This thesis includes three essays on topics related to the predictability of market returns. I investigate i) the predictability of market returns from an adjusted version of cay ratio (cayadj), ii) the explanatory power of a conditional version of the consumption-CAPM which uses predictor variables to scale the pricing kernel, and iii) whether information about future market returns can be extracted from a large set of commodity data. The first essay studies the predictive ability of cayadj . In Campbell and Mankiw (1989), the consumption-wealth ratio is represented as a linear function of expected market returns and consumption growth. Lettau and Ludvigson (2001) build their study on Campbell and Mankiw (1989) and estimate the ratio cay as a proxy for the consumption-wealth ratio, assuming that the fluctuation in expected consumption growth is constant. I argue that the variation in expected consumption growth should be taken into consideration and propose adjusting the cay ratio by the estimates of expected consumption growth. After making the adjustment, I find that the predictabilities of market returns, particularly at annual, bi-annual, and tri-annual horizons, are greatly improved. The significant predictive ability of cayadj still holds in out-of-sample forecasts. The second essay examines the performance of a conditional version of the consumption-CAPM, where conditioning variables are used to scale the pricing kernel. I find that incorporating the conditioning information into the standard consumption-CAPM greatly improves the performance in asset pricing tests, particularly when using cayadj as the conditioning variable. Moreover, the performance of conditional consumption-CAPM is as good as the ultimate consumption risk model (Parker and Julliard, 2005) which measures the consumption risk over several quarters. Further tests show that the factors of conditional consumption-CAPM drive out the consumption risk measured over several quarters. The third essay evaluates the ability of lagged commodity returns to forecast market returns. In order to exploit the predictive information from a relatively large amount of commodity returns, I apply the partial-least-squares (PLS) method pioneered by Kelly and Pruitt (2013). I find that the commodity returns measured over previous twelve months show strong predictive power in monthly and three-month forecasts, in-sample and out-of-sample. The findings are robust to controlling for risk factors such as momentum, Fama-French three factors and industry returns previously identified to be significant predictors of market returns (Hong, Torous and Valkanov, 2007).

In the field of aviation forecasting is used, among other things, to determine the number of passengers to expect for each flight. This is beneficial in the practice of revenue management, as the forecast is used as a base when setting the price for each flight. In this study, a forecast evaluation has been done on seven different routes with a total of 61 different flights, using four different methods. These are: Mean Absolute Scaled Error (MASE), Mean Absolute Percentage Error (MAPE), Tracking Signal, and a goodness of fit test to determine if the forecast errors are normally distributed. The MASE has been used to determine if the passenger forecasts are better or worse than a naïve forecast, while the MAPE provides an error value for internal comparisons between the flights. The Tracking Signal and the normal distribution test have been used in order to determine whether a flight has bias or not towards under- or overforecasting. The results point towards a general underforecast across all studied flights. A total of 89 % of the forecasts perform better than the naïve forecast, with an average MASE value of 0,78. As such, the forecast accuracy is better than that of the naïve forecast. There are however large error values among the observed flights, affecting the MAPE average. The MAPE average is 38,53 % while the median is 30,60 %. The measure can be used for internal comparisons, and one such way is to use the average value as a benchmark in order to focus on improving those forecasts with a higher than average MAPE. The authors have found that the MASE and MAPE are useful in measuring forecast accuracy and as such the recommendation of the authors is that these two error measures can be used together to evaluate forecast accuracy at frequent intervals. In addition to this there is value in examining the error distribution in conjunction with the Mean Error when searching for bias, as this will indicate if there is systematic error present.

The majority of the existing photovoltaic (PV) systems are dimensioned in such a way that no or only a small part of the production exceeds the buildings internal consumption. This is done because sold electricity to the grid has a lower economic value than if used internally in the building. Therefore commercial buildings, with high consumption during sunny hours, are to prefer when installing PV. Implementing a battery energy storage system in these facilities can lead to higher self consumption of the PV energy and reduced electricity bills. To take full advantage of this potential it requires optimal management of the battery. In this study an optimized battery algorithm was developed to show the full potential a perfectly managed battery can have to reduce cost of electricity within commercial buildings. There are three main charge and discharge patterns for a battery which can reduce the cost on the electricity bill: 1) Charge and discharge at different prices, 2) peak shaving and 3) overproduced PV is stored for later use. By utilizing a battery in an optimal way to reduce the costs as much as possible, batteries will reach break even at battery prices between 1350-2100 SEK/kWh, depending on which scenario evaluated. By implementing forecast based desicion-making, in which the battery operation is optimized with respect to PV and consumption forecasts, the system profitability declines rapidly, especially when using a consumption forecast. A real system would probably profit from basing the battery operation on both forecasts and real time measurements.

