Temperaturprognoser är av stor betydelse för många i dagens samhälle, både privatpersoner och diverse olika sektorer. Förväntan på att prognoserna håller hög träffsäkerhet är stor och god kvalitet på dessa är viktigt av många olika aspekter. De numeriska vädermodellerna, som används för att göra väderprognoser, har brister som i stort sätt alltid leder till systematiska fel i prognoserna. Bristerna beror exempelvis på dålig representation av atmosfärens fysikaliska processer och för att korrigera och reducera dessa fel efterbehandlas prognoserna med olika metoder, så kallad post-processing. För att minimera de systematiska felen och öka träffsäkerheten för prognoserna pågår ständigt en utveckling och förbättring av både modellerna och post-processingmetodiken. Uppföljning och utvärdering av prognoser är av stor nytta för denna utveckling som ska leda till minimering av prognosfel och optimering av modell och metodik. I denna studie har temperaturprognosdata, med prognoslängd 0-12 timmar, från Sveriges Meteorologiska och Hydrologiska Institut (SMHI) och norska Meteorologisk institutt (met.no) jämförts med uppmätta värden för 2 m-temperatur. Observerad temperaturdata från 22 olika synoptiska väderstationer på platser utspridda över hela Sverige har använts i studien och perioden som studien är baserad på är 20 februari till 31 maj 2018. Statistiska mått, med mest fokus på korrelationskoefficient och bias, har analyserats och jämförts för att undersöka likheter och skillnader i temperaturprognoserna från de två olika väderinstituten. Resultaten av studien visar att temperaturprognoserna från met.no generellt sett har något högre träffsäkerhet än SMHI:s för de allra flesta av de 22 geografiska platserna. Båda institutens prognoser har för flertalet av stationerna i fjällen samt norra Sverige generellt sett lägre träffsäkerhet för februari än för mars, april och maj. / Temperature forecasts are of great importance for many different reasons in today's society, both for private individuals and various sectors. The expectations that the forecasts maintain high accuracy and good quality is important in many different aspects. The weather models, which are used to make the forecasts, have deficiencies which in large part always lead to systematic errors in the forecasts. The deficiencies are for example, due to poor representation of the physical processes of the atmosphere and to correct and reduce these errors, the forecasts are post-processed by various methods. To minimize the systematic errors and increase the accuracy of the forecasts, there is an ongoing development and improvement of both the models and the post-processing methods. Evaluation of forecasts is of great benefit to this development, which will lead to minimization of forecast errors and optimization of the model and methodology. In this study, temperature forecast data, with a forecast length of 0-12 hours, from the Swedish Meteorological and Hydrological Institute (SMHI) and the Norwegian Meteorological Institute (met.no) were compared with measured 2 m-temperature values. Observed temperature data from 22 different weather stations in locations scattered all over Sweden have been used in the study and the period on which the study is based is from the 20th of February to 31st of May, 2018. Different statistical measures have been analyzed and compared to examine similarities and differences in temperature forecasts from the two different weather institutes. The results of the study show that met.no's temperature forecasts generally have slightly higher accuracy than SMHI's for most of the 22 locations. For any of the stations in the mountains and northern Sweden forecasts from both institutes generally have lower accuracy for February than March, April and May.

In recent years, the initial public offerings (IPOs) on the Ghana Stock Exchange (GSE) witnessed some level of undersubscriptions. The purpose of this research was to investigate the extent to which valuation, pricing, and performance of prior IPOs listed on the GSE contributed to this state of undersubscriptions. The research was informed by the valuation and pricing framework of Roosenboom. The research questions addressed whether IPOs on the GSE were under/overpriced and whether the projected and pre-issue financials were free from forecasting errors and earnings management. A cross-sectional, explanatory research design was employed to examine a dataset of 30 sampled IPOs. The dataset, obtained from IPO prospectuses, trading data, and financial statements, was analyzed using both logistic and multiple regressions. IPO valuation methods, first-day returns (R(1st day)), absolute forecast errors (AFE), and discretionary current accruals (DCA) served as dependent variables and firm characteristics of size, age, profitability, dividends, price-to-value (P/V) ratios, owner-manager, and auditors' reputation served as independent variables. Results revealed that firm characteristics were not significant predictors of the choice of IPO valuation methods, IPOs were underpriced and their R(1st day) were significantly predicted by P/V ratios, the financial projections were over forecasted and their AFE were not predicted by the independent variables, and the pre-IPO financials experienced earnings management and their DCA were significantly explained by the owner-manager variable. This research contributes to positive social change by assisting regulators, investment bankers, corporations, and institutional investors in improving their respective roles in the valuation and pricing of IPOs on the GSE, thus reducing the observed IPO undersubscriptions in the stock market.

