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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 COVID-19 pandemic has profoundly impacted every aspect of our lives, from economic to the social facets of contemporary society. While the new COVID-19 waves may not be anticipated to be as severe as previous ones, it would be unreasonable to assume that they will cease any time soon. Consequently, forecasting the number of future infections, recovered patients, and death cases remains a very much logical step in trying to fight against further waves, in conjunction with ongoing vaccination efforts. In this paper, we investigate the efficiency of three intelligent machine learning algorithms, namely GMDH, Bi-LSTM, and GA + NN, for COVID-19 forecasting, with an application to Iran and the United Kingdom. The experimental results show that the algorithms can be used to forecast the next six months of COVID-19 in terms of confirmed, recovered, and death cases, which gives a more ample timeframe for using the results to make better practical yet strategic decisions and take appropriate actions or measures to deploy resources effectively to contain or curb the spread of the coronavirus. Despite the distinct dynamics observed in the data, our analysis proves the robustness of the employed models. [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]

Investigating the current and future dynamics of energy consumption in modern economies such as the UK is crucial. This paper predicts the UK's energy consumption using data spanning January 1995 to March 2022 by comparing and evaluating the forecast performance of machine learning, dynamic regression, time series and combination modelling techniques. The analysis reveals that the seasonal ARIMA and TBATS hybrid models yield the lowest forecast errors in predicting the UK's electricity and gas consumption. Although the combination forecasts performed poorly relative to other models, machine learning techniques such as neural network and support vector regression produced better results compared to the dynamic regression models, whereas the seasonal hybrid model performed better than the machine learning and time series models. The results indicate that the UK's electricity consumption would either stabilise or decline over the forecast horizon, suggesting that it will take some years for electricity consumption to attain pre-2019 levels. For gas consumption, the results indicate that consumption would either maintain current levels or increase over the forecast period. We also show that combination forecasts do not often generate the best predictions, and therefore, choice of methodology matters in energy consumption forecasting. Overall, changing seasonal patterns, energy efficiency improvements, shift to renewable sources and uncertainties due to the COVID-19 pandemic, Brexit, and the Russia–Ukraine crisis appear to be significant drivers of energy consumption in the UK in recent times. These findings are expected to help in designing more effective energy policies as well as guide investor decisions in the energy sector. [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]

Inclinometer probes are devices that can be used to measure deformations within earthwork slopes. This paper demonstrates a novel application of Bayesian techniques to real‐world inclinometer data, providing both anomaly detection and forecasting. Specifically, this paper details an analysis of data collected from across the entire UK rail network. Inclinometers are a standard tool of geotechnical site monitoring. Data from these instruments is often used in risk analysis and decision‐making. However, discerning anomalous data points and forecasting future behaviour from inclinometer data requires significant 'engineering judgement' (subjective appraisal). This is because the observational data is derived from complex physical phenomena and contains complex spatio‐temporal correlations. Additionally, the practical demands of data collected from remote sites over several years tends to introduce systematic errors. These issues make the interpretation of inclinometer data challenging. Practitioners have effectively two goals when processing monitoring data. The first is to identify any anomalous or dangerous movements, and the second is to predict potential future adverse scenarios by forecasting. In this paper we apply Uncertainty Quantification (UQ) techniques by implementing a Bayesian approach to anomaly detection and forecasting for inclinometer data. Subsequently, both costs and risks may be minimised by quantifying and evaluating the appropriate uncertainties. This framework may then act as an enabler for enhanced decision making and risk analysis. We show that inclinometer data can be described by a latent autocorrelated Markov process derived from measurements. This can be used as the transition model of a non‐linear Bayesian filter. This allows for the prediction of system states. This learnt latent model also allows for the detection of anomalies: observations that are far from their expected value may be considered to have 'high surprisal', that is they have a high information content relative to the model encoding represented by the learnt latent model. We successfully apply the forecasting and anomaly detection techniques to a large real‐world data set in a computationally efficient manner. Although this paper studies inclinometers in particular, the techniques are broadly applicable to all areas of engineering UQ and Structural Health Monitoring (SHM). [ABSTRACT FROM AUTHOR]

