Demand Forecasting, Oligopoly Market, Cost-Minimization and Utility Maximization

This economic assignment covers demand forecasting methods, oligopoly market, cost-minimization and utility maximization techniques. It also explains cross-elasticity of demand between new and old cars.

Economic Assignment 1
ECONOMIC ASSIGNMENT
By (Student’s Name)
Professor’s Name
College
Course
Date

Economic Assignment 2
1. Demand forecasting is never a speculative exercise into unknown but a reasonable
judgment of future probabilities of market events hinged on scientific backgrounds. Qualitative
forecast usually entail enormous quantities of subjective judgments because there are many
personal opinions and additional human factors, accurate and reliability are often the huge
concerns. While quantitative methods are increasingly objective and scientific. It usually engage
historical data, and by utilizing mathematical models in processing such information to find out
the trends embedded in data. Demand forecasting is imperative to each business because demand
is a key driver in each business. Without demand for the firms’ goods and services, no business
will exist (Daganzo 2014). The business has to maintain a delicate balance between demand and
supply which is only achievable through demand qualitative or quantitative forecasting to predict
the demand. Demand forecasting thus allows planning of new facilities by making the operation
managers to have adequate time to build factories and install required processes to produce
product as and when required.
Qualitative methods include Jury of Executive Opinion; Executive Opinion; Expert
Opinion, Delphi Method, sales forces survey and consumer survey. Using Jury of Executive
Opinion Method, executives are asked individually to give their expert opinion on anticipated
demand during particular time duration (Hamzaçebi 2016). A single estimate is got by
aggregating the individual estimates. It has the advantage of allowing for a broad array of factors
to be taken into account but is only attached to various biases that can affect forecast accuracy as
expert can be biased during forecasting. With expert opinion, stakeholders engaged in different
organization’s business elements are bunched up and attain consensus by each distinctly bunched
up cohort like suppliers or retailers and the final overall estimate is determined per expert
opinion of each distinct group. With Delphi method, mail survey is utilized for extracting expert

Economic Assignment 3
opinions of expert group. A summary is done of responses of experts is done without revealing
the expert identity and subsequently mailed further to experts alongside questionnaire
(engineered) to explore reasoning behind extreme opinions proffer in 1st round. The process is
extended to one or more until a reasonable agreements is achieved among the experts. Delphi is
the most successful method where relevant knowledge is dispersed among the experts.
Quantitative methods include time series and causal methods and rely on mathematical models
as opposed to qualitative which depends on expert judgments. Causal demand forecasting
method is used in producing forecasting anchored on strong cause and effect connection between
independent variables and demand variable (dependent variable) and include regression analysis,
index method and segmentation. It is extremely accurate method. Time series is applied where
the variables shows distinct trends in previous horizon and the identified trend will thrive into
future. The series are collected and used to generate models. It is mostly applied when there is no
unique downward or upward trend in historical data being probed. Thus, causal demand
forecasting remains the best for forecasting “expensive mobile” because it the most accurate of
all methods. Regression analysis, for example, allows evaluation of relationship between one or
more explanatory variable (price of mobile) and dependent variable (demand for the mobile).
2. The market characterized by competition among few is the oligopoly market. It is
different from competition among many in a number of ways. It is dominated by small number
of large firms whereas competition among many has many small firms. Another difference is
that firms in oligopoly sell either similar or differentiated commodities as opposed to
competition among many that sell identical products only (Feng, Li and Li 2014). The industry
in which oligopoly operate has significance barrier to entry unlike in the competition among
many which has no barrier to entry. The oligopoly promote their own interest through the

Economic Assignment 4
collusion and forms cartels to ensure that the make their own prices. For example, the OPEC and
Cement Cartels always come together to collude and hoard their products to ensure that the
prices surged and make better profits.
3. Consumers attains utility maximization and producer ensures cost minimization and
this can be shown using indifference curve and isoquant techniques.
Cost-Minimization
The producer’s cost-minimization problems is demonstrated in the diagram below:
From their above diagram, the red curve is y-isoquant which is the set of all pairs (z1, z2)
of inputs which produce exactly output y. The light blue region, above y-isoquant, i set of all
pairs (z1, z2) of inputs which produce at least output y: the set of possible input bundles for
output y. Every green line is a set of pairs (z1, z2) of inputs which are equally expensive; an
isocost line. The points of any give isocost line satisfies the condition; w1z1 + w2z2=c; for
certain value of c. Isocost lines away from origin corresponds to higher cost. Thus, the firms has
to choose an input bundle (z1,z2) possible for output y which cost as little as feasible (Neglia,

Economic Assignment 5
Carra and Michiardi 2018). Based on the above figure, cost-minimizing input bundle is indicated
as such point on y-isoquant which is on lowest feasible isocost line. It has to satisfy two
conditions; it is on y-isoquant; and no other point on y-isoquant is on a lower isocost line.
Utility Maximization
From the above indifference and budget line curves, we can show how utility is
maximized. Provided the budget line of B1, the consumer shall maximize utility whereby the
highest indifference curve becomes tangential to budget line (20 apples, 10 bananas). Provided
present income (IC2) remains unobtainable. IC3 is obtainable, however, less utility compared to
higher IC1.
4. “There is a high cross-elasticity of demand between new and old cars”. Cross elasticity
measures responsiveness of demand for a commodity towards the change in price of a connected
good and always shown in percentage. Here, it will measure the responsiveness of demand for an

Economic Assignment 6
old car towards the change in the price of new car (Colchero et al. 2015). With the consumption
behavior of new and old car related, the change in price of new car leads to a change in demand
of old car. These goods are related and hence the coefficient of cross elasticity will never be
zero. Because they are related the coefficient will be positive. Where the price of new car
increases, demand for old car will increases (Olmstead et al. 2015).

Economic Assignment 7
References
Colchero, M.A., Salgado, J.C., Unar-Munguia, M., Hernandez-Avila, M. and Rivera-Dommarco,
J.A., 2015. Price elasticity of the demand for sugar sweetened beverages and soft drinks in
Mexico. Economics & Human Biology, 19, pp.129-137.
Daganzo, C., 2014. Multinomial probit: the theory and its application to demand forecasting.
Elsevier.
Feng, Y., Li, B. and Li, B., 2014. Price competition in an oligopoly market with multiple iaas
cloud providers. IEEE Transactions on Computers, 63(1), pp.59-73.
Hamzaçebi, C., 2016. Primary energy sources planning based on demand forecasting: The case
of Turkey. Journal of Energy in Southern Africa, 27(1), pp.1-10.
Neglia, G., Carra, D. and Michiardi, P., 2018. Cache policies for linear utility
maximization. IEEE/ACM Transactions on Networking, 26(1), pp.302-313.
Olmstead, T.A., Alessi, S.M., Kline, B., Pacula, R.L. and Petry, N.M., 2015. The price elasticity
of demand for heroin: matched longitudinal and experimental evidence. Journal of health
economics, 41, pp.59-71.