Deep artificial neural networks is a type of machine learning which can be used to find and utilize patterns in data. One of their many applications is as method for regression analysis. In this thesis deep artificial neural networks were implemented in the application of estimating the error of surface temperature forecasts as produced by a numerical weather prediction model. An ability to estimate the error of forecasts is synonymous with the ability to reduce forecast errors as the estimated error can be offset from the actual forecast. Six years of forecast data from the period 2010--2015 produced by the European Centre for Medium-Range Weather Forecasts' (ECMWF) numerical weather prediction model together with data from fourteen meteorological observational stations were used to train and evaluate error-predicting deep neural networks. The neural networks were able to reduce the forecast errors for all the locations that were tested to a varying extent. The largest reduction in error was by 83.0\% of the original error or a 16.7\degcs decrease in the mean-square error. The performance of the neural networks' error reduction ability was compared with that of a contemporary Kalman filter as implemented by the Swedish Meteorological and Hydrological Institute (SMHI). It was shown that the neural network implementation had superior performance for six out of seven of the evaluated stations where the Kalman filter had marginally better performance at one station.

The performance of three methods of mortality modelling and forecasting are compared. These include the basic Lee–Carter and two functional demographic models; the basic Hyndman–Ullah and the weighted Hyndman–Ullah. Using age-specific data from the Human Mortality Database of two developed countries, France and the UK (England&Wales), these methods are compared; through within-sample forecasting for the years 1999-2018. The weighted Hyndman–Ullah method is adjudged superior among the three methods through a comparison of mean forecast errors and qualitative inspection per the dataset of the selected countries. The weighted HU method is then used to conduct a 32–year ahead forecast to the year 2050.

Made available in DSpace on 2010-04-20T20:48:11Z (GMT). No. of bitstreams: 3 68472.pdf.jpg: 26974 bytes, checksum: 034a1c3c9d998708ccd9d2147b6ae400 (MD5) 68472.pdf: 1943638 bytes, checksum: 074240d8b8c6bdfa6dfcfb36d0dd4f75 (MD5) 68472.pdf.txt: 413719 bytes, checksum: bc028bb1f27dfc3f51effc0a0cdcb7af (MD5) Previous issue date: 2004-04-14T00:00:00Z / The main purpose of this thesis is to analyze the financial analysts of Brazilian firms. By gathering data from the market and analyzing the current performance of the firms, these professionals prepare earnings forecasts and stock recommendations. Using I/B/E/S database, it is presented a broad empirical research of the earnings forecasts and stock recommendations, as well as their information content for the Brazilian capital market. The empirical studies covered the period from January 1995 to June 2003. This thesis starts with the discussion of some concepts and the modus operandi of the financial analysts of Brazilian firms. After a literature review in the area, the empirical studies begin with the analysis of the earnings forecast errors. Some of their characteristics, such as accuracy, bias and precision are investigated in different contexts. After a critical analysis of the informational content for different types of earnings forecast revisions and actual announced earnings deviated form analysts expectations (earnings surprises), evidences of price effects in response to these facts are documented. The last part of this thesis discusses the role of stock recommendations in the Brazilian market. The percentage distribution of stock recommendations is verified as well as the informational content of stock recommendations. Other studies are carried out to verify the performance of the consensus stock recommendations and the effects of downgrades and upgrades of recommendations for Brazilian companies. / Esta tese propõe-se a analisar os analistas de mercado de capitais de empresas brasileiras. Coletando informações do mercado e analisando o desempenho corrente das empresas, estes profissionais realizam projeções de resultados e fazem recomendações. Usando dados extraídos do sistema I/B/E/S, realiza-se uma abrangente pesquisa empírica das previsões e recomendações dos analistas, bem como de seu conteúdo informativo para o mercado brasileiro. O período de estudo foi entre janeiro 1995 a junho 2003. Inicialmente são discutidos conceitos e particularidades do modus operandi dos analistas de empresas brasileiras. A seguir, depois de uma revisão da literatura onde se documentam as principais contribuições e descobertas, procede-se a uma investigação da natureza dos erros de previsão dos analistas de empresas brasileiras. Características como a acurácia, viés e precisão das previsões dos analistas são apreciadas e contextualizadas em diferentes situações. Efetua-se um detalhamento analítico do conteúdo informativo dos diferentes tipos de revisões de previsões dos analistas e das surpresas provocadas pelo anúncio de resultados em desacordo com as expectativas. De modo geral, as revisões e as surpresas, na medida em que informarem o mercado, provocam variações de retornos. Encerra-se a tese com uma análise das recomendações dos analistas. Apura-se a distribuição percentual das recomendações, assim como os efeitos sobre os preços de recomendações de compra (buy) e de venda(sell). O desempenho das recomendações de consenso e o efeito das revisões de recomendações para cima (upgrade) e para baixo (downgrade) são exemplos de outros pontos analisados.