The k‐nearest neighbors algorithm is one of the prominent techniques used in classification and regression. Despite its simplicity, the k‐nearest neighbors has been successfully applied in time series forecasting. However, the selection of the number of neighbors and feature selection is a daunting task. In this paper, we introduce two methodologies for forecasting time series that we refer to as Classical Parameters Tuning in Weighted Nearest Neighbors and Fast Parameters Tuning in Weighted Nearest Neighbors. The first approach uses classical parameters tuning that compares the most recent subsequence with every possible subsequence from the past of the same length. The second approach reduces the neighbors' search set, which leads to significantly reduced grid size and hence a lower computational time. To tune the models' parameters, both methods implement an approach inspired by cross‐validation for weighted nearest neighbors. We evaluate the forecasting performance and accuracy of our models. Then, we compare them to other approaches, especially, Seasonal Autoregressive Integrated Moving Average, Holt Winters, and Exponential Smoothing State Space Model. Real data examples on retail and food services sales in the United States and milk production in the United Kingdom are analyzed to demonstrate the application and efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid agent-based stock-flow-consistent model featuring heterogeneous banks, purposely built to examine the effects of variations in banks' expectations formation and forecasting behaviour and to conduct policy experiments with a focus on monetary and prudential policy. The model is initialised to a deterministic stationary state and a subset of its free parameters are calibrated empirically in order to reproduce characteristics of UK macro-time-series data. Experiments carried out on the baseline focus on the expectations formation and forecasting behaviour of banks through allowing banks to switch between forecasting strategies and having them engage in least-squares learning. Overall, simple heuristics are remarkably robust in the model. In the baseline, which represents a relatively stable environment, the use of arguably more sophisticated expectations formation mechanisms makes little difference to simulation results. In a modified version of the baseline representing a less stable environment alternative heuristics may in fact be destabilising. To conclude the paper, a range of policy experiments is conducted, showing that an appropriate mix of monetary and prudential policy can considerably attenuate the macroeconomic volatility produced by the model. [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]

This paper investigates the predictive ability of a brand new dataset that is based on big unstructured data published by the UK Office for National Statistics as "Faster Indicators of UK Economic Activity." We consider some indicative ways for use in macroeconomic nowcasting. Even though the ONS confirms that the newly introduced big data‐based indicators are not constructed with forecasting purposes in mind, and applied researchers should be cautious when using them in this way, a simple out‐of‐sample nowcasting exercise reveals partial evidence that this dataset has some predictive power over GDP growth. Our results, which show a positive and encouraging first step towards the use of this type of data, suggest that national statistics agencies should allocate more resources in constructing big data‐based databases. [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]

A growing number of studies have investigated how forecast skill, i.e., predictive power, translates into forecast value, i.e., usefulness, for improving forecast-informed decisions. The relationship between skill and value is widely understood to be complex and case-specific, yet few methods enable its systematic exploration using realistic forecast errors. This paper addresses this gap by proposing a single-parameter linear scaling method to generate families of synthetic forecasts with the desired skill improvements on an existing hindcast (a retrospective forecast of already-observed events). The method is applicable to any quantity for which a deterministic or ensemble hindcast is available, and generates a set of forecasts with different skill but strictly proportional errors. This like-for-like comparison preserves the autocorrelation and cross-correlations of errors, and opens the door for thorough, yet easily interpretable, explorations of the relationship between skill and value of a realistic forecast. We apply this new method to seasonal precipitation hindcasts (produced by the fifth generation of the Seasonal forecasting System of the European Centre for Medium-range Weather Forecasts, ECMWF-SEAS5) in order to explore their value for improving the management of a water supply system in the UK. The application showed that although value generally increases with skill, the skill–value relationship is not necessarily linear, and it strongly depends on operational preferences and hydrological conditions (wet or dry years). It also suggests that the forecast families methodology can help water managers and forecast developers identify when a skill increase would be most valuable. This has the potential to foster productive two-way conversations between forecast producers and users. [ABSTRACT FROM AUTHOR]

