This is Introductory Econometrics course for undergraduate class students.

1. BULEHORAUNIVERSITY
COLLEGEOFBUSINESSANDECONOMICS
DEPARTMENTOFECONOMICS
Course Title: Econometrics I
Instructor: Aschalew Sh.
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2. Introduction:
What is Econometrics?
• Literally econometrics means measurement (the meaning of
the Greek word metrics) in economic.
• However, econometrics includes all those statistical and
mathematical techniques that are utilized in the analysis of
economic data. The main aim of using those tools is to prove
or disprove particular economic propositions and models.
• Definition 2: Application of the mathematical statistics to
economic data in order to lend empirical support to the
economic & mathematical models and obtain numerical
results (Gerhard Tintner, 1968)
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3. Cont’d
• Definition 3: The quantitative analysis of actual economic
phenomena based on concurrent development of theory
and observation, related by appropriate methods of
inference (P.A.Samuelson, T.C.Koopmans and J.R.N.Stone, 1954)
• Definition 4: The social science which applies economics,
mathematics and statistical inference to the analysis of
economic phenomena (By Arthur S. Goldberger, 1964)
• Definition 5: The empirical determination of economic
laws (By H. Theil, 1971)
• Definition 6: A conjunction of economic theory and actual
measurements, using the theory and technique of
statistical inference as a bridge pier (By T.Haavelmo, 1944)
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4. It tell us a lot about econometrics
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5. Why a separate discipline?
• Based on the definition above, econometrics is an amalgam of
economic theory, mathematical economics, economic statistics
and mathematical statistics. However, the course
(Econometrics) deserves to be studied in its own right for the
following reasons:
• Economic theory makes statements that are mostly qualitative
in nature, while econometrics gives empirical content to most
economic theory.
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6. Cont’d
• Mathematical economics: The main concern of mathematical
economics is to express economic theory in mathematical form
(equations) without regard to measurability or empirical
verification of the theory. Econometrics, as noted previously, is
mainly interested in the empirical verification of economic
theory.
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7. Cont’d
Economic Statistics is mainly concerned with collecting, processing and
presenting economic data. It does not being concerned with using the
collected data to test economic theories
 Mathematical statistics: Although mathematical statistics provides many
tools to analyze the data, the econometrician often needs special methods
in view of the unique nature of most economic data, namely, that the data
are not generated as the result of a controlled experiment.
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8. Cont’d
• The econometrician generally depends on data that cannot be
controlled directly. They often faced with observational as
opposed to experimental data.
• That is, in the social sciences, the data that one generally
encounters are non-experimental in nature, that is, not
subject to the control of the researcher.
• This lack of control often creates special problems for the
researcher in pinning down the exact causes affecting a
particular situation.
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9. Methodology of Econometrics
(1) Statement of theory or hypothesis:
• Keynes stated, the fundamental psychological law is men
(women) are disposed as a rule and on average, to increase
their consumption as their income but not as much as the
increase in their income.
• In short, Keynes postulated that the marginal propensity to
consume (MPC), the rate of change of consumption for a unit
change income is greater than zero but less than 1.
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10. Cont’d
(2) Specification of the mathematical model of the theory
• Although Keynes postulated a positive relationship between
consumption and income, a mathematical economist might
suggest the following form of consumption function:
Y = ß1+ ß2X ; 0 < ß2< 1
• Y= consumption expenditure
• X= income
• ß1 andß2 are parameters; ß1 is intercept, and ß2 is slope
coefficients
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11. Cont’d
(3) Specification of the econometric model of the theory
• The inexact relationship between economic variables, the
econometrician would modify the deterministic consumption
function as follows:
• Y = ß1+ ß2X + u ; 0 < ß2< 1;
• Y = consumption expenditure; X = income;
• ß1 and ß2 are parameters; ß1 is intercept and ß2 is slope
coefficients; u is disturbance term or error term. It is a random
or stochastic variable
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12. Cont’d
• (4) Obtaining Data
• To estimate the econometric model that is to obtain the
numerical values of β and β , we need data. e.g
• Y= Personal consumption expenditure
• X= Gross Domestic Product all in Billion US Dollars
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13. Cont’d
Year Y X
1980 2447.1 3776.3
1981 2476.9 3843.1
1982 2503.7 3760.3
1983 2619.4 3906.6
1984 2746.1 4148.5
1985 2865.8 4279.8
1986 2969.1 4404.5
1987 3052.2 4539.9
1988 3162.4 4718.6
1989 3223.3 4838
1990 3260.4 4877.5
1991 3240.8 4821
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14. Cont’d
(5) Estimating the Econometric Model
• Y^ = - 231.8 + 0.7194 X
• MPC was about 0.72 and it means that for the sample
period when real income increases 1 USD, led (on average)
real consumption expenditure increases of about 72 cents
• Note: A hat symbol (^) above one variable will signify an
estimator of the relevant population value
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15. Cont’d
• (6) Hypothesis Testing
• Are the estimates accord with the expectations of the theory
that is being tested? Is MPC < 1 statistically? If so, it may
support Keynes’ theory.
• Confirmation or refutation of economic theories based on
sample evidence is object of Statistical Inference (hypothesis
testing)
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16. Cont’d
(7) Forecasting or Prediction
 With given future value(s) of X, what is the future value(s) of Y?
e.g., GDP=$6000Bill in 2030, what is the forecast consumption
expenditure?
Y^= - 231.8+0.7196(6000) = 4084.6
 Income Multiplier M = 1/(1 – MPC) (=3.57). decrease (increase)
of $1 in investment will eventually lead to $3.57 decrease
(increase) in income
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17. Cont’d
(8) Using model for control or policy purposes
• Y=4000= -231.8+0.7194X  X  5882
• MPC = 0.72, an income of $5882 Bill will produce an
expenditure of $4000 Bill.
• By fiscal and monetary policy, Government can manipulate
the control variable X to get the desired level of target
variable Y.
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18. 18
Economic Theory
Mathematical Model Econometric Model Data Collection
Estimation
Hypothesis Testing
Forecasting
Application
in control or
policy
studies
Figure 1. Anatomy of economic modelling

