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UK top income shares have varied hugely over the past two centuries, ranging from more than 30% to less than 7% of pre‐tax national income allocated to the top 1 percentile. We build a congruent dynamic linear regression model of the top 1% income share allowing for economic, political and social factors. Saturation estimation is used to model outliers and trend breaks, proxying underlying structural changes driving income inequality in the UK. We use the model to forecast the top 1% income share over the last 15 years, and compare to a range of forecast devices. Despite a well‐specified constant parameter model conditioning on significant explanatory variables, the best performing forecasts are obtained from a random walk and a smoothed random walk. These results are explained by the presence of shifts in the income share over the forecast period, resulting in forecasts from equilibrium correction models converging to the wrong equilibrium. Our best prediction for 2026 based on the most recent data from 2021 (a 5‐year ahead projection) is that the pre‐tax top 1% income share will remain at the most recent realized value of 12.7%, but there is a large degree of uncertainty, with a 95% confidence band ranging from 10% to 15.7%. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. David Forbes Hendry received his MSc and PhD from the LSE. [ABSTRACT FROM AUTHOR]

The objective of the paper is to assess the resilience of UK's economy towards two economic shocks: the Covid-19 pandemic that hit the global economy in Q4 2019, in years 2020, 2021 and 2022 and the Brexit following the withdrawal of UK from the European Union on 31 January 2020. To assess the resilience of UK's economy, two sets of forecasts are generated: forecasts using historical data including the pandemic and the Brexit (from Q1 1998 to Q4 2021) and not including the pandemic and the Brexit (from Q1 1998 to Q3 2019). The computation of the difference of their averages is an indicator of the resilience of the economy during the pandemic, the greater the difference the greater the resilience. Eurozone is used as benchmark. By subtracting the average forecasted 2022- 2050 Eurozone quarterly GDP growth rate (annualized) obtained with the Q1 1998-Q4 2021 data, +2.93%, by the one obtained with the Q1 1998-Q3 2019 data, +1.59%, the difference is +1.33%, whereas with UK the difference is -2.33% [-0.24% - (-2.09%)]. Thus, Eurozone shows a greater resilience (+1.33%) than the UK (-2.33%) based on 2022-2050 forecasts. In addition, the authors pointed out that the average of the 2022-2050 quarterly (annualized) growth rate forecasts of the Eurozone is expected to be +2.93% with the 1998-2021 data whereas it is expected to be only -2.09% forUK The Eurozone economy shows better prospects than the UK economy. [ABSTRACT FROM AUTHOR]

For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-series, using current observations to generate forecasts. This necessitates a real-time data stream and enough historical observations at every location for these methods to be deployed. In this paper, we propose the use of Global methods to train generalised models. Using data from 20 locations distributed throughout the UK, we show that it is possible to learn models without access to data for all locations, enabling them to generate forecasts for unseen locations. We show a single Global model trained on multiple locations can produce more consistent and accurate results across locations. Furthermore, by leveraging weather observations and measurements from other locations we show it is possible to create models capable of accurately forecasting irradiance at locations without any real-time data. We apply our approaches to both classical and state-of-the-art Machine Learning methods, including a Transformer architecture. We compare models using satellite imagery or point observations (temperature, pressure, etc.) as weather data. These methods could facilitate planning and optimisation for both newly deployed solar farms and domestic installations from the moment they come online. [ABSTRACT FROM AUTHOR]

Previous research has suggested that older trans and gender non-conforming (TGNC) people may face particular challenges related to stigma, social exclusion and discrimination in later life. However, direct data on social support and needs in older TGNC population both internationally and within the UK is limited due to the small, dispersed nature of this population, and the absence of specific data collection on aging TGNC populations. During the UK COVID-19 lockdown in summer 2020, older people and those with long-term health conditions were advised to adopt particular precautions. This study aimed to explore older TGNC people's social support networks, key concerns and unmet needs during the COVID-19 'lockdown'. A UK cross-sectional survey of LGBT + people aged 60+ (n = 375) was undertaken during the lockdown. This paper analyses responses from the subset (n = 38) of TGNC participants. The majority of TGNC respondents described diverse social networks, often centered around friends and non-kin social networks, although partners and adult children were also significant for some. In most cases, those with existing strong networks continued to maintain social connections during lockdown, albeit with some regrets about loss of activities and face-to-face connection. However, a minority of respondents had experienced greater challenges prior to lockdown, and may have been at increased vulnerability during the pandemic, for example indicating that they had no-one to call on for practical support in an emergency. When asked about unmet needs and challenges, social isolation was repeatedly raised as the most frequent concern. Several respondents also mentioned issues specifically affecting TGNC communities, including access to gender affirming care and a perceived rise in social intolerance. Health and social care providers should be aware of the diversity of support networks within TGNC communities. There may also be benefits in community sector interventions to help older TGNC build and maintain strong social networks. [ABSTRACT FROM AUTHOR]

The 100th anniversary of the Liverpool Tidal Institute (LTI) was celebrated during 2019. One aspect of tidal science for which the LTI acquired a worldwide reputation was the development and use of tide prediction machines (TPMs). The TPM was invented in the late 19th century, but most of them were made in the first half of the 20th century, up until the time that the advent of digital computers consigned them to museums. This paper describes the basic principles of a TPM, reviews how many were constructed around the world and discusses the method devised by Arthur Doodson at the LTI for the determination of harmonic tidal constants from tide gauge data. These constants were required in order to set up the TPMs for predicting the heights and times of the tides. Although only 3 of the 30-odd TPMs constructed were employed in operational tidal prediction at the LTI, Doodson was responsible for the design and oversight of the manufacture of several others. The paper demonstrates how the UK, and the LTI and Doodson in particular, played a central role in this area of tidal science. [ABSTRACT FROM AUTHOR]