This paper considers the problem of forecasting inflation in the United States, the euro area, and the United Kingdom in the presence of possible structural breaks and changing parameters. We examine a range of moving window techniques that have been proposed in the literature. We extend previous works by considering factor models using principal components and dynamic factors. We then consider the use of forecast combinations with time‐varying weights. Our basic finding is that moving windows do not produce a clear benefit to forecasting. Time‐varying combination of forecasts does produce a substantial improvement in forecasting accuracy. [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]

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]

Concerning approaches and communications to the Royal Family and other British public figures are relatively numerous. This paper examines over 2000 such cases logged over a three‐year period in the United Kingdom. Using police and health data, the paper conducts a series of bivariate and multivariate analyses to demonstrate the predictors of what types of risk are posed by an individual case (e.g., communicate only, approach, security breach). The results showed that (a) the rates of serious mental disorders are higher among this sample than the general population base rate, (b) approachers were significantly more likely than communicators to suffer from serious mental disorders, (c) approachers were significantly more likely than communicators to have a history of substance use and abuse problems, (d) approachers were significantly more likely than communicators to have a history of violent behavior against property and persons, and (e) the motivations of approachers and communicators significantly differ. The paper concludes with a consideration of the implications for threat assessment and management. [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]

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]

This paper provides novel insight into the growing literature on the policy uncertainty‐stock market volatility nexus by examining the out‐of‐sample predictive ability of the quality of political signals over stock market volatility at various forecast horizons. Specifically, we examine whether or not accounting for the signal quality in forecasting models within a mixed frequency framework can improve forecast performance and help achieve economic gains for investors. Both in‐ and out‐of‐sample tests, based on a GARCH‐MIDAS framework, show that the quality of the policy signal matters regarding the predictive role of policy uncertainty over subsequent stock market volatility. While high economic policy uncertainty (EPU) predicts high volatility, particularly when the signal quality is high, the positive relationship between EPU and volatility breaks down when the signal quality is low. The improved out‐of‐sample volatility forecasts obtained from the models that account for the quality of policy signals also help typical mean–variance investors achieve improved economic outcomes captured by higher certainty equivalent returns and Sharpe ratios. Although our results indicate clear distinctions between the US and UK stock markets in terms of how market participants process policy signals, they highlight the role of the quality of policy signals as a driver of volatility forecasts with significant economic implications. [ABSTRACT FROM AUTHOR]

Alerts of potentially hazardous coronal mass ejection (CME) are based on the detection of rapid changes in remote observations of the solar atmosphere. This paper presents a method that detects and estimates the central coordinates of CME eruptions in Extreme Ultraviolet data, with the dual aim of providing an early alert, and giving an initial estimate of the CME direction of propagation to a CME geometrical model. In particular, we plan to link the Automated detection of coronaL MAss ejecta origiNs for space weather AppliCations (ALMANAC) method to the CME detection and characterization module of the Space Weather Empirical Ensemble Package, which is a fully automated modular software package for operational space weather capability currently being developed for the UK Meteorological Office. In this work, ALMANAC is applied to observations by the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO). As well as presenting the method, a proof of concept test is made on a limited set of data associated with 20 halo CMEs recorded by the Coordinated Data Analysis Workshop (CDAW) catalog near the activity maximum of solar cycle 24. SDO/AIA data for each event is processed at 6 min cadence to identify the on‐disk location and time of each CME. The absolute mean deviance between the ALMANAC and CDAW source event coordinates are within 37.05 ± 29.71 min and 11.01 ± 10.39°. These promising results give a solid foundation for future work, and will provide initial constraints to an automated CME alert and forecasting system. Plain Language Summary: This work details a new tool to be used in the operational space weather forecasting of large eruptions from the solar atmosphere for the UK Meteorology Office. This work has considerable potential for economical and societal impact, since early warning of large space weather events can mitigate the substantial risk to technological infrastructure. We have created a novel software package which can forecast the early signatures of coronal mass ejections (CMEs) in the low solar atmosphere in real time. The code will eventually be used on a daily basis to improve predictions of CME arrival times and their potential impact at Earth. Furthermore, when the method is applied to historical data sets, it may lead to a greater scientific understanding on the connection between solar eruptive events and space weather. Key Points: The software package presented can forecast the early signatures of coronal mass ejections (CMEs) in the low solar atmosphere in real timeThe goal of this work is to improve the forecasting of CME arrival times and potential impact when used as part of a software suiteApplied to historical data sets, the method can lead to a greater scientific understanding on the connection between CMEs and space weather [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has raised major issues with regard to modelling and forecasting outcomes such as cases, deaths and hospitalisations. In particular, the forecasting of area-specific counts of infectious disease poses problems when counts are changing rapidly and there are infection hotspots, as in epidemic situations. Such forecasts are of central importance for prioritizing interventions or making severity designations for different areas. In this paper, we consider different specifications of autoregressive dependence in incidence counts as these may considerably impact on adaptivity in epidemic situations. In particular, we introduce parameters to allow temporal adaptivity in autoregressive dependence. A case study considers COVID-19 data for 144 English local authorities during the UK epidemic second wave in late 2020 and early 2021, which demonstrate geographical clustering in new cases—linked to the then emergent alpha variant. The model allows for both spatial and time variation in autoregressive effects. We assess sensitivity in short-term predictions and fit to specification (spatial vs space-time autoregression, linear vs log-linear, and form of space decay), and show improved one-step ahead and in-sample prediction using space-time autoregression including temporal adaptivity. [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]

