This is a sandwich thesis / In an interrupted time series (ITS) design, data are collected at multiple time points before and after the implementation of an intervention or program to investigate the effect of the intervention on an outcome of interest. ITS design is often implemented in healthcare settings and is considered the strongest quasi-experimental design in terms of internal and external validity as well as its ability to establish causal relationships. There are several statistical methods that can be used to analyze data from ITS studies. Nevertheless, limitations exist in practical applications, where researchers inappropriately apply the methods, and frequently ignore the assumptions and factors that may influence the optimality of the statistical analysis. Moreover, there is little to no guidance available regarding the application of the various methods, and a standardized framework for analysis of ITS studies does not exist. As such, there is a need to identify and compare existing ITS methods in terms of their strengths and limitations. Their methodological challenges also need to be investigated to inform and direct future research. In light of this, this PhD thesis addresses two main objectives: 1) to conduct a scoping review of the methods that have been employed in the analysis of ITS studies, and 2) to develop improved methods that address a major limitation of the statistical methods frequently used in ITS data analysis. These objectives are addressed in three projects. For the first project, a scoping review of the methods that have been used in analyzing ITS data was conducted, with the focus on ITS applications in health research. The review was based on the Arksey and O’Malley framework and the Joanna Briggs Handbook for scoping reviews. A total of 1389 studies were included in our scoping review. The articles were grouped into methods papers and applications papers based on the focus of the article. For the methods papers, we narratively described the identified methods and discussed their strengths and limitations. The application papers were summarized using frequencies and percentages. We identified some limitations of current methods and provided some recommendations useful in health research. In the second project, we developed and presented an improved method for ITS analysis when the data at each time point are aggregated across several participants, which is the most common case in ITS studies in healthcare settings. We considered the segmented linear regression approach, which our scoping review identified as the most frequently used method in ITS studies. When data are aggregated, heterogeneity is introduced due to variability in the patient population within sites (e.g. healthcare facilities) and this is ignored in the segmented linear regression method. Moreover, statistical uncertainty (imprecision) is introduced in the data because of the sample size (number of participants from whom data are aggregated). Ignoring this variability and uncertainty will likely lead to invalid estimates and loss of statistical power, which in turn leads to erroneous conclusions. Our proposed method incorporates patient variability and sample size as weights in a weighted segmented regression model. We performed extensive simulations and assessed the performance of our method using established performance criteria, such as bias, mean squared error, level and statistical power. We also compared our method with the segmented linear regression approach. The results indicated that the weighted segmented regression was uniformly more precise, less biased and more powerful than the segmented linear regression method. In the third project, we extended the weighted method to multisite ITS studies, where data are aggregated at two levels: across several participants within sites as well as across multiple sites. The extended method incorporates the two levels of heterogeneity using weights, where the weights are defined using patient variability, sample size, number of sites as well as site-to-site variability. This extended weighted regression model, which follows the weighted least squares approach is employed to estimate parameters and perform significance testing. We conducted extensive empirical evaluations using various scenarios generated from a multi-site ITS study and compared the performance of our method with that of the segmented linear regression model as well as a pooled analysis method previously developed for multisite studies. We observed that for most scenarios considered, our method produced estimates with narrower 95% confidence intervals and smaller p-values, indicating that our method is more precise and is associated with more statistical power. In some scenarios, where we considered low levels of heterogeneity, our method and the previously proposed method showed comparable results. In conclusion, this PhD thesis facilitates future ITS research by laying the groundwork for developing standard guidelines for the design and analysis of ITS studies. The proposed improved method for ITS analysis, which is the weighted segmented regression, contributes to the advancement of ITS research and will enable researchers to optimize their analysis, leading to more precise and powerful results. / Thesis / Doctor of Philosophy (PhD)