Smart City public services need detailed and relevant public information to increase their efficiency. To have relevant information, collecting and processing the data about its previous uses are crucial. Clustering is one of the most powerful, yet computationally demanding, tools that can be used to process such information. Since public services data are vast, but usually not accurate, the objects clustered are considered as uncertain. In this paper, we propose a novel clustering method for uncertain objects called Improved Bisector Pruning (IBP), which uses bisectors to reduce the number of computations. We combine IBP with a modified segmentation of a data set area (SDSA) method that enables the parallelization of the clustering process. In the experiments, we show that IBP-SDSA is superior in performance to the most used clustering method UK-means combined with Voronoi or MinMax pruning, regardless of the problem size. We applied IBP-SDSA on clustering the public services data in the city of Osijek and show that the acquired data can be used to improve public services logistics. [ABSTRACT FROM AUTHOR]

Background: Around 8% of the UK Armed Forces leave in any given year, and must navigate unfamiliar civilian systems to acquire employment, healthcare, and other necessities. This paper determines longer-term prevalences of mental ill health and socioeconomic outcomes in UK Service leavers, and how they are related to demographic factors, military history, and pre-enlistment adversity. Methods: This study utilised data from a longitudinal sample of a cohort study UK Armed Forces personnel since 2003. A range of self-reported military and sociodemographic factors were analysed as predictors of probable Post-Traumatic Stress Disorder, common mental disorders, alcohol misuse, unemployment and financial hardship. Prevalences and odds ratios of associations between predictors and outcomes were estimated for regular veterans in this cohort. Results: Veteran hardship was mostly associated with factors linked to socio-economic status: age, education, and childhood adversity. Few military-specific factors predicted mental health or socio-economic hardship, except method of leaving (where those leaving due to medical or unplanned discharge were more likely to encounter most forms of hardship as veterans), and rank which is itself related to socioeconomic status. Conclusion: Transition and resettlement provisions become increasingly generous with longer service, yet this paper shows the need for those services becomes progressively less necessary as personnel acquire seniority and skills, and instead could be best targeted at unplanned leavers, taking socioeconomic status into consideration. Many will agree that longer service should be more rewarded, but the opposite is true if provision instead reflects need rather than length of service. This is a social, political and ethical dilemma. [ABSTRACT FROM AUTHOR]

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving. [ABSTRACT FROM AUTHOR]

This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting. [ABSTRACT FROM AUTHOR]

The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ℓ1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ℓ1/ℓ2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods. Author summary: With the advent and evolution of large-scale and comprehensive biobanks, there come up unprecedented opportunities for researchers to further uncover the complex landscape of human genetics. One major direction that attracts long-standing interest is the investigation of the relationships between genotypes and phenotypes. This includes but doesn't limit to the identification of genotypes that are significantly associated with the phenotypes, and the prediction of phenotypic values based on the genotypic information. Genome-wide association studies (GWAS) is a very powerful and widely used framework for the former task, having produced a number of very impactful discoveries. However, when it comes to the latter, its performance is fairly limited by the univariate nature. To address this, multiple regression methods have been suggested to fill in the gap. That said, challenges emerge as the dimension and the size of datasets both become large nowadays. In this paper, we present a novel computational framework that enables us to solve efficiently the entire lasso or elastic-net solution path on large-scale and ultrahigh-dimensional data, and therefore make simultaneous variable selection and prediction. Our approach can build on any existing lasso solver for small or moderate-sized problems, scale it up to a big-data solution, and incorporate other extensions easily. We provide a package snpnet that extends the glmnet package in R and optimizes for large phenotype-genotype data. On the UK Biobank, we observe competitive prediction performance of the lasso and the elastic-net for all four phenotypes considered from the UK Biobank. That said, the scope of our approach goes beyond genetic studies. It can be applied to general sparse regression problems and build scalable solution for a variety of distribution families based on existing solvers. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the emergence of a growing literature bemoaning the level of quantitative methods provision within the U.K. Higher Education sector, noting its negative impact upon the subsequent skills of graduates and their preparedness for the workplace. The present paper documents and evaluates an attempt to counter these issues via the introduction of an increasing element of flexible learning on a business and financial forecasting module. Using a mixture of empirical methods, it is shown that flexible learning results in improvements in student performance and ability across a range of metrics. It is argued that 'broad' forms of flexible learning can be employed to overcome the concerns of an increasingly negative literature on quantitative methods provision and the subsequent skills levels of students. [ABSTRACT FROM AUTHOR]

Flooding in 2021 has highlighted the increased risk to national resilience. This is against a backdrop of the UK climate projected to become more extreme over the next few decades. This paper considers the notable river and surface water flood events within England and Wales during 2021 and examines the performance of the Flood Forecasting Centre in highlighting the flood risk to our customers. We reflect on team debriefs as well as feedback and surveys from our customers. We distil our learnings and make connections with improvements in our underpinning science, forecasting tools, products and services. Finally, we highlight challenges associated with surface water flooding and suggest how we may collectively start to overcome these. [ABSTRACT FROM AUTHOR]

Extending the use of the p–y curves included in the regulation codes API and DNV to design large-diameter monopiles supporting offshore wind turbines (OWTs) was unsuccessful as it resulted in an inaccurate estimation of the monopile behavior. This had prompted many investigators to propose formulations to enhance the performances of Winkler model. In this paper, two case studies are considered. A case consisting of an OWT at Horns Rev (Denmark) supported by a monopile in a sandy soil was studied first. Taking the FEA using ABAQUS as reference, results of WILDOWER 1.0 (a Winkler computer code) using the recently proposed p–y curves giving design parameters were plotted and evaluated. In order to see the ability of proposed p–y curves to predict the monopile head movements, and consequently the first natural frequency (1st NF), a second case study consisting of a monopile supporting an OWT at North Hoyle (UK) was selected. The monopile head stiffness in terms of lateral, rocking, and cross-coupling stiffness coefficients, necessary for the 1st NF, were computed using both ABAQUS and WILDPOWER 1.0. Comparisons with the measured 1st NF showed that with the exception of one p–y model, none of other proposed Winkler methods is able to predict accurately this parameter. [ABSTRACT FROM AUTHOR]