Summary: This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic variables including GDP, inflation and unemployment. Our text is drawn from three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. Exploiting newspaper text can improve economic forecasts both unconditionally and when conditioning on other relevant information, but the performance of the latter varies according to the method used. Incorporating text into forecasts by combining counts of terms with supervised machine learning delivers the highest forecast improvements relative to existing text‐based methods. These improvements are most pronounced during periods of economic stress when, arguably, forecasts matter most. [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]

In this paper, we analyze the potential role of growth in inequality for forecasting realized volatility of the stock market of the UK. In our forecasting exercise, we use linear and nonlinear models as well as measures of absolute and relative consumption and income inequalities at quarterly frequency over the period of 1975 to 2016. Our results indicate that, while linear models incorporating the information of growth in inequality does produce lower forecast errors, these models do not necessarily outperform the univariate linear and nonlinear models based on formal statistical forecast comparison tests, especially in short‐ to medium runs. However, at a one‐year‐ahead horizon, absolute measure of consumption inequality results in significant statistical gains for stock market volatility predictions – possibly due to consumption inequality translating into both political and social uncertainty in the long run. [ABSTRACT FROM AUTHOR]

The unfolding coronavirus (COVID‐19) pandemic has highlighted the global need for robust predictive and containment tools and strategies. COVID‐19 continues to cause widespread economic and social turmoil, and while the current focus is on both minimising the spread of the disease and deploying a range of vaccines to save lives, attention will soon turn to future proofing. In line with this, this paper proposes a prediction and containment model that could be used for pandemics and natural disasters. It combines selective lockdowns and protective cordons to rapidly contain the hazard while allowing minimally impacted local communities to conduct "business as usual" and/or offer support to highly impacted areas. A flexible, easy to use data analytics model, based on Self Organising Maps, is developed to facilitate easy decision making by governments and organisations. Comparative tests using publicly available data for Great Britain (GB) show that through the use of the proposed prediction and containment strategy, it is possible to reduce the peak infection rate, while keeping several regions (up to 25% of GB parliamentary constituencies) economically active within protective cordons. [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]

Despite the suggestion from several meta-analyses that nest predation is not limiting songbird populations, responses to experimental removal of nest predators, such as corvids, have varied. The impact of nest predation by corvids on songbird populations is unclear. One potential explanation for this disparity is that susceptibility could vary according to the nesting biology of a species. To investigate this hypothesis, we conducted a review of studies detailing nest predation on UK songbirds and extracted the reported rates of nest predation from 80 papers which attempted to identify the predators responsible. We found that corvids were significant predators of songbird nests (24% of nest predation was attributed to corvids). However, species that construct open nests, and whose breeding season overlaps considerably with the breeding season of magpie and jay, incurred the highest rate of nest predation by corvids specifically (different factors predicted predation by non-corvid predators). We then used attributes of nesting biology to predict predation rates for UK breeding species for which we found no empirical data (n = 31). Although, in most cases cited in the literature songbird populations are unaffected by changes in corvid numbers, there are some cases in which species we predicted to be more susceptible to corvid predation responded to changes in corvid numbers. However, an understanding of how of other predators and other non-predation-related factors limit both songbird breeding success and songbird population numbers is necessary in any conservation management of songbird populations. [ABSTRACT FROM AUTHOR]

