseline CO_2 mole fraction deviations from the long-term trend at Cape Grim, Mauna Loa and Jungfraujoch are estimated to be 1.1, 6.0 and 8.1 ppm, respectively.

Key Determinants of Inflation in Ghana

Inflation, and its deleterious effects on economies, has for long been the worry of governments especially among developing countries including Ghana. Several studies on the Ghanaian economy, have concluded that inflation in Ghana is purely a monetary phenomenon though in reality, the causes of inflation are numerous and vary. The main objective of this paper was to identify the key determinants of inflation in Ghana using the most recent monthly data from January 2000 to December 2014 (data period of 180 months). The study tested whether or not Crude Oil Price at the World Market, Exchange Rate, and Electioneering Spillover Quaternary Effects (ESQE) statistically affect inflation in Ghana either individually or jointly. The study found that crude oil price at the world market, exchange rate, and ESQE are key determinants of inflation in Ghana. The findings indicate a positive relationship between Crude Oil Price at the world market and Inflation, Cedi-Dollar Exchange Rate and Inflation as well as ESQE and Inflation (in the case ESQE, the study considered the first quarter of each post-election year within the data period). Each of the Original Research Article Oppong et al.; BJEMT, 8(3): 200-214, 2015; Article no.BJEMT.2015.111 201 determinants: Crude Oil Price, Exchange Rate and ESQE was statistically significant at 1%. The study also indicated a high R-squared of more than 95% for the joint impact of all three determinants on inflation. This means that jointly, Crude Oil Price, Exchange Rate, and ESQE explain more than 95% of the variation in inflation in Ghana. The paper recommends further study into this subject matter by considering many other potential determinants of inflation in Ghana and the developing world as a whole.