An electric power system is a complex network consisting of a large number of power generators and consumers interconnected by transmission and distribution lines. One remarkable thing about the electric grid is that there has to be a continuous balance between the amount of electricity generated and consumed at all times. Maintaining this balance is critical for the stable operation of the grid and this task is achieved in the long term, short term and real-time by operating a three-tier wholesale electricity market consisting of the capacity market, the energy market and the ancillary services market respectively. For a demand resource to participate in the energy and the capacity markets, it needs to be able to reduce the power consumption on-demand, whereas to participate in the ancillary services market, the power consumption of the demand resource needs to be varied continuously following the regulation signal sent by the grid operator. This act of changing the demand to help maintain energy balance is called demand response (DR). The dissertation presents novel algorithms and tools to enable residential buildings to participate as demand resources on such markets to provide DR. Residential sector consumes 37% of the total U.S. electricity consumption and a recent consumer survey showed that 88% of consumers are either eager or supportive of advanced technologies for energy efficiency, including demand response. This indicates that residential sector is a very good target for DR. Two broad solutions for residential DR are presented. The first is a set of efficient algorithms that intelligently controls the customers' heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid. The second solution is an extensible residential demand response simulation framework that can help evaluate and experiment with different residential demand response algorithms. One of the algorithms presented in this dissertation is to reduce the aggregated demand of a set of HVACs during a DR event while respecting the customers' comfort requirements. The algorithm is shown to be efficient, simple to implement and is proven to be optimal. The second algorithm helps provide the regulation DR while honoring customer comfort requirements. The algorithm is efficient, simple to implement and is shown to perform well in a range of real-world situations. A case study is presented estimating the monetary benefit that can be obtained by implementing the algorithm in a cluster of 100 typical homes and shows promising result. Finally, the dissertation presents the design of a python-based object-oriented residential DR simulation framework which is easy to extend as needed. The framework supports simulation of thermal dynamics of a residential building and supports house hold appliances such as HVAC, water heater, clothes washer/dryer and dish washer. A case study showing the application of the simulation framework for various DR implementation is presented, which shows that the simulation framework performs well and can be a useful tool for future research in residential DR. / PHD / The total power generation and consumption has to always match in the electric grid. When there is a mismatch because the generation is less than the load, the match can be restored either by increasing the generation or by decreasing the load. Often, during system stress conditions, it is cheaper to decrease certain loads than to increase generation, and this method of achieving power balance is called demand response (DR). Residential sector consumes 37% of the total U.S. electricity consumption and is largely unexplored for demand response purpose, so the focus of the dissertation is on providing solutions to enable residential houses to provide demand response services. This dissertation presents two broad solutions. The first is a set of efficient algorithms that intelligently controls the customers’ heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid while keeping their comfort in mind. The second solution is a simulation software that can help evaluate and experiment with different residential demand response algorithms. The first algorithm is for reducing the collective power consumption of an aggregation of residential HVAC, whereas the second algorithm is for making the collective power follow a signal sent by the grid operators. It is shown that the algorithms presented can intelligently control the HVAC devices such that DR services can be provided to the grid while ensuring that the temperatures of the houses remain within comfortable range. The algorithms can enable demand response service providers to tap into the residential demand response market and earn revenue, while the simulation software can be valuable for future research in this area. The simulation software is simple to use and is designed with extensibility in mind, so adding new features is easy. The software is shown to work well for studying residential building control for demand response purpose and can be a useful tool for future research in residential DR.

No / This paper investigates the impact of corporate risk levels on aggregated, voluntary and mandatory risk disclosures in the annual report narratives of UK non-financial listed companies. We find that firms characterised by higher levels of systematic, financing risks and risk-adjusted returns and those with lower levels of stock return variability are likely to exhibit significantly higher levels of aggregated and voluntary risk disclosures. The results also show that firms of large size, high dividend-yield, high board independence, low (high) insider (outsider) ownership, and effective audit environments are likely to exhibit higher levels of aggregated and voluntary risk disclosures than other firms. Similarly, mandatory risk disclosures are influenced positively by firm size, dividend-yield and board independence and negatively by high leverage. The results suggest that managers of firms exhibiting greater compliance with mandatory regulations have a greater propensity to make voluntary risk disclosures. When we distinguish between high- and low-risk firms, we find that high-risk firms appear to be more sensitive to underlying risk levels, resulting in more disclosure of both voluntary and mandatory risk information than in the case of low-risk firms. The results generally support the present UK emphasis on encouraging rather than mandating risk disclosure. Nevertheless, under this regime, the voluntary risk disclosures of some firms, e.g., those characterised by higher-volatility market returns, do not reflect their underlying risk levels.