This paper uses a meta-analysis to survey existing factor forecast applications for output and inflation and assesses what causes large factor models to perform better or more poorly at forecasting than other models. Our results suggest that factor models tend to outperform small models, whereas factor forecasts are slightly worse than pooled forecasts. Factor models deliver better predictions for US variables than for UK variables, for US output than for euro-area output and for euro-area inflation than for US inflation. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre-selecting the variables included in the dataset did not improve factor forecasts in the past. Finally, the factor estimation technique may matter as well. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper offers the personal view of a senior executive in the Defence Procurement Agency about the future requirements of the defence industry's principal UK customer. The focus here is on trying to identify the type of corporate behaviour and range of skill sets that the UK Government will need from defence suppliers, both primes and supply chain companies in the future. The concluding message from this 'Customer View' is that there is likely to be a continuing market for the company that specializes in delivering defence capability. It will be a company that will be looking for a long-term relationship, with a specialist knowledge of its various national customers and a willingness to work openly and closely with them. It will be agile in its ability to bring together diverse technologies, to package them as a system and to deliver them either as hardware or as a service. Such suppliers will also need to innovate and to adapt at least as rapidly as the threats that they seek to counter. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for forecasting Value-at-Risk (VaR) by combining a Bayesian GARCH(1,1) model with Student's-t distribution for the underlying volatility models, vine copula functions to model dependence, and the peaks-over-threshold (POT) method of extreme value theory (EVT) to model the tail behaviour of asset returns. We further propose a new approach for threshold selection in extreme value analysis, which we call a hybrid method. The empirical results and back-testing analysis show that the model captures VaR quite well through periods of calmness and crisis; therefore, it is suitable for use as a measure of risk. Our results also suggest that with a correct implementation of the VaR model, Basel III is not needed. [ABSTRACT FROM AUTHOR]

Given the escalating healthcare costs around the world (more than 10% of the world's GDP) and increasing demand hospitals are under constant scrutiny in terms of managing services with limited resources and tighter budgets. Hospitals endeavour to find sustainable solutions for a variety of challenges ranging from productivity enhancements to resource allocation. For instance, in the UK, evidence suggests that hospitals are struggling due to increased delayed transfers of care, bed-occupancy rates well above the recommended levels of 85% and unmet A&E performance targets. In this paper, we present a hybrid forecasting-simulation-optimisation model for an NHS Foundation Trust in the UK. Using the Hospital Episode Statistics dataset for A&E, outpatient and inpatient services, we estimate the future patient demands for each speciality and model how it behaves with the forecasted activity in the future. Discrete event simulation is used to capture the entire hospital within a simulation environment, where the outputs is used as inputs into a multi-period integer linear programming (MILP) model to predict three vital resource requirements (on a monthly basis over a 1-year period), namely beds, physicians and nurses. We further carry out a sensitivity analysis to establish the robustness of solutions to changes in parameters, such as nurse-to-bed ratio. This type of modelling framework is developed for the first time to better plan the needs of hospitals now and into the future. [ABSTRACT FROM AUTHOR]

