Question No. 4 - MMPC-005 - Quantitative Analysis for Managerial Applications

Solutions to Assignments

                            MBA and MBA (Banking & Finance)

                    MMPC-005 - Quantitative Analysis for  Managerial 

                                                Applications

Question No. 4. 

“Time series analysis is one of the most powerful methods in use, especially for short-term forecasting purposes.” Comment on the statement. 

Time series analysis is one of the most powerful methods in use, especially for short term forecasting purposes. From the historical data one attempts to obtain the underlying pattern so that a suitable model of the process can be developed, which is then used for purposes of forecasting or studying the internal structure of the process as a whole. We have already seen in Unit 17 that a variety of methods such as subjective methods, moving averages and exponential smoothing, regression methods, causal models and time-series analysis are available for forecasting. Time series analysis looks for the dependence between values in a time series (a set of values recorded at equal time intervals) with a view to accurately identify the underlying pattern of the data. In the case of quantitative methods of forecasting, each technique makes explicit assumptions about the underlying pattern. 

For instance, in using regression models we had first to make a guess on whether a linear or parabolic model should be chosen and only then could we proceed with the estimation of parameters and model development. We could rely on mere visual inspection of the data or its graphical plot to make the best choice of the underlying model. However, such guess work, through not uncommon, is unlikely to yield very accurate or reliable results. In time series analysis, a systematic attempt is made to identify and isolate different kinds of patterns in the data. The four kinds of patterns that are most frequently encountered are horizontal, non-stationary (trend or growth), seasonal and cyclical. Generally, a random or noise component is also superimposed. We shall first examine the method of decomposition wherein a model of the time series in terms of these patterns can be developed. This can then be used for forecasting purposes as illustrated through an example. 

Finally the question of the choice of a forecasting method is taken up. Characteristics of various methods are summarised along with likely situations where these may be applied. Of course, considerations of cost and accuracy desired in the forecast play a very important role in the choice.