• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.105-117
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• 2007

In order to succeed in a bid or development, the project manager should estimate its cost and schedule more accurately in the early stage of the project. Usually, the nominal schedule of most projects can be derived from rule of thumb, first-order estimation practice, or ball-park schedule estimation table. But the rule-of-thumb models for the nominal schedule estimation are so various, and the first-order estimation practice does not provide sufficient information. So they do not help much to decide on the proper development effort and schedule for a particular size of project. This paper presents a statistical regression model for deciding the development effort and schedule of a project using the ball-park schedule estimation table. First, we have redefined such words suggested in the ballpark schedule estimation table as shortest possible schedule, efficient schedule and nominal schedule, Next, we have investigated the relationship between the development effort and the schedule. Finally, we have suggested a model for estimating the development effort and the more accurate schedule of such particular sizes of software as are not presented in the ball-park schedule estimation table. The experimental results show that our proposed regression analysis model decreases the mean magnitude of relative error by 2% at maximum. Also this model can estimated the development effort and schedule for a particular size of software.

• KDI Journal of Economic Policy
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• v.31 no.1
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• pp.169-206
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• 2009

This study stems from a question, "How should we understand the pattern of the Korean economy after the 1990s?" Among various analytic methods applicable, this study chooses a Structural Vector Autoregression (SVAR) with long-run restrictions, identifies diverse impacts that gave rise to the current status of the Korean economy, and differentiates relative contributions of those impacts. To that end, SVAR is applied to four economic models; Blanchard and Quah (1989)'s 2-variable model, its 3-variable extensions, and the two other New Keynesian type linear models modified from Stock and Watson (2002). Especially, the latter two models are devised to reflect the recent transitions in the determination of foreign exchange rate (from a fixed rate regime to a flexible rate one) as well as the monetary policy rule (from aggregate targeting to inflation targeting). When organizing the assumed results in the form of impulse response and forecasting error variance decomposition, two common denominators are found as follows. First, changes in the rate of economic growth are mainly attributable to the impact on productivity, and such trend has grown strong since the 2000s, which indicates that Korea's economic growth since the 2000s has been closely associated with its potential growth rate. Second, the magnitude or consistency of impact responses tends to have subsided since the 2000s. Given Korea's high dependence on trade, it is possible that low interest rates, low inflation, steady growth, and the economic emergence of China as a world player have helped secure capital and demand for export and import, which therefore might reduced the impact of each sector on overall economic status. Despite the fact that a diverse mixture of models and impacts has been used for analysis, always two common findings are observed in the result. Therefore, it can be concluded that the decreased rate of economic growth of Korea since 2000 appears to be on the same track as the decrease in Korea's potential growth rate. The contents of this paper are constructed as follows: The second section observes the recent trend of the economic development of Korea and related Korean articles, which might help in clearly defining the scope and analytic methodology of this study. The third section provides an analysis model to be used in this study, which is Structural VAR as mentioned above. Variables used, estimation equations, and identification conditions of impacts are explained. The fourth section reports estimation results derived by the previously introduced model, and the fifth section concludes.