This paper shows how existing methods of time series analysis and modeling can be exploited in novel ways to monitor and forecast the COVID-19 epidemic. In the past, epidemics have been monitored by various statistical and model metrics, such as evaluation of the effective reproduction number, R (t). However, R (t) can be difficult and time consuming to compute. This paper suggests two relatively simple data-based metrics that could be used in conjunction with R (t) estimation and provide rapid indicators of how the epidemic's dynamic behavior is progressing. The new metrics are the epidemic rate of change (RC) and a related state-dependent response rate parameter (RP), recursive estimates of which are obtained from dynamic harmonic and dynamic linear regression (DHR and DLR) algorithms. Their effectiveness is illustrated by the analysis of COVID-19 data in the UK and Italy. The paper also shows how similar methodology, combined with the refined instrumental variable method for estimating hybrid Box–Jenkins models of linear dynamic systems (RIVC), can be used to relate the daily death numbers in the Italian and UK epidemics and then provide 15-day-ahead forecasts of the UK daily death numbers. The same approach can be used to model and forecast the UK epidemic based on the daily number of COVID-19 patients in UK hospitals. Finally, the paper speculates on how the state-dependent parameter (SDP) modeling procedures may provide data-based insight into a nonlinear differential equation model for epidemics such as COVID-19. [ABSTRACT FROM AUTHOR]

Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety. [ABSTRACT FROM AUTHOR]

Genome-Wide Association Studies (GWAS) explain only a small fraction of heritability for most complex human phenotypes. Genomic heritability estimates the variance explained by the SNPs on the whole genome using mixed models and accounts for the many small contributions of SNPs in the explanation of a phenotype. This paper approaches heritability from a machine learning perspective, and examines the close link between mixed models and ridge regression. Our contribution is two-fold. First, we propose estimating genomic heritability using a predictive approach via ridge regression and Generalized Cross Validation (GCV). We show that this is consistent with classical mixed model based estimation. Second, we derive simple formulae that express prediction accuracy as a function of the ratio n p , where n is the population size and p the total number of SNPs. These formulae clearly show that a high heritability does not imply an accurate prediction when p > n. Both the estimation of heritability via GCV and the prediction accuracy formulae are validated using simulated data and real data from UK Biobank. [ABSTRACT FROM AUTHOR]

Purpose: In the current low-interest market environment, more and more asset managers have started to consider to invest in property markets. To implement adequate and forward-looking risk management procedures, this market should be analyzed in more detail. Therefore, this study aims to examine the housing market data from the UK. More specifically, sentiment data and house prices are examined, using techniques of time-series econometrics suggested by Toda and Yamamoto (1995). The monthly data used in this study is the RICS Housing Market Survey and the Nationwide House Price Index – covering the period from January 2000 to December 2018. Furthermore, the authors also analyze the stability of the implemented Granger causality tests. In sum, the authors found clear empirical evidence for unidirectional Granger causality from sentiment indicator to the house prices index. Consequently, the sentiment indicator can help to forecast property prices in the UK. Design/methodology/approach: By investigating sentiment data for house prices using techniques of time-series econometrics (more specifically the procedure suggested by Toda and Yamamoto, 1995), the research question whether sentiment indicators can be helpful to predict property prices in the UK is analyzed empirically. Findings: The empirical results show that the RICS Housing Market Survey can help to predict the house prices in the UK. Practical implications: Given these findings, the information provided by property market sentiment indicators certainly should be used in a forward-looking early warning system for house prices in the UK. Originality/value: To authors' knowledge, this is the first paper that uses the procedure suggested by Toda and Yamaoto to search for suitable early warning indicators for investors in UK real estate assets. [ABSTRACT FROM AUTHOR]

Renewable energy has become popular compared with traditional energy like coal. The relative demand for renewable energy compared to traditional energy is an important index to determine the energy supply structure. Forecasting the relative demand index has become quite essential. Data mining methods like decision trees are quite effective in such time series forecasting, but theory behind them is rarely discussed in research. In this paper, some theories are explored about decision trees including the behavior of bias, variance, and squared prediction error using trees and the prediction interval analysis. After that, real UK grid data are used in interval forecasting application. In the renewable energy ratio forecasting application, the ratio of renewable energy supply over that of traditional energy can be dynamically forecasted with an interval coverage accuracy higher than 80% and a small width around 22, which is similar to its standard deviation. [ABSTRACT FROM AUTHOR]

Problem: Do conservative econometric models that comply with the Golden Rule of Forecasting provide more accurate forecasts?, Methods: To test the effects of forecast accuracy, we applied three evidence-based guidelines to 19 published regression models used for forecasting 154 elections in Australia, Canada, Italy, Japan, Netherlands, Portugal, Spain, Turkey, U.K., and the U.S. The guidelines direct forecasters using causal models to be conservative to account for uncertainty by (I) modifying effect estimates to reflect uncertainty either by damping coefficients towards no effect or equalizing coefficients, (II) combining forecasts from diverse models, and (III) incorporating more knowledge by including more variables with known important effects., Findings: Modifying the econometric models to make them more conservative reduced forecast errors compared to forecasts from the original models: (I) Damping coefficients by 10% reduced error by 2% on average, although further damping generally harmed accuracy; modifying coefficients by equalizing coefficients consistently reduced errors with average error reductions between 2% and 8% depending on the level of equalizing. Averaging the original regression model forecast with an equal-weights model forecast reduced error by 7%. (II) Combining forecasts from two Australian models and from eight U.S. models reduced error by 14% and 36%, respectively. (III) Using more knowledge by including all six unique variables from the Australian models and all 24 unique variables from the U.S. models in equal-weight "knowledge models" reduced error by 10% and 43%, respectively., Originality: This paper provides the first test of applying guidelines for conservative forecasting to established election forecasting models., Usefulness: Election forecasters can substantially improve the accuracy of forecasts from econometric models by following simple guidelines for conservative forecasting. Decision-makers can make better decisions when they are provided with models that are more realistic and forecasts that are more accurate., Competing Interests: The authors have declared that no competing interests exist.