The thesis is focused on creating of aggregated items and pricing appreciation of the group works. This thesis deals with in its early life cycle of building works and defines at what stage it is advisable to use the aggregate cost. The following part is aimed at budget in the aggregated prices and creating aggregated prices. In practical part there is methodology designed to compile aggregate price of building construction. The aim of methodology is to propose evaluation process of coherent construction work groups. The proposed methodology will be used to value case study of family house.

Equity Risk Premium (ERP) is the cornerstone in Financial Economics. It is a basic requirement in stock valuation, evaluation of portfolio performance and asset allocation. For the last decades, several studies have attempted to investigate the relationship between macroeconomic drivers of ERP. In this work, I empirically investigate the macroeconomic determinants of UK ERP. For this I parsimoniously cover a large body of literature stemming from ERP puzzle. I motivate the empirical investigation based on three mutually exclusive theoretical lenses. The thesis is organised in the journal paper format. In the first paper I review the literature on ERP over the past twenty-eight years. In particular, the aim of the paper is three fold. First, to review the methods and techniques, proposed by the literature to estimate ERP. Second, to review the literature that attempts to resolve the ERP puzzle, first coined by Mehra and Prescott (1985), by exploring five different types of modifications to the standard utility framework. And third, to review the literature that investigates and develops relationship between ERP and various macroeconomic and market factors in domestic and international context. I find that ERP puzzle is still a puzzle, within the universe of standard power utility framework and Consumption Capital Asset Pricing Model, a conclusion which is in line with Kocherlakota (1996) and Mehra (2003). In the second paper, I investigate the impact of structural monetary policy shocks on ex-post ERP. More specifically, the aim of this paper is to investigate the whether the response of UK ERP is different to the structural monetary policy shocks, before and after the implementation of Quantitative Easing in the UK. I find that monetary policy shocks negatively affect the ERP at aggregate level. However, at the sectoral level, the magnitude of the response is heterogeneous. Further, monetary policy shocks have a significant negative (positive) impact on the ERP before (after) the implementation of Quantitative Easing (QE). The empirical evidence provided in the paper sheds light on the equity market’s asymmetric response to the Bank of England’s monetary policy before and after the monetary stimulus. In the third paper I examine the impact of aggregate and disaggregate consumption shocks on the ex-post ERP of various FTSE indices and the 25 Fama-French style value-weighted portfolios, constructed on the basis of size and book-to-market characteristics. I extract consumption shocks using Structural Vector Autoregression (SVAR) and investigate its time-series and cross-sectional implications for ERP in the UK. These structural consumption shocks represent deviation of agent’s actual consumption path from its theoretically expected path. Aggregate consumption shocks seem to explain significant time variation in the ERP. At disaggregated level, when the actual consumption is less than expected, the ERP rises. Durable and Semi-durable consumption shocks have a greater impact on the ERP than non-durable consumption shocks. In the fourth and final paper I investigate the impact of short and long term market implied volatility on the UK ERP. I also examine the pricing implications of innovations to short and long term implied market volatility in the cross-section of stocks returns. I find that both the short and the long term implied volatility have significant negative impact on the aggregate ERP, while at sectoral level the impact is heterogeneous. I find both short and long term volatility is priced negatively indicating that (i) investors care both short and long term market implied volatility (ii) investors are ready to pay for insurance against these risks.

