Der Beitrag analysiert die Präzision der Aufkommensprognose wichtiger Bundesabgaben der Jahre 1976 bis 2002 in Österreich. Dadurch wird eine im Schrifttum bestehende Forschungs- und Informationslücke verringert. Eine Prognose wird dazu als präzise verstanden, wenn sie sowohl unverzerrt als auch im Mittel genau ist. Die Prognose des Steueraufkommens auf Bundesebene ist in Österreich gemessen am Bruttogesamtabgabenaufkommen präzise. Dennoch sind aufgrund der unpräzisen Prognosen wichtiger Einzelsteuern Verbesserungen möglich. Als mögliche Ursachen für die Verschätzungen werden die Organisation der Prognose, die verwendeten Prognosemethoden, der Vorsteuerbetrug, Ausgliederungstendenzen aus dem Staatshaushalt und neue kommunale Finanzierungsformen isoliert. Eine Erhöhung der Präzision sollte durch die Kombination mehrerer unabhängiger Prognosen zu einer Gesamtprognose, durch eine stärkere Dokumentation der Prognose, durch die Verwendung univariater Zeitreihenmethoden für die Prognose des Aufkommens an veranlagter Einkommensteuer und an Körperschaftsteuer und durch die Reduktion (Umsatzsteuer) bzw. Erhöhung (Mineralölsteuer) der verwendeten Aufkommenselastizitäten erreicht werden.

The study was conducted to establish what effect life cycle cost forecasting accuracy has on project valuations with special reference to a global mining organisation’s coal business unit in South Africa. The research stemmed from the fact that the organisation identified through its own research in 2009 that its capital projects rarely met the originally budgeted life cycle cost forecasts estimated during the project development stages. These forecasts were generally found to be underestimated. Overrunning of cost budgets in project management terms results in project failure. The study employed two main empirical research sections. The first section took a case study approach where past implemented project results were collated and analysed. The main aim was to determine how close to reality the original life cycle cost estimates were, and secondly how any variances to the originally budgeted costs impacted on the anticipated project value post implementation. Secondly, the study employed in-depth interviews with seven project specialists within the organisation that were also involved in the development stages of the investigated projects. The study concluded that life cycle cost forecasts are very important project business case inputs and that the necessary time and effort should go into developing them so as to ensure that they are as comprehensive and accurate as possible. The sensitivity analysis that was conducted revealed that a coal mining project business case is the second most sensitive to variations in life cycle costs after variations in commodity price. The results indicated that a 20% increase in life cycle costs can destroy an equivalent project value of up to 100%. Accurate life cycle cost forecasting is therefore essential in order to estimate to a certain degree of accuracy the value of a project which in turn will be used to inform capital investment decision making. / MBA, North-West University, Potchefstroom Campus, 2014

The study was conducted to establish what effect life cycle cost forecasting accuracy has on project valuations with special reference to a global mining organisation’s coal business unit in South Africa. The research stemmed from the fact that the organisation identified through its own research in 2009 that its capital projects rarely met the originally budgeted life cycle cost forecasts estimated during the project development stages. These forecasts were generally found to be underestimated. Overrunning of cost budgets in project management terms results in project failure. The study employed two main empirical research sections. The first section took a case study approach where past implemented project results were collated and analysed. The main aim was to determine how close to reality the original life cycle cost estimates were, and secondly how any variances to the originally budgeted costs impacted on the anticipated project value post implementation. Secondly, the study employed in-depth interviews with seven project specialists within the organisation that were also involved in the development stages of the investigated projects. The study concluded that life cycle cost forecasts are very important project business case inputs and that the necessary time and effort should go into developing them so as to ensure that they are as comprehensive and accurate as possible. The sensitivity analysis that was conducted revealed that a coal mining project business case is the second most sensitive to variations in life cycle costs after variations in commodity price. The results indicated that a 20% increase in life cycle costs can destroy an equivalent project value of up to 100%. Accurate life cycle cost forecasting is therefore essential in order to estimate to a certain degree of accuracy the value of a project which in turn will be used to inform capital investment decision making. / MBA, North-West University, Potchefstroom Campus, 2014