Sifnioti, D.E.; Dolphin, T.J., and Vincent, C.E., 2020. Performance of hindcast wave model data used in UK coastal waters. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1284–1290. Coconut Creek (Florida), ISSN 0749-0208. Long-term wave data are a necessity for various marine applications, but wave observations can be temporally and spatially sparse, are often absent in the required locations and can be costly and often cannot be obtained in a short notice. The models used are the UK's Met Office's European 38-year wave hindcast "ReMap", which has spatial resolution of 8 km and temporal step of 3 hours, and ECMWF's (European Centre for Medium-Range Weather Forecasts) "ERA5" reanalysis dataset from 1979 to present with a spatial resolution of 31 km and a temporal step of 1 hour. Both datasets include wave and wind parameters, thus providing valuable long-term data that can be used in coastal regions where wave observations are unavailable. In this paper we assess the errors in performance of the ReMap and ERA5 data by comparing these with wave observations made at two coastal sites in the southern North Sea. ReMap and ERA5 showed a strong correlation with measured waves for significant wave height (Hs) (r = 0.84 - 0.9), fair correlation for peak wave period (Tp) (r = 0.58 - 0.68) and good correlation for mean zero up-crossing wave period (Tz) (r = 0.73 – 0.79), with different error statistics per location and per dataset. Analysis of the 90th percentile shows that the ReMap and ERA5 overestimated Hs by approximately 0.5 m, and by up to 1.2 s for Tp and 0.6s for Tz. Monthly and annual means are also presented and discussed. Overall the performances of ReMap and ERA5 against the measured waves were similar. It is concluded that the ReMap and ERA5 data can be used with reasonable confidence in coastal regions where no direct measurements are available for 'average' conditions, but caution needs to be taken for extreme conditions as an over prediction for extremes is likely. [ABSTRACT FROM AUTHOR]

Currently available risk prediction methods are limited in their ability to deal with complex, heterogeneous, and longitudinal data such as that available in primary care records, or in their ability to deal with multiple competing risks. This paper develops a novel deep learning approach that is able to successfully address current limitations of standard statistical approaches such as landmarking and joint modeling. Our approach, which we call Dynamic-DeepHit, flexibly incorporates the available longitudinal data comprising various repeated measurements (rather than only the last available measurements) in order to issue dynamically updated survival predictions for one or multiple competing risk(s). Dynamic-DeepHit learns the time-to-event distributions without the need to make any assumptions about the underlying stochastic models for the longitudinal and the time-to-event processes. Thus, unlike existing works in statistics, our method is able to learn data-driven associations between the longitudinal data and the various associated risks without underlying model specifications. We demonstrate the power of our approach by applying it to a real-world longitudinal dataset from the U.K. Cystic Fibrosis Registry, which includes a heterogeneous cohort of 5883 adult patients with annual follow-ups between 2009 to 2015. The results show that Dynamic-DeepHit provides a drastic improvement in discriminating individual risks of different forms of failures due to cystic fibrosis. Furthermore, our analysis utilizes post-processing statistics that provide clinical insight by measuring the influence of each covariate on risk predictions and the temporal importance of longitudinal measurements, thereby enabling us to identify covariates that are influential for different competing risks. [ABSTRACT FROM AUTHOR]

This paper analyses both quarterly data from the Confederation of British Industry (CBI) Survey on respondents’ expectations of recent and forthcoming manufacturing output and monthly Office of National Statistics (ONS) figures on actual manufacturing output within the UK. Quarterly output expectations of the CBI manufacturers are explained from the monthly ONS observations using a bounded rationality approach. The logistic formulation models the diffusion process across respondents. There is a backward-looking CBI Survey perspective, explained by past ONS observations, and a forward-looking perspective, explained from future ONS statistics. Also, the forecasting of monthly manufacturing output from earlier values, along with the quarterly CBI Survey information, is examined and tested against the alternative Pesaran/Thomas method. The study provides econometric evidence for the validity of the logistic model and shows that bounded rationality can explain the formation of predictions among business managers in the UK manufacturing sector. The emerging consensus from the literature, supported by this paper, is that the logistic format is a superior approximation to the true data generating process compared with the earlier standard Anderson/Pesaran/Thomas approach. An adjustment to the Survey is used, which achieves perfect symmetry with up and down versions of the data. The benefits of this adjustment are tested in the forecasting section. [ABSTRACT FROM AUTHOR]

Electricity demand forecasts are needed for decisions regarding generation dispatch for lead times as short as just a few minutes. Imbalance between generation and demand causes deviation of the system frequency from its target, which in Great Britain is 50 Hz. This, in turn, causes a change in demand, due largely to motor loads. For Great Britain, the change is estimated to be 2.5% of demand per 1 Hz of frequency deviation from its target. This can be used to calculate the demand that would have occurred if frequency had been at 50 Hz. Modeling and forecasting the resulting frequency-corrected demand provides a better basis for dispatching generation. This paper evaluates methods for forecasting frequency-corrected demand up to 10 min ahead. We introduce an exponential smoothing model that, like the system operator's proposed Kalman filter approach, jointly models frequency and demand. We also evaluate a set of univariate methods applied directly to the series of frequency-corrected demand. These methods have not previously been considered for lead times less than 10 min. In our empirical analysis, the best results were produced by a seasonal exponential smoothing method applied directly to the series of frequency-corrected demand. [ABSTRACT FROM AUTHOR]

This paper investigates the historical trends in economic development through the impact of economic depressions and emissions of greenhouse gasses, namely carbon dioxide (CO2). The analysis includes four countries: the United States, the United Kingdom, Germany and Japan. The focus, therefore, will be on the impact of two economic crises and their effect on global warming. Temperature changes in the longer period are very often regarded as a result of human activity, which can be measured by the increase of GDP (per capita). The findings indicate that GDP (per capita) parameters cannot be considered as correct measures of human pollution activity. The results show that the long-run temperature can be evaluated with the help of annual average temperatures of the previous four years. The proposed model does not only provide quite satisfactory forecasts, but is very stable with coefficients variables that can make a model more reliable for practice. [ABSTRACT FROM AUTHOR]

In this paper, we propose a model for forecasting Value-at-Risk (VaR) using a Bayesian Markov-switching GJR-GARCH(1,1) model with skewed Student's-t innovation, copula functions and extreme value theory. A Bayesian Markov-switching GJR-GARCH(1,1) model that identifies non-constant volatility over time and allows the GARCH parameters to vary over time following a Markov process, is combined with copula functions and EVT to formulate the Bayesian Markov-switching GJR-GARCH(1,1) copula-EVT VaR model, which is then used to forecast the level of risk on financial asset returns. We further propose a new method for threshold selection in EVT analysis, which we term the hybrid method. Empirical and back-testing results show that the proposed VaR models capture VaR reasonably well in periods of calm and in periods of crisis., Competing Interests: The authors have declared that no competing interests exist.

