Table of Content
1.1 TIME SERIES
1.2 SIMPLE REGRESSION
1.3 MULTIPLE REGRESSION
Faculty of Economics and Business Administration
In all aspects of our daily live, we seek to anticipate or forecast events. Especially organizations and companies are engaged in producing and using a full range of different economic forecasts. The widespread usefulness and application of forecasting systems and statistical and econometric modeling techniques has become solidly entrenched. Being aware of this fact, has led to a fundamental need for better quantitative analysis and business planning. Private and public sectors alike have found it both practical and essential to employ more rigorous analytical framework. Accordingly, more sophisticated forecasting techniques to enhance the level of predictability and confidence are required to foresee future events.
The need for such forecasts arises because people are taking positions and enter into commitments about the future. Therefore, a need to form a view about the possible future consequences of these positions or commitments exists. Thus, in economic and business life, forecasts are essential, and errors can be very costly. According to those facts, now the question arises: What factors influence the accuracy of forecasts? In the following paper, three different forecasting methods will be explained and evaluated according to their accuracy.
There exist diverse techniques of forecasting; those methods may be either formal or intuitive. Nevertheless, as the future is unknown, all forecasting systems rest ultimately on learning from the past. There exist naïve processes extrapolating the past in a simple way. But those will be prone to error when the world changes. More sophisticated methods seek to foresee change by understanding the source of past changes, and therefore incorporate change in the forecast. The standard output from macro models is a central forecast, that is, a prediction of the most likely path for the variables of interest. But these central forecasts are subject to appreciable uncertainty, and this needs to be taken into account in using them. One way to do so is to associate with the central forecasts an estimate of their possible error.
Accordingly, systems to foresee the future are attracting more attention then ever before. In the last decades, existing forecasting methods have been improved and more and more emphasis has been laid on more revised techniques and more responsible behaviour according to the use of those systems. Especially in the field of decision-making processes, producing forecasts is of crucial importance. This fact generated awareness on the corporate level. It turned out that it is not enough to wish to do well, but one has to know how to do
well. Thus, it is reasonable to say that the more uncertain the circumstances and conditions are, the higher the interest in these methods is. On the other hand, the more uncertain the conditions, the lower the accuracy of forecasting. Thus, we have a phenomenon where the more interest in one activity is shown, the less satisfying are the results being produced. Two common pitfalls are, to either overestimate the past, thus creating a stumbling block for understanding the future, or mystifying the significance of the future, refusing to see that it was nothing other than an extension of the past.
Anticipating the future deals with phenomena whose future shapes are predicted by using the past values or the matters related to them. To efficiently record this data, specific methods have to be applied, with the purpose being to make relevant decisions. Thus, first one has to understand what the phenomenon is, gather the data and evaluate the affect on the forecast. The method chosen then depends on the decisions one wants to make on the basis of our forecast. Knowing these answers, make the forecast very accurate, reliable and it will present a sound basis for anticipating the future. In practice, there exist lots of such factors one makes use of in forecasting future changes. An example might be the past pattern of sales. Past experience combined with detailed past sales by product line, geographical pattern, and type of customer help predicting future sales. Because sales depend on the strength and actions of competitors, a company should consider, in order of forecasting sales, the likely strategies and reactions of competitors. Additionally, market research studies gather information about market conditions and customer preferences and thus help forecasting the future. Also, in both state and local governments, planning for revenues, expenditures, and future needs for public goods and services necessitate rigorous analysis of past trends and current conditions. These are followed by forecasts of future demands and resources needed to satisfy them. In private industry, finance, and casino gaming, better and more timely estimates of future business patterns and demands for services will provide immeasurable benefits to better planning, control of manufacturing and inventory levels, staffing levels and hiring patterns, and the provision of necessary services.
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- Antje Artmann (Author), 2001, Forecasting - What factors influence the accuracy of forecasts?, Munich, GRIN Verlag, https://www.grin.com/document/4535