1. QMT 3033
ECONOMETRICS
QMT 3033 ECONOMETRIC

2. Chapter 1
INTRODUCTION
QMT 3033 ECONOMETRIC

3. Introduction to Econometrics
Econometrics literally means “measurement in economics”.
Econometrics may be defined as the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena.
QMT 3033 ECONOMETRIC

4. Econometrics is not mathematical economics
Econometrics is not mathematical economics. It is about developing quantitative estimates of economics relations or models.
QMT 3033 ECONOMETRIC

5. Economic Models
A model is a simple representation of a real-world process.
Models have to be simple to make them traceable.
They also have to be general enough to be useful.
Models explain the relationships between variables of interest.
QMT 3033 ECONOMETRIC

6. Example
The national income identity represents a simple economic model.
For a closed economy, the following identity holds,
Y = C + I + G
where all variables are flows in real terms.
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7. Hypotheses about the individual parts of this identity formed:
Consumption
C = f [(1-t) Y, r]
0 < f1 < 1, f2 < 0
Investment
I = f [(1-t) Y, r]
f1 > 0, f2 < 0
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8. The explanation is conditional upon the values of G, r and t.
The model constitutes a theory about the joint determination of C, I and Y.
C, I and Y are endogenous.
The explanation is conditional upon the values of G, r and t.
G, r and t are exogenous.
QMT 3033 ECONOMETRIC

9. Limits of Economic Models
Economic models leave many questions unanswered.
Functional form.
Data definition and measurement.
Dynamic (lag) structure.
Qualitative versus quantitative implications.
Choice between competing theories.
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10. The Econometrics Model
The specification of a model with a deterministic component (the explanatory variables) and the stochastic error component is called the econometric model.
It provides a link between the data and economic theory.
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11. Economic vs Statistical Models
Economic model explains the behaviour of one variable in terms of other variables.
Eg. Q = f(P)
Linear: Qt = β1 + β2Pt
Log-linear: ln Qt = β1 + β2 ln Pt
Statistical model: Qt = β1 + β2 Pt + ut
QMT 3033 ECONOMETRIC

12. Writing Research Paper
Research can be defined as an organized, systematic, data-based, critical, objective, specific inquiry or investigation into a specific problem, undertaken with the purpose of finding answers or solutions to it.
Research provides the needed information that guide managers to make informed decisions to successfully deal with problems.
QMT 3033 ECONOMETRIC

13. Theoretical framework
Research Process
The broad
problem area
Preliminary data
gathering
Problem
definition
Research
design
Hypothesis
development
Theoretical framework
Data
collection
Data analysis
and interpretation
Research
report
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14. Review of Probability Concepts
Basic Probability Concepts
Probability is the likelihood or chance that a particular event will occur (always between 0 and 1).
Event is each possible outcome of a variable.
Simple Event is an event that can be described by a single characteristic.
Sample Space is the collection of all possible events.
QMT 3033 ECONOMETRIC

15. Assessing Probability
A priori classical probability
- Probability of occurrence: =
where
X = number of ways in which the event occurs
T = total number of elementary outcomes
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16. Subjective Probability
Empirical Classical Probability
- Probability of occurrence.
Number of favorable outcomes observed
Total number of elementary observed
Subjective Probability
- An individual judgment or opinion about the probability of occurrence. =
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17. Conditional Probabilities
A conditional probability is the probability of one event, given that another event has occurred:
The conditional probability of A given that B has occurred
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18. The conditional probability of B given that A has occurred
Where
P(A and B) = joint probability of A and B
P(A) = marginal probability of A
P(B) = marginal probability of B
QMT 3033 ECONOMETRIC

19. Bayes’ Theorem
Conditional probability takes into account information about the occurrence of one event to find the probability of another event.
This concept can be extended to revise probabilities based on new information and to determine the probability that a particular effect was due to a specific cause.
The procedure for revising these probabilities is called Bayes’ Theorem.
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20. Bayes’ Theorem Formula
Where
Bi = ith event of k mutually exclusive and collectively exhaustive events
A = new event that might impact P(Bi)
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21. Discrete Probability Binomial Distribution
Binomial distribution is used when discrete random variable of interest is the number of successes obtained in a sample of n observation.
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22. Binomial distribution:
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23. Poisson Distribution
Poisson distribution is used when you wish to count the number of times an event occurs in a given area of opportunity.
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24. Poisson Distribution Formula
Where
X = number of events in an area of
opportunity
 = expected number of events
e = base of the natural logarithm system ( )
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