In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes. Individual characteristics tend to over-perform fMRI-based metrics in the prediction of behavioral measures. However, the combination of both metrics can increase predictive accuracy, suggesting that fMRI-based metrics can successfully complement individual characteristics, mainly in large datasets. [ABSTRACT FROM AUTHOR]

Background: The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction. Methods: Using the Salford Kidney Study biobank, a UK-based prospective cohort of over 3000 non-dialysis CKD patients, 433 patients met our inclusion criteria: a minimum of four eGFR measurements over a two-year period and a linear eGFR trajectory. Plasma samples were obtained and analysed for novel proteomic signals using SWATH-Mass-Spectrometry. The 4-variable UK-calibrated KFRE was calculated for each patient based on their baseline clinical characteristics. Boruta machine learning algorithm was used for the selection of proteins most contributing to differentiation between patient groups. Logistic regression was employed for estimation of ESRD prediction by (1) proteomic features; (2) KFRE; and (3) proteomic features alongside KFRE. Results: SWATH maps with 943 quantified proteins were generated and investigated in tandem with available clinical data to identify potential progression biomarkers. We identified a set of proteins (SPTA1, MYL6 and C6) that, when used alongside the 4-variable UK-KFRE, improved the prediction of 5-year risk of ESRD (AUC = 0.75 vs AUC = 0.70). Functional enrichment analysis revealed Rho GTPases and regulation of the actin cytoskeleton pathways to be statistically significant, inferring their role in kidney function and the pathogenesis of renal disease. Conclusions: Proteins SPTA1, MYL6 and C6, when used alongside the 4-variable UK-KFRE achieve an improved performance when predicting a 5-year risk of ESRD. Specific pathways implicated in the pathogenesis of podocyte dysfunction were also identified, which could serve as potential therapeutic targets. The findings of our study carry implications for comprehending the involvement of the Rho family GTPases in the pathophysiology of kidney disease, advancing our understanding of the proteomic factors influencing susceptibility to renal damage. [ABSTRACT FROM AUTHOR]

The electricity industry is significantly affected by weather conditions both in terms of the operation of the network infrastructure and electricity consumption. Following privatization and deregulation, the electricity industry in the U.K. has become fragmented and central planning has largely disappeared. In order to maximize profits, the margin of supply has decreased and the network is being run closer to capacity in certain areas. Careful planning is required to manage future electricity demand within the framework of this leaner electricity network. There is evidence that the climate in the U.K. is changing with a possible 3°C average annual temperature increase by 2080. This paper investigates the impact of weather variables on monthly electricity demand in England and Wales. A multiple regression model is developed to forecast monthly electricity demand based on weather variables, gross domestic product, and population growth. The average mean absolute percentage error (MAPE) for the worst model is approximately 2.60% in fitting the monthly electricity demand from 1989 to 1995 and approximately 2.69% in the forecasting over the period 1996 to 2003. This error may reflect the nonlinear dependence of demand on temperature at the hot and cold temperature extremes; however, the inclusion of degree days, enthalpy latent days, and relative humidity in the model improves the demand forecast during the summer months. [ABSTRACT FROM AUTHOR]

Background: Parents could be important influences on child physical activity and parents are often encouraged to be more active with their child. This paper examined the association between parent and child physical activity and sedentary time in a UK cohort of children assessed when the children were in Year 1 (5-6 years old) and in Year 4 (8-9 years old). Methods: One thousand two hundred twenty three children and parents provided data in Year 4 and of these 685 participated in Year 1. Children and parents wore an accelerometer for five days including a weekend. Mean minutes of sedentary time and moderate-to-vigorous intensity physical activity (MVPA) were derived. Multiple imputation was used to impute all missing data and create complete datasets. Linear regression models examined whether parent MVPA and sedentary time at Year 4 and at Year 1 predicted child MVPA and sedentary time at Year 4. Change in parent MVPA and sedentary time was used to predict change in child MVPA and sedentary time between Year 1 and Year 4. Results: Imputed data showed that at Year 4, female parent sedentary time was associated with child sedentary time (0.13, 95% CI = 0.00 to 0.27 mins/day), with a similar association for male parents (0.15, 95% CI = −0.02 to 0.32 mins/day). Female parent and child MVPA at Year 4 were associated (0.16, 95% CI = 0.08 to 0.23 mins/day) with a smaller association for male parents (0.08, 95% CI = −0.01 to 0.17 mins/day). There was little evidence that either male or female parent MVPA at Year 1 predicted child MVPA at Year 4 with similar associations for sedentary time. There was little evidence that change in parent MVPA or sedentary time predicted change in child MVPA or sedentary time respectively. Conclusions: Parents who were more physically active when their child was 8-9 years old had a child who was more active, but the magnitude of association was generally small. There was little evidence that parental activity from three years earlier predicted child activity at age 8-9, or that change in parent activity predicted change in child activity. [ABSTRACT FROM AUTHOR]

