Parametric Estimation: Techniques, Advantages and Limitations

Parametric estimating is a linear and accurate technique used to identify project cost and duration. It is widely used in technical projects and construction industry. However, it has limitations in welfare projects where the variables are distinct and cannot be used as parameters for cost estimation. This article discusses the process, advantages and limitations of parametric estimation.

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Parametric estimating is one of the most accurate techniques for identifying a project cost
and duration. The process of the estimation is also very linear and easy to implement as well. For
a successful estimation, each unit of deliverables must be predefined and then the cost and time
requirements are analyzed through research and the available information. Usually, the
customers are interviewed to gain insights about the satisfaction level with the company’s
services. Parametric estimating is very useful when detailed information about the deliverables is
not available (Scott, 2015, p. 235). The lack of information can affect the project dramatically.
Nowadays, parametric estimating has become necessary in every technical project. This tool is
widely used to estimating the schedules, budget and control the whole project (Andersen, Fusari
& Todorov, 2015, p. 1081-1145). Various authors has presented their different view in several
articles. According to some authors, parametric estimating can be utilized in the technical
projects and has become essential to stay competitive in the business. Specific characteristics of
a technical-intensive project can be utilized to form cost relationship and has the ability to
predict the base cost with limited information. Though, the appropriateness of the estimation is
depends upon the validity of the cost estimating relationship and effective use of historical data.
The parametric estimation can be obtained by utilizing the cost estimating relationship among
the characteristics of a project and use an algorithm to define an approximation of the total cost.
Even though the detailed estimation can provide accurate estimation, the parametric estimation is
useful when there is limited amount of information is available. In parametric estimating, the
first unit cost is calculated which can occur often (Martin & Pham, 2015, p. 89). Any cost
alteration associates with a large curve is calculated separately to determine the cost estimation.
The parametric estimation tool was first introduced during the World War 2 and later in 1999 the

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parametric cost estimating handbook was developed to increase the uses. This tool was first
developed to estimate government projects such as weapon acquisitions, space exploration
program and airplane. Usually this tools is pretty effective with the long projects durations and
high capital investment costs. The adequate return on investment also can be calculated by
following this approach. Generally, multiple uncertainty occurs during long duration project as
change in the scope, change in the specification and introduction of the new technologies. As the
project life cycle increases the probability of uncertainty also increases (Simar & Wilson, 2015,
p. 77-110). Traditionally, the widest known use of parametric estimating is in the construction
industry where it is used to reduce the time and subsequent expense to bid a project. In welfare
projects, the alter estimates acquired from discrete responses contingent valuation experiments
normally assume a particular distribution of willingness-to-pay. In most of the cases, the upper
and lower bonds of estimations are calculated through conventional microeconomic theory.
These bonds are calculated in terms of non-parametric estimators rather than using parametric
estimation. All calculations can be made by hand, simplifying communication among those
involved in interpreting result from contingent valuation studies using discrete-response data
(Hinchliffe & Lambert, 2013, p. 56). According to some authors, in the welfare projects the
difference between parametric and non-parametric estimation is not especially important as it is
suggested that experimental design of any certain project needs to give more priority before
utilizing the non-parametric estimation that loses the advantage of underlying information in the
traditional models.
For welfare projects, the parametric estimation is not very useful as it does not provide
optimistic solution. The parametric estimation only uses the past representation in order to
predict the future. In the cases of welfare projects, quantify the parametric inputs is not possible.

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The welfare projects are normally differ from other projects (Diggle, 2013, p. 123-125). Every
welfare project sets on different variables which are often found as a distinct insights. These
insights cannot be used as a parameter for calculating the appropriate cost estimation.
Figure 1: Parameter Estimating Advantage
Source: (Hinchliffe & Lambert, 2013, p. 13)
The parametric calculation process is shown in the above figure. In the welfare projects
the parametric estimating cannot be predicted. So, the parametric advantages cannot be utilized
in the projects such as welfare projects. A common problem is that the engineer’s optimism that
a simple solution is sufficient does not become reality. This problems are hindering the
estimating parameters. Traditionally, the widest known use of parametric estimating is in the
construction industry where it is used to reduce the time and subsequent expense to bid a project.
In welfare projects, the alter estimates acquired from discrete responses contingent valuation
experiments normally assume a particular distribution of willingness-to-pay.

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Reference:
Andersen, T. G., Fusari, N., & Todorov, V. (2015). Parametric inference and dynamic state
recovery from option panels. Econometrica, 83(3), 1081-1145.
Diggle, P. J. (2013). Statistical analysis of spatial and spatio-temporal point patterns. Chapman
and Hall/CRC.
Hinchliffe, S. R., & Lambert, P. C. (2013). Flexible parametric modelling of cause-specific
hazards to estimate cumulative incidence functions. BMC medical research
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Martin, W., & Pham, C. S. (2015). Estimating the gravity model when zero trade flows are
frequent and economically determined. The World Bank.
Ocampo-Duque, W., Osorio, C., Piamba, C., Schuhmacher, M., & Domingo, J. L. (2013). Water
quality analysis in rivers with non-parametric probability distributions and fuzzy
inference systems: application to the Cauca River, Colombia. Environment
international, 52, 17-28.
Scott, D. W. (2015). Multivariate density estimation: theory, practice, and visualization. John
Wiley & Sons.
Simar, L., & Wilson, P. W. (2015). Statistical approaches for non‐parametric frontier models: a
guided tour. International Statistical Review, 83(1), 77-110.