Le contexte de nos travaux de thèse est l'intégration de l'énergie éolienne sur les réseaux insulaires. Ces travaux sont soutenus par EDF SEI, l'opérateur électrique des îles françaises. Nous étudions un système éolien-stockage où un système de stockage d'énergie doit aider un producteur éolien à tenir, vis-à-vis du réseau, un engagement de production pris un jour à l'avance. Dans ce contexte, nous proposons une démarche pour l'optimisation du dimensionnement et du contrôle du système de stockage (gestion d'énergie). Comme les erreurs de prévision J+1 de production éolienne sont fortement incertaines, la gestion d'énergie du stockage est un problème d'optimisation stochastique (contrôle optimal stochastique). Pour le résoudre, nous étudions tout d'abord la modélisation des composants du système (modélisation énergétique du stockage par batterie Li-ion ou Sodium-Soufre) ainsi que des entrées (modélisation temporelle stochastique des entrées incertaines). Nous discutons également de la modélisation du vieillissement du stockage, sous une forme adaptée à l'optimisation de la gestion. Ces modèles nous permettent d'optimiser la gestion de l'énergie par la méthode de la programmation dynamique stochastique (SDP). Nous discutons à la fois de l'algorithme et de ses résultats, en particulier de l'effet de la forme des pénalisations sur la loi de gestion. Nous présentons également l'application de la SDP sur des problèmes complémentaires de gestion d'énergie (lissage de la production d'un houlogénérateur, limitation des rampes de production éolienne). Cette étude de l'optimisation de la gestion permet d'aborder l'optimisation du dimensionnement (choix de la capacité énergétique). Des simulations temporelles stochastiques mettent en évidence le fort impact de la structure temporelle (autocorrélation) des erreurs de prévision sur le besoin en capacité de stockage pour atteindre un niveau de performance donné. La prise en compte de paramètres de coût permet ensuite l'optimisation du dimensionnement d'un point de vue économique, en considérant les coûts de l'investissement, des pertes ainsi que du vieillissement. Nous étudions également le dimensionnement du stockage lorsque la pénalisation des écarts à l'engagement comporte un seuil de tolérance. Nous terminons ce manuscrit en abordant la question structurelle de l'interaction entre l'optimisation du dimensionnement et celle du contrôle d'un système de stockage, car ces deux problèmes d'optimisation sont couplés. / The context of this PhD thesis is the integration of wind power into the electricity grid of small islands. This work is supported by EDF SEI, the system operator for French islands. We study a wind-storage system where an energy storage is meant to help a wind farm operator fulfill a day-ahead production commitment to the grid. Within this context, we propose an approach for the optimization of the sizing and the control of the energy storage system (energy management). Because day-ahead wind power forecast errors are a major source of uncertainty, the energy management of the storage is a stochastic optimization problem (stochastic optimal control). To solve this problem, we first study the modeling of the components of the system. This include energy-based models of the storage system, with a focus on Lithium-ion and Sodium-Sulfur battery technologies. We then model the system inputs and in particular the stochastic time series like day-ahead forecast errors. We also discuss the modeling of storage aging, using a formulation which is adapted to the control optimization. Assembling all these models enables us to optimize the energy management of the storage system using the stochastic dynamic programming (SDP) method. We introduce the SDP algorithms and present our optimization results, with a special interest for the effect of the shape of the penalty function on the energy control law. We also present additional energy management applications with SDP (mitigation of wind power ramps and smoothing of ocean wave power). Having optimized the storage energy management, we address the optimization of the storage sizing (choice of the rated energy). Stochastic time series simulations show that the temporal structure (autocorrelation) of wind power forecast errors have a major impact on the need for storage capacity to reach a given performance level. Then we combine simulation results with cost parameters, including investment, losses and aging costs, to build a economic cost function for sizing. We also study storage sizing when the penalization of commitment deviations includes a tolerance threshold. We finish this manuscript with a structural study of the interaction between the optimizations of the sizing and the control of an energy storage system, because these two optimization problems are coupled.