Power distribution networks are increasingly challenged by ageing plant, environmental extremes and previously unforeseen operational factors. The combination of high loading and weather conditions is responsible for large numbers of recurring faults in legacy plants which have an impact on service quality. Owing to their scale and dispersed nature, it is prohibitively expensive to intensively monitor distribution networks to capture the electrical context these disruptions occur in, making it difficult to forestall recurring faults. In this paper, localised weather data are shown to support fault prediction on distribution networks. Operational data are temporally aligned with meteorological observations to identify recurring fault causes with the potentially complex relation between them learned from historical fault records. Five years of data from a UK Distribution Network Operator is used to demonstrate the approach at both HV and LV distribution network levels with results showing the ability to predict the occurrence of a weather related fault at a given substation considering only meteorological observations. Unifying a diverse range of previously identified fault relations in a single ensemble model and accompanying the predicted network conditions with an uncertainty measure would allow a network operator to manage their network more effectively in the long term and take evasive action for imminent events over shorter timescales. [ABSTRACT FROM AUTHOR]

High-frequency financial data is more difficult to predict than low-frequency data because it possesses nonlinearity, nonstationarity, higher volatility, and long memory and is frequently accompanied by the jump phenomena. In this paper, the nonparametric regression (NR) model based on kernel function is used to fit the nonlinear relationship between the nonstationary series Y t and its lagging series to model the trend of high frequency financial time series. Furthermore, the deep learning LSTM (long short-term memory) model is applied to capture the high volatility and frequent jumps of high frequency financial data and to improve the forecasting accuracy of the residual series. The results demonstrate that the hybrid NR and LSTM model has greatly improved the forecasting accuracy in several evaluation criteria. In comparison to NR, support vector machine (SVM), LSTM, ARIMA and NR-SVM models, the mean absolute error (MAE) of NR-LSTM has reduced by 89.78%, 97.85%, 86.48%, 32.47% and 89%, respectively. In addition, we have constructed the trading strategy for the Shanghai-Shenzhen 300 index by using the NR-LSTM model. The NR-LSTM model can continue to provide good returns even during a bear market, which can serve as a guide for investors. Furthermore, the NR-LSTM model also exhibits the best forecasting effect when we model the high-frequency data of Ping An bank in China, the FTSE 100 index in the UK, and the S&P 500 index in the US. [ABSTRACT FROM AUTHOR]

BACKGROUND: For commonly used mortality models, the existing estimates change with the recalibration of new data. This issue is also known as the lack of the new-data-invariant property. OBJECTIVE: We adapt the Lee-Carter, age-period-cohort, Renshaw-Haberman, and Li-Lee models to achieve the new-data-invariant property. The resulting fitted or forecast mortality indexes are tractable and comparable when more recent data are modelled. METHODS: Illustrated by mortality rates of the England andWales populations, we explore the tradeoff between goodness of fit and the new-data-invariant property. Using the adapted model and vector autoregressive framework, we explore the interdependencies of subregional mortality dynamics in the United Kingdom. RESULTS: To compare the goodness of fit, we consider the four adapted models and the Cairns-Blake-Dowd model, which are invariant to new data without adaptation. The Renshaw- Haberman model is demonstrated to be the best-performing model. The in-sample and backtesting results show that the proposed adaptation introduces only a small cost of reduced model fitting, which is robust across sensitivity analyses. CONCLUSIONS: The adapted Renshaw-Haberman model is recommended to construct tractable mortality indexes. CONTRIBUTION: From a methodological perspective, we adopt popular models to achieve a desirable newdata-invariant property. Our empirical results suggest that the adapted model can provide reliable forecast of mortality rates for use in demographic research. [ABSTRACT FROM AUTHOR]

The paper draws attention to the profound changes that are taking place in the proportions of the population of Great Britain that are single, married and widowed at various ages. These changes are the net result of a number of demographic, economic and sociological developments, such as the decrease in mortality rates and the changing balance of the sexes. After discussing an abortive attempt to forecast the future progress of the proportions, based on changes that have taken place in the recent past, the paper describes a method of estimating the pro- portions of each sex that are likely to be found in each marital status at the various attained ages in future years if the experience of the years 1950-52 in Great Britain as regards rates of marriage, divorce, widowhood and re-marriage continue indefinitely, subject to minor modifications to ensure that the numbers of marriages, etc., for the two sexes are consistent, and that the total number of married men equals that of married women. The variations in the proportions in each marital status in Great Britain during the past thirty years and those to be expected during the next thirty years, and ultimately, if the experience of the years 1950-52 persists, are set out and analysed. It is shown that, whereas the proportion of men never marrying has up to the present been about half the proportion of women never marrying, the continuance of present trends would result in the proportion of men never marrying becoming double that for women. This notable change is to be attributed, in the main, to the effect of improved vitality in childhood in replacing the excess of women at the younger adult ages by an excess of men. It is pointed out that, in the event, this change may not be fully realised, because a net emigration of men or a net immigration of women may mitigate the relative dearth of potential brides in future; but the proportions never marrying set out in the paper show the directions towards which current conditions are tending. The consequential changes for other marital statuses are also set out and discussed. [ABSTRACT FROM AUTHOR]

Flooding is one of the costliest hazards in the United Kingdom. A large part of the annual flood damage is caused by surface water flooding that is a direct result of intense rainfall. Traditional catchment-based approaches to flood prediction are not applicable for surface water floods. However, given sufficiently accurate forecasts of rainfall intensity, with sufficient lead time, actions can be taken to reduce their impact. These actions require reliable information about severity and areas at risk that is clear and easily interpreted. The accuracy requirements, in particular, are very challenging, as they relate to prediction of intensities that occur only infrequently and that typically affect only small areas. In this paper, forecasts of intense rainfall from a new convection-permitting ensemble prediction system are evaluated using radar observations of intense rain and surface water flooding reports. An urban flooding case that occurred in Edinburgh in 2011 is first investigated and then a broader look is taken at performance through a 3-month period during the London Olympic and Paralympic Games in 2012. Conclusions are drawn about the value of the ensemble and the particular means of presenting the forecasts, and areas requiring further work are highlighted. [ABSTRACT FROM AUTHOR]