O planejamento da operação energética em sistemas hidrotérmicos de potência com um único reservatório tem como objetivo determinar a participação de usinas hidrelétricas e térmicas de forma a garantir o suprimento de energia demandada ao menor custo operacional possível, dentro de restrições físicas e técnicas do modelo. Alguns fatores tornam a solução deste problema bastante complexa destacando a não linearidade e a não separabilidade temporal aditiva. O objetivo deste trabalho é apresentar uma nova abordagem com tratamento agregado das afluências, descrevendo uma nova caracterização das distribuições de probabilidades e um novo modelo para a programação dinâmica estocástica markoviana. Nesse novo modelo da programação dinâmica estocástica markoviana, agregações plurimensais de vazões são utilizadas como entrada em um modelo de programação dinâmica estocástica markoviana modificado para discretizações temporais plurimensais. A nova abordagem proposta foi simulada em diferentes usinas hidrelétricas brasileiras localizadas em diferentes regiões geográficas e sob diferentes regimes hidrológicos. Os resultados das simulações feitas com a utilização deste novo modelo são apresentados e comparados ao modelo de programação dinâmica estocástica markoviana mensal, atualmente utilizado no setor elétrico brasileiro, com economia de custos relativas superiores a 10% em alguns casos. / The energetic operation planning on hydrothermal power systems with a single reservoir aims to determine the participation of hydroelectric power plants and thermal power plants to guaranty supply of energy demanded with the smallest possible cost, under physical and technical model boundaries. Some points became the solution of this problem complex, highlighting the non linearity and the additive non time separability. The objective of this paper is show the new approach with aggregated inflows, describing a new probability distributions featuring and a new model for the markovian stochastic dynamic programming. On this new model of markovian stochastic dynamic programming, multi monthly inflow aggregations are used as input in a model of markovian stochastic dynamic programming modified for multi months discretizations. The new approach proposed was simulated on differents Brazilian hydroelectric power plants located on different regions and under different hydrologic regime. The results of simulations using this new model are presented and compared to the model of monthly markovian dynamic programming, nowadays used on the Brazilian electrical sector, with relatives economic savings up to 10% in some cases.

O planejamento da operação energética em sistemas hidrotérmicos de potência com um único reservatório tem como objetivo determinar a participação de usinas hidrelétricas e térmicas de forma a garantir o suprimento de energia demandada ao menor custo operacional possível, dentro de restrições físicas e técnicas do modelo. Alguns fatores tornam a solução deste problema bastante complexa destacando a não linearidade e a não separabilidade temporal aditiva. O objetivo deste trabalho é apresentar uma nova abordagem com tratamento agregado das afluências, descrevendo uma nova caracterização das distribuições de probabilidades e um novo modelo para a programação dinâmica estocástica markoviana. Nesse novo modelo da programação dinâmica estocástica markoviana, agregações plurimensais de vazões são utilizadas como entrada em um modelo de programação dinâmica estocástica markoviana modificado para discretizações temporais plurimensais. A nova abordagem proposta foi simulada em diferentes usinas hidrelétricas brasileiras localizadas em diferentes regiões geográficas e sob diferentes regimes hidrológicos. Os resultados das simulações feitas com a utilização deste novo modelo são apresentados e comparados ao modelo de programação dinâmica estocástica markoviana mensal, atualmente utilizado no setor elétrico brasileiro, com economia de custos relativas superiores a 10% em alguns casos. / The energetic operation planning on hydrothermal power systems with a single reservoir aims to determine the participation of hydroelectric power plants and thermal power plants to guaranty supply of energy demanded with the smallest possible cost, under physical and technical model boundaries. Some points became the solution of this problem complex, highlighting the non linearity and the additive non time separability. The objective of this paper is show the new approach with aggregated inflows, describing a new probability distributions featuring and a new model for the markovian stochastic dynamic programming. On this new model of markovian stochastic dynamic programming, multi monthly inflow aggregations are used as input in a model of markovian stochastic dynamic programming modified for multi months discretizations. The new approach proposed was simulated on differents Brazilian hydroelectric power plants located on different regions and under different hydrologic regime. The results of simulations using this new model are presented and compared to the model of monthly markovian dynamic programming, nowadays used on the Brazilian electrical sector, with relatives economic savings up to 10% in some cases.