The reference functions and tables for base-metal thermocouples, which relate temperature and electro-motive-force, are specified by thermocouple type, rather than by alloy composition. The common thermocouple types—E, J, K, N, and T—are required to meet the specifications within defined tolerance bands; referred to as either limits-of-error (ASTM, US) or class (IEC, UK and Europe). In this requirement hides a fundamental problem: the tolerances are a manufacturing specification. Despite this, the tolerances are often used as a proxy for expected behaviour. However, these thermocouple types are subject to rapid and irreversible changes after short exposure times to modest temperatures. Thus, tolerance statements are poor predictors of in-use behaviour. This paper initially reviews the development of the base-metal thermocouples before identifying many of the metallurgical processes that lead to departures from the accepted tolerance bands. Focus is specifically given to the processes that occur in several type K and N thermocouples, which exemplify the changes common in the other base-metal thermocouples. From these results, it is shown that the use of traditional calibration techniques can be a futile exercise. Comparisons are then made between as-received thermocouples and those given thermal treatments, that have been designed to allow meaningful calibration while also inhibiting drift. These results show that thermal treatments and calibration are a practicable means to minimise drift over typical periods of use while offering small uncertainties, well within the span of manufacturing tolerances. Lastly it is suggested manufacturers give more information on the testing used to establish conformance with tables and reference functions and that they follow standardised testing procedures. [ABSTRACT FROM AUTHOR]

This paper presents an approach to managing surface water abstraction utilizing real-time flow forecasting and control techniques. To evaluate the effectiveness of alternative data-driven and process-based methods, flow forecasts at a case study site (River Dove, UK) using (1) a probability-distributed rainfall-runoff model (PDM), (2) PDM coupled with an autoregressive integrated moving average (ARIMA) error predictor, and (3) a long short-term memory (LSTM) neural network are integrated into a water resources management model coupled with genetic algorithm optimization to simulate and compare water abstractions, reservoir storage, downstream river flows, and pumping energy costs. When compared to historical data, results show that both PDM plus ARIMA and LSTM forecasts led to improved water abstraction operations, i.e., increased water abstraction volumes during dry periods while maintaining river environmental flows, as well as reduced pumping costs. Cost savings were found to be sensitive to the accuracy of the forecasting technique only within specific flow ranges. This study demonstrates the water resource benefits of real-time flow forecasting in supporting flexible water pumping schedules and further discusses the benefits of alternative modeling approaches in the specific context of controlling water abstraction. [ABSTRACT FROM AUTHOR]

Purpose – The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market. Design/methodology/approach – The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research. Findings – The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models. Practical implications – The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures. Originality/value – The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting. [ABSTRACT FROM AUTHOR]

Temperature changes are known to affect the social and environmental determinants of health in various ways. Consequently, excess deaths as a result of extreme weather conditions may increase over the coming decades because of climate change. In this paper, the relationship between trends in mortality and trends in temperature change (as a proxy) is investigated using annual data and for specified (warm and cold) periods during the year in the UK. A thoughtful statistical analysis is implemented and a new stochastic, central mortality rate model is proposed. The new model encompasses the good features of the Lee and Carter ( Journal of the American Statistical Association, 1992, 87: 659-671) model and its recent extensions, and for the very first time includes an exogenous factor which is a temperature-related factor. The new model is shown to provide a significantly better-fitting performance and more interpretable forecasts. An illustrative example of pricing a life insurance product is provided and discussed. [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]

Purpose – The Foresight Future Flooding (FFF) project researched flood risk in the UK to the year 2100 for central government, using scenarios and a national risk assessment model backed by qualitative analysis from panels of some 45 senior scientists. The purpose of this paper is to assess the impact of the project, both nationally and internationally.Design/methodology/approach – This paper assesses the impact of the FFF project, both nationally and internationally, using web searches, document analysis, and a questionnaire survey of key actors in the flood risk management policy field.Findings – It was found that the penetration of the project into professionals' consciousness was high in relation to other comparable projects and publications, and its impact on policy – both immediately and continuing – was profound. The FFF initiative did not create policy change, however, but facilitated its legitimation, adding impetus to what was already there, as one element of a part-catalytic and part-incremental process of policy evolution.Research limitations/implications – Special circumstances, internal and external to the project, mean that this cannot be a simple model for matching research to policymakers' needs in the future.Practical implications – Important lessons may be learnt from this project about both the methods of forward-looking foresight-type research, and the way that its results are disseminated to its target audiences.Originality/value – This is an innovative attempt to assess the impact of a new type of foresight project. [ABSTRACT FROM AUTHOR]