The Sahel region located in Western Africa is well known for its high rainfall variability. Severe and recurring droughts have plagued the region during the last three decades of the 20th century, while heavy precipitation events (with return periods of up to 1,200 years) were reported between 2007 and 2014. Vulnerability to extreme events is partly due to the fact that people are not prepared to cope with them. It would be of great benefit to farmers if information about the magnitudes of precipitation and streamflow in the upcoming rainy season were available a few months before; they could then switch to more adapted crops and farm management systems if required. Such information would also be useful for other sectors of the economy, such as hydropower production, domestic/industrial water consumption, fishing and navigation. A logical solution to the above problem would be seasonal rainfall and streamflow forecasting, which would allow to generate knowledge about the upcoming rainy season based on information available before it's beginning. The research in this thesis sought to improve seasonal rainfall and streamflow forecasting in the Sahel by developing statistical rainfall and streamflow seasonal forecasting models. Sea surface temperature (SST) were used as pools of predictor. The developed method allowed for a systematic search of the best period to calculate the predictor before it was used to predict average rainfall or streamflow over the upcoming rainy season. Eight statistical models consisted of various statistical methods including linear and polynomial regressions were developed in this study. Two main approaches for seasonal streamflow forecasting were developed here: 1) A two steps streamflow forecasting approach (called the indirect method) which first linked the average SST over a period prior to the date of forecast to average rainfall amount in the upcoming rainy season using the eight statistical models, then linked the rainfall amount to streamflow using a rainfall-runoff model (Soil and Water Assessment Tool (SWAT)). In this approach, the forecasted rainfall was disaggregated to daily time step using a simple approach (the fragment method) before being fed into SWAT. 2) A one step streamflow forecasting approach (called as the direct method) which linked the average SST over a period prior to the date of forecast to the average streamflow in the upcoming rainy season using the eight statistical models. To decrease the uncertainty due to model selection, Bayesian Model Averaging (BMA) was also applied. This method is able to explore the possibility of combining all available potential predictors (instead of selecting one based on an arbitrary criterion). The BMA is also capability to produce the probability density of the forecast which allows end-users to visualize the density of expected value and assess the level of uncertainty of the generated forecast. Finally, the economic value of forecast system was estimated using a simple economic approach (the cost/loss ratio method). Each developed method was evaluated using three well known model efficiency criteria: the Nash-Sutcliffe coefficient (Ef), the coefficient of determination (R2) and the Hit score (H). The proposed models showed equivalent or better rainfall forecasting skills than most research conducted in the Sahel region. The linear model driven by the Pacific SST produced the best rainfall forecasts (Ef = 0.82, R2 = 0.83, and H = 82%) at a lead time of up to 12 months. The rainfall forecasting model based on polynomial regression and forced by the Atlantic ocean SST can be used using a lead time of up to 5 months and had a slightly lower performance (Ef = 0.80, R2 = 0.81, and H = 82%). Despite the fact that the natural relationship between rainfall and SST is nonlinear, this study found that good results can be achieved using linear models. For streamflow forecasting, the direct method using polynomial regression performed slightly better than the indirect method (Ef = 0.74, R2 = 0.76, and H = 84% for the direct method; Ef = 0.70, R2 = 0.69, and H = 77% for the indirect method). The direct method was driven by the Pacific SST and had five months lead time. The indirect method was driven by the Atlantic SST and had six months lead time. No significant difference was found in terms of performance between BMA and the linear regression models based on a single predictor for streamflow forecasting. However, BMA was able to provide a probabilistic forecast that accounts for model selection uncertainty, while the linear regression model had a longer lead time. The economic value of forecasts developed using the direct and indirect methods were estimated using the cost/loss ratio method. It was found that the direct method had a better value than the indirect method. The value of the forecast declined with higher return periods for all methods. Results also showed that for the particular watershed under investigation, the direct method provided a better information for flood protection. This research has demonstrated the possibility of decent seasonal streamflow forecasting in the Sirba watershed, using the tropical Pacific and Atlantic SSTs as predictors.The findings of this study can be used to improve the performance of seasonal streamflow forecasting in the Sahel. A package implementing the statistical models developed in this study was developed so that end users can apply them for seasonal rainfall or streamflow forecasting in any region they are interested in, and using any predictor they may want to try.

