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Exam (elaborations) ECS3706 - Econometrics (ECS3706) OCT/NOV 2021 EXAM MEMO
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Exam (elaborations) ECS3706 - Econometrics (ECS3706)
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ECS3706OCT/NOV 2021
EXAM QUESTIONS & SOLUTIONS
SPECIFIC FOR JAN/FEB SUPP EXAMS
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, QUESTION A1 (15 marks)
Stochastic error terms is added in order to account for variation in the dependent variable that
is not explained by the model. The specific value of the error term for each observation is
determined purely by chance. Probably the best way to picture this concept is to think of each
observation of the error term as being drawn from a random variable distribution.
Helps researchers to note the difference between the theoretical value of the model
and the actual observed results.
Vagueness of theory: The theory, if any, determining the behaviour of Y may be, and
often is, incomplete.
Unavailability of data: Even if we know what some of the excluded variables are and
therefore consider a multiple regression rather than a simple regression
Intrinsic randomness in human behavior: Even if we succeed in introducing all the
relevant variables into the model, there is bound to be some “intrinsic” randomness in
individual Y’s that cannot be explained no matter how hard we try.
1B
The above statement is generally true because in a regression equation the explanatory
variables have a causal effect on the dependant variable. For instance Price, income and other
goods (Complements and substitutes) cause changes in quantity demanded in demand
equation.
However, there are situations where there is a two-way flow of influence among economic
variables; that is, one economic variable affects another economic variable(s) and is, in turn,
affected by it (them). In this case there is what we call simultaneous bias where a variable is
both a dependant and an independent variable.
1c
(c) Explain the meaning of linear in the variables and linear in the coefficients. (3)
A linear relationship (or linear association) is a statistical term used to describe a straight-line
relationship between two variables. Linear relationships can be expressed either in a
graphical format or as a mathematical equation of the form y = mx + b.
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