La question du développement inclusif dans les pays en développement est au cœur de cette thèse. Cette dernière s'articule autour de quatre chapitres sur les questions de politique fiscale et les questions liées à la croissance inclusive. Le chapitre 1 explore comment la politique fiscale de l’Etat affecte l'inclusivité de la croissance dans les pays en développement. Nous observons que la politique fiscale affecte la croissance inclusive de manière significative si et seulement si les pays ont de fortes qualités institutionnelles. En outre, notre résultat montre qu'il existe un seuil optimal au-delà duquel toute augmentation du taux d'imposition négativement la croissance inclusive. Le chapitre 2 examine les effets des composantes des dépenses publiques sur l'équité et la croissance dans les pays d’Afrique subsaharienne, notamment s'il est possible de concevoir des dépenses publiques en vue de promouvoir une société plus équitable sans sacrifier la croissance économique. Notre étude a permis de montrer que l’investissement en infrastructure a contribué à une croissance plus inclusive en Afrique subsaharienne que d'autres dépenses publiques. Ces résultats suggèrent que des programmes temporaires et bien ciblés devraient être mis en place pour aider ceux qui sont laissés pour compte par le processus de croissance. Le chapitre 3 cherche à savoir si les problèmes d’inégalités de revenus se sont posés ou non dans les périodes d'ajustement budgétaire en Côte d'Ivoire au cours de la période 1980-2014. Nos résultats montrent une amélioration de la performance de croissance après les épisodes de consolidation budgétaire, mais aussi des diminutions de l'écart de revenu dans les périodes suivantes les années d’ajustements budgétaires. Enfin, le chapitre 4 évalue la crédibilité des prévisions budgétaires et leurs effets sur le bien-être social dans les pays de la CEMAC et de l'UEMOA. Nous sommes aboutis aux résultats que l'inefficacité des prévisions budgétaires se produit dans la plupart des cas parce que les erreurs de prévisions sont proportionnelles à la prévision elle-même, mais aussi parce que les erreurs passées sont répétées dans le temps. En outre, une partie des erreurs de prévision des recettes peut s'expliquer par des chocs aléatoires survenus dans l'économie. Par conséquent, ces erreurs dans les prévisions de revenus considérées comme des chocs de politique budgétaire ont un effet négatif sur la croissance inclusive. / The issue of inclusive development in developing countries is at the heart of this thesis. The latter revolves around four chapters on fiscal policy issues and inclusive growth-related matters. Chapter 1 explores how government tax policy affects the inclusiveness of growth in developing countries. Evidence is shown that tax policy affects significantly inclusive growth if and only if the countries have a strong institution quality like low corruption and a good bureaucratic policy. In addition, our result shows that there is an optimal tax beyond which, any increase in the personal income tax rate should have negative impact on inclusive growth. The Chapter 2 examines the effects of government expenditure components on both equity and growth in sub-Saharan countries, especially whether it is possible to design public spending to promote a more equitable society without sacrificing economic growth. We find that investment in infrastructure contributed to more inclusive growth in Sub-sub Saharan African economies than others government spending. These results suggest that temporary and well-targeted programs should be implemented to help those being left out by the growth process. The Chapter 3 investigates whether income inequality matters in the periods of fiscal adjustments in Côte d’Ivoire over the period 1980-2014. The results show an improvement in growth performance after fiscal consolidations episodes, but also income gap decreases in the periods ahead fiscal adjustments. Lastly, Chapter 4 assesses the credibility of fiscal forecasts and their social effects in CEMAC and WAEMU countries. We obtain evidence that the inefficiency of fiscal forecast occurs in most time because the forecast deviation is proportional to the forecast itself, but also because the past errors are repeated in the present. Furthermore, a part of revenue forecast errors can be explained by random shocks to the economy. Therefore, these errors in revenue forecast considered as fiscal policy shocks has a detrimental effect on inclusive growth.