Background: Ageing populations and rising prevalence of non-communicable diseases (NCDs) increasingly contribute to the growing cost burden facing European healthcare systems. Few studies have attempted to quantify the future magnitude of this burden at the European level, and none of them consider the impact of potential changes in risk factor trajectories on future health expenditures. Methods: The new microsimulation model forecasts the impact of behavioural and metabolic risk factors on NCDs, longevity and direct healthcare costs, and shows how changes in epidemiological trends can modify those impacts. Economic burden of NCDs is modelled under three scenarios based on assumed future risk factors trends: business as usual (BAU); best case and worst case predictions (BCP and WCP). Findings: The direct costs of NCDs in the EU 27 countries and the UK (in constant 2014 prices) will grow under all scenarios. Between 2014 and 2050, the overall healthcare spending is expected to increase by 0.8% annually under BAU. In the all the countries, 605 billion Euros can be saved by 2050 if BCP is realized compared to the BAU, while excess spending under the WCP is forecast to be around 350 billion. Interpretation: Although the savings realised under the BCP can be substantial, population ageing is a stronger driver of rising total healthcare expenditures in Europe compared to scenario-based changes in risk factor prevalence. [ABSTRACT FROM AUTHOR]

Brain age prediction from 3D MRI volumes using deep learning has recently become a popular research topic, as brain age has been shown to be an important biomarker. Training deep networks can be very computationally demanding for large datasets like the U.K. Biobank (currently 29,035 subjects). In our previous work, it was demonstrated that using a few 2D projections (mean and standard deviation along three axes) instead of each full 3D volume leads to much faster training at the cost of a reduction in prediction accuracy. Here, we investigated if another set of 2D projections, based on higher-order statistical central moments and eigenslices, leads to a higher accuracy. Our results show that higher-order moments do not lead to a higher accuracy, but that eigenslices provide a small improvement. We also show that an ensemble of such models provides further improvement. [ABSTRACT FROM AUTHOR]

This study proposes two novel tests for security analyst herding based on binomial correlation and forecast error volatility scaling, and applies it to investigate herding patterns in analyst target prices in 2008–2020 in the UK. Analysts robustly herd in their valuations, with results consistent across years, sectors, in terms of panel fixed effect, quantile, instrumental variable regressions, and when controlled for optimism and conservatism. Herding becomes prominent for stocks followed by at least five analysts and towards the long sides of Fama-French sorts, reinforcing its non-spurious and behavioral nature. [ABSTRACT FROM AUTHOR]

Numerous methods have been proposed for forecasting load for normal days. Modeling of anomalous load, however, has often been ignored in the research literature. Occurring on special days, such as public holidays, anomalous load conditions pose considerable modeling challenges due to their infrequent occurrence and significant deviation from normal load. To overcome these limitations, we adopt a rule-based approach, which allows incorporation of prior expert knowledge of load profiles into the statistical model. We use triple seasonal Holt-Winters-Taylor (HWT) exponential smoothing, triple seasonal autoregressive moving average (ARMA), artificial neural networks (ANNs), and triple seasonal intraweek singular value decomposition (SVD) based exponential smoothing. These methods have been shown to be competitive for modeling load for normal days. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model load for special days, when used in conjunction with a rule-based approach. The proposed rule-based method is able to model normal and anomalous load in a unified framework. Using nine years of half-hourly load for Great Britain, we evaluate point forecasts, for lead times from one half-hour up to a day ahead. A combination of two rule-based methods generated the most accurate forecasts. [ABSTRACT FROM PUBLISHER]

This article presents estimates of the minority ethnic populations of Great Britain in 2000 and describes the regional distributions of the different groups. It also discusses changes in the sizes of the different minority ethnic populations during the 1990s. The paper provides information on some key demographic features of these populations at the end of the 1990s--age and sex structures, proportions born in the United Kingdom and whether children lived with their natural parents.

This paper reports on projections of the United Kingdom's ethnic group populations for 2001-2051. For the years 2001-2007 we estimate fertility rates, survival probabilities, internal migration probabilities and international migration flows for 16 ethnic groups and 355 UK areas. We make assumptions about future component rates, probabilities and flows and feed these into our projection model. This model is a cohort-component model specified for single years of age to 100+. To handle this large state space, we employed a bi-regional model. We implement four projections: (1) a benchmark projection that uses the component inputs for 2001; (2) a trend projection where assumptions beyond 2007 are adjusted to those in the UK 2008-based National Population Projections (NPP); (3) a projection that modifies the NPP assumptions and (4) a projection that uses a different emigration assumption. The projected UK population ranges between a low of 63 millions in 2051 under the first projection to a high of 79 million in the third projection. Under all projections ethnic composition continues to change: the White British, White Irish and Black Caribbean groups experience the slowest growth and lose population share; the Other White and Mixed groups to experience relative increases in share; South Asian groups grow strongly as do the Chinese and Other Ethnic groups. The ethnic minority share of the population increases from 13% (2001) to 25% in the trend projection but to only 20% under our modified emigration projection. However, what is certain is that the UK can look forward to be becoming a more diverse nation by mid-century. [ABSTRACT FROM AUTHOR]

This case-oriented paper shows how an in-house OR group in a small Government Department (ECGD) influences decision-making by feeding technical analysis directly into strategic decision-making. It covers both the technical detail of the analysis and the OR process which led to implementation. The technical part of the paper describes how the forecast expected cost of giving a fixed interest rate hold to a borrower was estimated using an option valuation. The cost estimation is complicated by the context: ECGD is also providing a guarantee of loan repayment by the overseas borrower to a bank, and ECGD costs include a binary bank margin cost which depends on whether the fixed interest rate hold is taken up by the borrower. The remainder of the paper describes the way OR analysts worked with decision-makers and considers the implications for effective practice. [ABSTRACT FROM AUTHOR]

This paper considers efforts to improve in-patient flows, a particularly urgent issue in the National Health Service (NHS). The context is described and related to reasons why OR has been making relatively little contribution. The paper argues that large complex models may often be unnecessary and even get in the way of providing clear insight and guidance for problem owners. The importance of understanding the generic working of systems to lead to improvement, and the limitations of simply describing them, is stressed. It is demonstrated that some very simple models can be of significant practical value in understanding and managing complex systems, changing mindsets and driving collection and use of operationally valuable data. Recommendations for more effective engagement with the NHS are offered. [ABSTRACT FROM AUTHOR]