In this study a population of 60 family owned businesses acquired in 1971 are analysed over a period of 15 years. The firms are followed historically for four years before and ten years after the merger. The aim is to identify dominating processes and behaviour in different variables during the period 1967-81. This will be done through the following: - on an aggregated level, identify and analyse characteristic processes and patterns by the acquired businesses before and after the acquisition - on an aggregated level compare the pre- and post-merger performances - on an individual business level illustrate, validate and theoretically interpret results and conclusions. Primarily this study has not a theoretical but an empirical point of departure. A working paradigm is that the "confrontation" between the firms 1 "external environment and internal resources" results in dynamics having an impact on the firms. The processes are classified in taxonomies/typologies, in an attempt to answer what has happened. Interpreting the forces behind the development is the attempt to answer why it has happened. The empirical data was collected through three different surveys resulting in quantitative and qualitative observations combined in different perspectives in a multimethological approach. The first is economic data (sales, financial ratios etc) gathered from the firms' external account statements. However, several firms were found to have gone bankrupt, closed down etc. This initiated a second, follow-up study, which had a longitudinal "geography of enterprise" approach and was implemented through a telephone inquiry. The third collection is a case-study of five firms from the population carried out by discussions with representatives of the merging companies. The merged businesses turned out to be extremes compared to branch characteristics respectively. Refinements of the patterns made it possible to construct a three-dimensional typology showing four principal processes. Ten years after the merger there followed five principal spatial and institutional changes. Closures, removals from community and amalgamation with group companies, reduction to production units only, the joining of premises with group companies in the same community and relatively "indépendant" affiliations. One third of the population have been closed down or removed. One half do not exist as "indépendant units". Only one third have escaped larger infringement. Thirty businesses have once more been acquired. Some more than once. When comparing the pre- and post-merger performances, a convergence phenomenon was identified. Oscillating and deviating pre-merger trends later converged towards standard variable values and equilibrium, searching for an optimum group course. The different changes and restructuring activities conducted after the acquisitions, can be summarized in three principal post-merger processes: - liquidation and adjustment of output capacity to market demand. - reorientation through new product and market combinations. - growth and development through "multiplying by splitting" and emancipation of expansion potential. / <p>Diss. Umeå : Univ., 1986</p> / digitalisering@umu

Reliability worth analysis is an important tool for distribution systems planning and operations. The interruption cost model used in the analysis directly affects the accuracy of the reliability worth evaluation. In this dissertation, the reliability worth analysis was dealt with two interruption cost models including an average or aggregated model (AAM), and a probabilistic distribution model (PDM) in two phases. In the first phase, the dissertation presents a reliability cost model based AAM for distribution system planning. The reliability cost model has been derived as a linear function of line flows for evaluating the outages. The objective is to minimize the total cost including the outage cost, feeder resistive loss, and fixed investment cost. The Evolutionary Programming (EP) was used to solve the very complicated mixed-integer, highly non-linear, and non-differential problem. A real distribution network was modeled as the sample system for tests. There is also a higher opportunity to obtain the global optimum during the EP process. In the second phase, the interruption cost model PDM was proposed by using the radial basis function (RBF) neural network with orthogonal least-squares (OLS) learning method. The residential and industrial interruption costs in PDM were integrated by the proposed neural network technique. A Monte-Carlo time sequential simulation technique was adopted for worth assessment. The technique is tested by evaluating the reliability worth of a Taipower system for the installation of disconnected switches, lateral fuses, transformers and alternative supplies. The results show that the two cost models result in very different interruption costs, and PDM may be more realistic in modeling the system.

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