This dissertation is composed of two parts, an integrative essay and a set of published papers. The essay and the collection of papers are placed in the context of development and application of time series econometric models in a temporal-axis from 1970s through 2005, with particular focus in the Marketing discipline. The main aim of the integrative essay is on modelling the effects of marketing actions on performance variables, such as sales and market share in competitive markets. Such research required the estimation of two kinds of time series econometric models: multivariate and multiple time series models. I use Autoregressive Integrated Moving Average (ARIMA) intervention models and the Pierce and Haugh statistical test to model the impact of a single marketing instrument, mainly price promotions, to measure own and cross-short term sales effects, and to study asymmetric marketing competition. I develop and apply Vector AutoRegressive (VAR) and Bayesian Vector AutoRegressive (BVAR) models to estimate dynamic relationships in the market and to forecast market share. Especially, BVAR models are advantageous because they contain all relevant dynamic and interactive effects. They accommodate not only classical competitive reaction effects, but also own and cross-market share brand feedback effects and internal decision rules and provided substantively useful insights into the dynamics of demand. The integrative essay is structured in four main parts. The introduction sets the basic ideas behind the published papers, with particular focus on the motivation of the essay, the types of competitive reaction effects analysed, an overview of the time series econometric models in marketing, a short discussion of the basic methodology used in the research and a brief description of the inter-relationships across the published papers and structure of the essay. The discussion is centred on how to model the effects of marketing actions at the selective demand or brand level and at the primary demand or product level. At the brand level I discuss the research contribution of my work on (i) modelling promotional short-term effects of price and non-price actions on sales and market share for consumer packaged goods, with no competition, (ii) how to measure own and cross short-term sales effects of advertising and price, in particular, cross-lead and lag effects, asymmetric sales behaviour and competition without retaliatory actions, in an automobile market, (iii) how to model the marketing-mix effectiveness at the short and long-term on market shares in a car market, (iv) what is the best method to forecast market share, and (v) the study of causal linkages at different time horizons between sales and marketing activity for a particular brand. At the product or commodity level, I propose a way to model the flows of tourists that come from different origins (countries) to the same country-destination as market segments defining the primary demand of a commodity - the product

In the past, a lot of studies about the comparison of exchange rate forecasting models have been carried out. Most of these studies have a similar result which is the random walk model has the best forecasting performance. In this thesis, I want to find a model to beat the random walk model in forecasting the exchange rate. In my study, the vector autoregressive model (VAR), restricted vector autoregressive model (RVAR), vector error correction model (VEC), Bayesian vector autoregressive model are employed in the analysis. These multivariable time series models are compared with the random walk model by evaluating the forecasting accuracy of the exchange rate for three North European countries both in short-term and long-term. For short-term, it can be concluded that the random walk model has the best forecasting accuracy. However, for long-term, the random walk model is beaten. The equal accuracy test proves this phenomenon really exists.

M. Ing. / Undoubtedly, forecasting accuracy presents many advantages to a business, but the opposite is also true for forecasting inaccuracy. This paper is intended to outline the effects of forecasting accuracy on business planning while also investigating factors that affect it. The role of the human element in this regard is also discussed in the report. The study is qualitative in nature with an exploratory approach. A survey and focus group interviews / discussions were conducted so as to achieve the aim of the project. The information obtained from these two methods was used to explore the research questions which in turn were designed to identify the impact of forecasting accuracy and factors that affect this accuracy. The findings of the study indicate that the effect of forecasting accuracy is more significant than commonly perceived. The findings also outline the important factors affecting forecasting accuracy. The basis of this argument is that most of the factors that affect forecasting accuracy can be controlled and are centered on people. Therefore, in order for companies to survive, they must begin improving v their forecasting process and paying more attention to the human element of this process.

Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs. The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other models are better suited for taking into consideration trends and seasonal variation.

<p>Accurate forecasting is a real problem at many companies and that includes Alfa Laval in Lund. Alfa Laval experiences problems forecasting for future raw material demand. Management is aware that the forecasting methods used today can be improved or replaced by others. A change could lead to better forecasting accuracy and lower errors which means less inventory, shorter cycle times and better customer service at lower costs.</p><p>The purpose of this study is to analyze Alfa Laval’s current forecasting models for demand of raw material used for pressed plates, and then determine if other models are better suited for taking into consideration trends and seasonal variation.</p>

The case study has been carried out at SSAB Oxelösund, where a major project is on the way to improve the system support for substance planning, to ensure that the right products are actually produced. Purpose: The purpose of this thesis is to analyze inventory control in intermediate storage within steel industry with data-driven methods and simulation. Where the thesis aims to identify the causes and improvement opportunities of the missing products at the intermediate storage. Method: This study consisted of quantitative research with a deductive approach. Which was done by analyzing the forecast accuracy and erroneous product at rolling, and study how changes in safety stock and batch sizes affect the production with help of discrete event simulation. All analysis was done on secondary data provided by SSAB. Results: Few product groups had high percentual error and absolute error. About one percent of products at rolling had wrong product type. Lastly, the study found that larger batches resulted in less orders being missed. The changes of safety stocks did not have any significant impact on the simulation, due to possible errors under simulation modeling.  Conclusion: Depending on the actual production in other steel industries, the contribution of this thesis only applies to similar production. Suggestions for future research should be to improve the simulation and apply other inventory control methods. In addition, to try other forecasting methods to improve the demand forecasts and try to find a root of the problem for wrong production.

M.Sc. (Statistics)