This thesis is composed of free main chapters. The first two chapters of is a theoretical part. The first chapter is devoted to the theory of economic policy and analysis of economic indicators. The second chapter concerns the econometric theory and describes vector autoregression models theory and econometric forecasting. In the third, practical part, aims to find out with the help of real data of the Czech economy impacts of alternative VAT rates on selected indicators of the czech economy, these indicators are gross domestic product, unemployment rate and consumer price index. As a tool to determine the impact of using models and vector autoregression method scenarios.

[pt] Esta dissertação teve como objetivo o desenvolvimento e a implantação de uma metodologia de previsão de vendas e dimensionamento de lotes de encomenda num distribuidor atacadista de produtos químicos. Para tanto, abordou técnicas quantitativas de previsão de demanda de curto prazo e medidas de variância dos erros de previsão a fim de suportar decisões empresariais na aplicação da metodologia, capazes de projetar padrões passados num cenário futuro. A aplicação da metodologia possibilitará à empresa a formalização de um processo atualmente subjetivo, outorgando maior precisão na previsão de vendas, redução de custos com estoque e uma base mais concreta para alocação de recursos financeiros. / [en] This thesis has as objective the developing and implantation of a methodology for forecasting sales and design of batch ordering in a wholesale distributor of chemical products. For this purpose, it approached short term quantitative techniques of demand forecast and measures of variance of forecast errors in order to support business decisions on the application of the methodology, able to design past patterns on a future scenario. The application of the methodology will enable the company the formalization of a process currently subjective, granting a greater accuracy in forecasting sales, reduction in the inventory costs and a more concrete basis for resource allocation.

Made available in DSpace on 2016-05-02T13:53:50Z (GMT). No. of bitstreams: 1 JulianoCesarSasseronDissertacao.pdf: 912677 bytes, checksum: f2d63baed22140f9170ad66e591200ff (MD5) Previous issue date: 2013-04-12 / In order to evaluate the best crop estimation model (harvest of 10 linear meters, ear sampling, density of grains per ear) and the spacing effect between the planting line (0.50 and 0.80 m) on the productivity of maize grain (Zea mays L.) in a same environment, field experiments were conducted in the municipality of Carmo do Rio Claro, in the southern State of Minas Gerais, from August 14th, 2011 to March 6th, 2012. The outlining used was a randomized block banded with 3x2 factorial. We evaluated the estimated productivity in each model. There were differences between the estimation models, and the closest models to the actual production were obtained by harvesting 10 linear meters and by ear sampling. Also there was effect of spacing between lines, obtaining the maximum yield of grain with reduced spacing, i.e. 0.50 m. . / Com objetivo de avaliar o melhor modelo de previsão de safra (colheita de 10 metros lineares, amostragem de espiga, densidade de grãos por espiga) e o efeito do espaçamento entre linhas de plantio (0,50 e 0,80 m), sobre a produtividade de grãos de milho (Zea mays L.) em um mesmo ambiente, foram conduzidos experimentos de campo no município de Carmo do Rio Claro, no sul do estado de Minas Gerais no período de 14 de agosto de 2011 a 06 de março de 2012. O delineamento utilizado foi em blocos casualisados em faixas com fatorial 3x2. Foi avaliada a produtividade estimada em cada modelo. Houve diferença entre os métodos de previsão, sendo que os mais próximos da produção real foram obtidos através da colheita de 10 metros lineares e pela amostragem de espiga. Também houve efeito do espaçamento entre linhas, obtendo o máximo rendimento de grãos com o espaçamento reduzido, ou seja, 0,50 m.