This paper identifies the synoptic situations, and combinations of those situations, together with other influences such as topography and minor fronts (e.g. sea breeze and coastal fronts), that produce tornadoes in the United Kingdom (UK). This marks an important step towards improving the forecasting of tornadoes in the UK and the issuing of tornado warnings at suitable times. The Tornado and Storm Research Organisation (TORRO) has experimented with issuing tornado watches and pre-watch severe thunderstorm/tornado advisories to its UK network of observers since 1991. Complementing this work, this paper identifies those synoptic situations that statistically and historically have given rise to the most prolific outbreaks or strongest tornado events. These situations include a strong (ana) cold front associated with a rapidly deepening low; a zone of wind shear, especially when enhanced by topography; and a slack area of low pressure in the late spring and summer. The role of lee effects in which eddies are created downwind of islands, coastal promontories and high ground inland are highlighted too. Synoptic situations that produce tornadoes, but only rarely, include severe summer or supercell thunderstorms; the point of occlusion (triple point) associated with a deep depression, often incorporating the remnants of an ex-tropical storm; and returning polar maritime airstreams with an upper trough to the west of the UK. Generally, most UK tornadoes occur during an air-mass change and during ‘cold weather’ scenarios during the autumn and winter. This highlights that the synoptic situations that frequently produce tornadoes in the UK differ from those producing tornadoes in continental USA and in many other parts of the world. Instead, these findings suggest the UK tornado regime is similar to other west coast locations such as California, western France and, perhaps, Portugal. [Copyright &y& Elsevier]

Research indicates that investors rely on various criteria to evaluate early-stage companies. However, past research in this area has focused on subsets of factors and does not distinguish between the predictors and causal determinants of start-up valuation. In our study, we applied machine learning and causal discovery to analyze a comprehensive dataset with 57 independent variables and 2,366 valuations of start-ups in the United Kingdom. The results show a strong relationship between good predictors and causal determinants of valuation. However, noncausal variables may still be useful for prediction, and inversely, some observed causes may not help in the prediction task. [ABSTRACT FROM AUTHOR]

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 – 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data. Identification of gene-by-environment interactions is crucial to understand the interplay of environmental effects on complex traits. Here, the authors present MonsterLM, a method for estimating the proportion of trait variance explained by gene-by-environment interactions in a fast, unbiased manner on biobank-scale datasets. [ABSTRACT FROM AUTHOR]

In this paper, we apply and further illustrate a recently developed extended continuous chain ladder model to forecast mesothelioma deaths. Making such a forecast has always been a challenge for insurance companies as exposure is difficult or impossible to measure, and the latency of the disease usually lasts several decades. While we compare three approaches to this problem, we show that the extended continuous chain ladder model is a promising benchmark candidate for asbestosis mortality forecasting due to its flexible and simple forecasting strategy. Furthermore, we demonstrate how the model can be used to provide an update for the forecast of the number of deaths due to mesothelioma in Great Britain using in recent Health and Safety Executive (HSE) data. [ABSTRACT FROM AUTHOR]

This paper derives a model of the stocks plan based on optimal behaviour by the firm when facing adjustment costs with forward-looking expectations formation. Estimates of the model are reported for manufacturing, using three alternative expectations mechanisms for determining expected sales: time series, published forecasts (derived from National Institute published forecasts); and rational expectations. The latter uses a submodel dependent on weakly exogenous variables. Single equation and whole model comparisons are made of the stocks equation estimated with forward expectations versus a traditional backward-looking model. These favour the forward-looking model. [ABSTRACT FROM AUTHOR]

Grid-connected photovoltaic (PV) capacity is increasing and is currently estimated to account for 3.0% of worldwide energy generation. One strategy to balance fluctuating PV power is to incentivize self-consumption by shifting certain loads. The potential improvement in the amount of self-consumption is usually estimated using smart meter and PV production data. Smart meter data are usually available only at sampling frequences far below the Nyquist limit. In this paper we investigate how this insufficient sampling rate affects the estimated self-consumption potential of shiftable household appliances (washing machines, tumble dryers and dishwashers). We base our analyses on measured consumption data from 16 households in the UK and corresponding PV data. We found that the simulated results have a marked dependence on the data sampling rate. The amount of self-consumed energy estimated with data sampled every 10 min was overestimated by 30–40% compared to estimations using data with 1 min sampling rate. We therefore recommend to take this factor into account when making predictions on the impact of appliance load shifting on the rate of self-consumption. [ABSTRACT FROM AUTHOR]

Traditional weather forecasting approaches use various numerical simulations and empirical models to produce a gridded estimate of rainfall, often spanning multiple regions but struggling to capture extreme events. The approach presented here combines modern meteorological forecasts from the ECMWF SEAS5 seasonal forecasts with convolutional neural networks (CNNs) to improve the forecasting of total monthly regional rainfall across Great Britain. The CNN is trained using mean sea-level pressure and 2-m air temperature forecasts from the ECMWF C3S service using three lead-times: 1 month, 3 months and 6 months. The training is supervised using the equivalent benchmark rainfall data provided by the CEH-GEAR (Centre for Ecology and Hydrology, gridded estimates of areal rainfall). Comparing the CNN to the ECMWF predictions shows the CNN out-performs the ECMWF across all three lead times. This is done using an unseen validation dataset and based on the root mean square error (RMSE) between the predicted rainfall values for each region and benchmark values from the CEH-GEAR dataset. The largest improvement is at a 1-month lead time where the CNN model scores a RMSE 6.89 mm lower than the ECMWF. However, these differences are exacerbated at the extremes with the CNN producing, at a 1-month lead time, RMSEs which are 28.19 mm lower than the corresponding predictions from the ECMWF. Following this, a sensitivity analysis shows the CNN model predicts increased rainfall values in the presence of a low sea-level pressure anomaly around Iceland, followed by a high sea-level pressure anomaly south of Greenland. [ABSTRACT FROM AUTHOR]