This paper aims to forecast the performance of Great Britain and Northern Ireland in the 2012 London Olympic Games. Previous research has relied primarily on population size and gross domestic product (GDP) to predict Olympic performance but the methodology used here follows the approach of Shibli and Bingham (2008), who predicted that China would win 46 gold medals in Beijing 2008. This approach is based on the thesis that elite performance is now more a matter of managed public investment and therefore less dependent on non-controllable variables such as population size and GDP. The Shibli and Bingham forecasts proved to be the most accurate forecast made, even though it was five medals short of China's total of 51 gold medals. In this paper, we extend the analysis to include total medals as well as gold medals and conclude that Great Britain and Northern Ireland will win 27 gold medals in London 2012 and 56 medals in total. Furthermore, we can reasonably expect Great Britain and Northern Ireland to win medals in 15 sports and 18 disciplines. If our forecasts prove to be accurate, then on all four measures 2012 will be Great Britain's best performance in the Olympic Games since 1908. [ABSTRACT FROM AUTHOR]

This paper investigates the forecasting ability of three different Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models and the Kalman filter method. The three GARCH models applied are: bivariate GARCH, BEKK GARCH, and GARCH-GJR. Forecast errors based on 20 UK company's weekly stock return (based on time-varying beta) forecasts are employed to evaluate the out-of-sample forecasting ability of both the GARCH models and the Kalman method. Measures of forecast errors overwhelmingly support the Kalman filter approach. Among the GARCH models, GJR appears to provide somewhat more accurate forecasts than the two other GARCH models. [ABSTRACT FROM AUTHOR]

The Heathrow expansion project proposals comprise major works around Heathrow airport to allow the construction of new terminal buildings and the third runway. Owing to the critical nature of uninterrupted operation of both runway and the M25 motorway, the preliminary design had to be developed to minimise maintenance operations. At over 140 m in the total length, the adoption of an integral bridge of this length is in excess of most integral bridges in the UK, particularly where full-height abutments are being utilised. The maximum predicted expansion length also lies outside the limit equilibrium method set out in PD6694-1. As such, it was agreed that a full soil–structure interaction study should be carried out to assess the impacts of the structural behaviour of the bridge and its abutments. This paper looks at the calibration work for that study, the derivation of the earth pressures behind the walls, the behaviour of the abutment and how this compares with the predictions set out in PD 6694-1. [ABSTRACT FROM AUTHOR]

The female labour force participation decision in the UK is a well-researched topic in empirical economics and econometrics. In this paper, using data from the UK Labour Force Survey in 2007, we contribute to the rich body of the literature by estimating a model using the kernel mixed-data-type conditional-probability (KMDTCP) estimator. We extend the analysis by comparing the predicted probabilities of the KMDTCP estimator with the estimates of a multinomial logit (MNL) model. Additionally, kernel smoothing specification tests are applied in order to compare different functional forms for the independent variables in the MNL model against the KMDTCP estimates. Our results demonstrate that the difference between the predictions from the KMDTCP estimator and the MNL is reduced when the functional form of continuous independent variables in the MNL model is relaxed to a semi-parametric form using dummy variables and interaction terms. The properties that are captured by the KMDTCP estimator show clearly that the propensity to work decreases with the increase in the number of dependent children. Furthermore, our analyses show that females older than 40 years of age have a high propensity to work irrespective of the number of dependent children. [ABSTRACT FROM AUTHOR]

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]

This paper first employs principal component analysis technique to develop and introduce an alternative UK corporate governance disclosure index to the US‐centric ones. Second, we then investigate whether this new corporate governance disclosure index can determine the level of executive pay (including CEOs, CFOs, and all executive directors) in UK listed firms, and consequently ascertain whether the governance mechanisms can moderate the pay‐for‐performance sensitivity. Employing data on corporate governance, executive pay and performance from 2008 to 2013, we find that, on average, better‐governed firms tend to pay their executives lower compared with their poorly‐governed counterparts. Additionally, our findings suggest that the pay‐for‐performance sensitivity is generally positive, but improves in firms with high corporate governance quality, implying that the pay‐for‐performance sensitivity is contingent on the quality of internal governance structures. We interpret our findings within the predictions of optimal contracting theory and managerial power hypothesis. [ABSTRACT FROM AUTHOR]