As intensas evoluções do mercado forçam as empresas a adequarem seus negócios abordando novos meios estratégicos para se manterem competitivas diante de seus concorrentes. O objetivo do trabalho é analisar novos arranjos produtivos encontrados no segmento automobilístico que se destacam por necessitar de uma forma cada vez melhor de relacionamento com seus clientes através de uma modelagem do sistema para disponibilizar a visibilidade do estoque nos diversos estágios como: antes da produção, durante a produção, durante o transito, quando estocados nos pátios e até possibilitando a colocação de pedido para itens que não estão disponíveis nos estágios anteriores citados. O trabalho também aborda um estudo de previsão de demanda que utiliza modelos matemáticos sobre a média da quantidade de vendas apuradas em determinado período de tempo e um comparativo com o método dos mínimos quadrados. A consequência do engajamento dos pedidos nas etapas anteriores da real disponibilidade física dos produtos para comercialização e entrega, propõe otimizar os níveis de estoque em quantidades mais baixas do que o modelo atual. Os cenários são simulados e visam contribuir como ferramenta para demonstrar a viabilidade de um novo modelo de política de comercialização dos produtos. / The intense Market developments force companies adapt their business by addressing new strategic means to remain competitive in the face of its competitors. The objective is to analyze new production arrangements found in the automotive sector that need to stand out by an ever better relationship with its customers through a modeling system to provide inventory visibility in different stages such as: before production, during production, during transit, when stored in the courtyards and even allowing for the placement of order for items that are not available in earlier stages cited. The paper also discusses a study of demand forecasting which uses mathematical models to average volumes calculated requests in a given time period and a comparison with the least squares method. The result of the engagement of requests in previous stages of actual physical availability of products for sale and delivery, proposes optimize inventory levels in amounts lower than the current model. The scenarios are simulated and aim to contribute as a tool to demonstrate the feasibility of a new model of political marketing.

The South African Weather Service rainfall seasonal forecasts are verified for the period of January-February-March to October-November-December 1998-2004. These forecasts are compiled using different models from different institutions. Probability seasonal forecasts can be evaluated using different skill measures, but in this study the Ranked Probability Skill Score (RPSS), Reliability Diagram (RD) and Relative Operating Characteristics (ROC) are used. The RPSS is presented in the form of maps whereas the RD and ROC are analyses are presented in the form of graphs. The aim of the study is to present skill estimates of operational seasonal forecasts issued at South African Weather Service A limited number of forecasts show positive RPSS value throughout the validation period. From RD and ROC analysis, there is no skill in predicting the normal category as compared to below-normal and above-normal categories. Notwithstanding, the frequency diagrams show that the normal category was often given a large weight in the operational forecasts. The value of verifying seasonal forecast accuracy from the user’s perspective is important. The understanding of seasonal forecast performance helps decision makers to determine when and how to respond to expected climate anomalies. Therefore the frequent update of the seasonal forecast verification is important in order to help Users make better decisions. Copyright / Dissertation (MSc)--University of Pretoria, 2008. / Geography, Geoinformatics and Meteorology / Unrestricted

This study aimed to combine avalanche statistics with climate change models in orderto assess how a change in precipitation patterns, snow depth and snow density canimpact the avalanche danger in Jämtland, Sweden. Existing climate model reportsfrom SMHI and the Swedish county administration offices were used, and avalanchestatistics were compiled using data from SEPA from 2017 to 2020.It was found that days with moderate avalanche danger are most common (56 %) andthat a lot of days the danger is considerable (33%). The most common avalancheproblem is wind-drifted snow. The results show that wind velocity of 8 m/s isconnected to considerable danger in over 80 % of cases and for 10 m/s even 90 %. Dailyprecipitation of 3 mm or more is also connected to considerable danger on 81% of days;independently of wind. Towards the end of the 21st century, precipitation in Jämtland in winter and spring isexpected to increase by up to 50 % whereas snow depth is likely to decrease so muchthat many places will not reach 100 cm anymore (under the conservative RCP8.5scenario). While the snow depth comes with shortened winter seasons, increasedprecipitation is shown to increase the danger level. It is therefore likely that theavalanche forecasting period will be shortened but intensified in terms of danger.In conclusion, this study confirms again that avalanches are difficult to predict, andthat climate change will not make this easier. This makes it essential to keep updatingthe avalanche information that is available not just in Sweden but across the globe.However, the results are inconclusive due to the shortage of data and due to thecomplex combinations of factors that can impact avalanche danger. Further researchis required. / <p>2021-07-02</p>