19. GOALS OF ECONOMETRICS
• Analysis/Testing Economic Theories: This involves using
statistical methods to assess the validity of economic theories.
Econometricians develop models that translate economic
theories into mathematical equations and then test these
models against real-world data. This helps determine how well
the theories explain actual economic behavior.

20. Cont’d
• Providing Estimates (Policy Making): Econometrics aims to
quantify the relationships between economic variables. By
analyzing data, econometricians estimate the magnitude and
direction of the influence one variable has on another. This
provides concrete figures that can be used for various
purposes, like policymaking.

21. Cont’d
• Forecasting the Future: Econometrics allows for predictions
about future economic trends. Using the estimated
relationships between variables, econometricians can build
models to forecast future values of economic indicators like
inflation rates, interest rates, or GDP. It's important to
remember that forecasts are not perfect and come with
inherent uncertainties.

22. The Structure of Economic Data
• Before a hypothesis can be tested and any conclusion made,
data must be gathered. There exist a variety of types of
economic data:
 Cross-Sectional Data
 Time Series Data
 Panel or Longitudinal Data
 Pooled Cross Sections
 Each data type has advantages and disadvantages.
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23. Time series data
• Time series data, as the name suggests, are data that have
been collected over a period of time on one or more variables.
Time series data have associated with them a particular
frequency of observation or collection of data points.
• The frequency is simply a measure of the interval over, or the
regularity with which, the data are collected or recorded.
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24. 24
Cont’d
• The data may be (e.g. exchange rates, prices, number of shares
outstanding)

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Cont’d
• Examples of Problems that Could be Tackled Using a Time
Series Regression
• How the value of a country’s stock index has varied with that
country’s macroeconomic fundamentals.
• How the value of a company’s stock price has varied when it
announced the value of its dividend payment.
• The effect on a country’s currency of an increase in its
interest rate
• In all of the above cases, it is clearly the time dimension which
is the most important, and the analysis will be conducted using
the values of the variables over time.

26. 26
Cross-sectional data
• Cross-sectional data are data on one or more variables collected
at a single point in time, e.g.
- A survey of usage of internet stockbroking services
- A sample of bond credit ratings for UK banks
• Examples of Problems that Could be Tackled Using a Cross-
Sectional Regression
• The relationship between company size and the return to
investing in its shares

27. Panel Data
• Panel data has the dimensions of both time series and cross-
sections, e.g. the daily prices of a number of blue chip stocks
over two years.
• It is common to denote each observation by the letter t and
the total number of observations by T for time series data, and
to denote each observation by the letter i and the total
number of observations by N for cross-sectional data.
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29. Pooled Cross Sections
• Pooled Cross sections are a combination of RANDOM samples
from different years.
• The same observation should not be followed over different
years
• Analysis is similar to cross sectional data, with the additional
consideration of structural changes due to time
• relatively new concept useful for analyzing policy effects
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30. Cont’d
Obs. Year
Hours System
Played
Hours
Studied Utility Male
1 1995 (pre WII) 6 9 27 1
2 1995 9 5 35 1
3 1995 4 7 12 0
4 1995 7 2 25 0
5 2007 (post WII) 6 5 17 0
6 2007 3 7 22 1
7 2007 1 11 25 0
8 2007 6 4 22 1
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31. Categories of Variables
• Ratio Scale: This is the most informative scale. It allows you to
not only compare the order and difference between values,
but also their actual ratios. Ratio scales have a true zero point,
meaning zero represents the complete absence of the
variable.
• Examples of ratio scales include temperature (in Kelvin), height, and
weight. You can say that someone who is 180 cm tall is twice as tall as
someone who is 90 cm.
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32. Cont’d
• Interval Scale:
• Interval scales are similar to ratio scales in that they allow you
to compare, order, and difference between values. However,
they lack a true zero point. The difference between values is
meaningful, but the zero point itself is arbitrary.
• Examples of interval scales include temperature (in Celsius or
Fahrenheit), IQ scores, and time (in seconds, minutes, etc.).
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33. Cont’d
• Ordinal Scale: Ordinal scales allow you to rank or order the
values of a variable, but the difference between values cannot
be determined.
• For instance, you can rank customer satisfaction as high, medium, or low,
but you can't say how much more satisfied someone who is "high" is
compared to someone who is "medium". Other examples of ordinal
scales include letter grades, shoe sizes, and military ranks.
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34. Cont’d
• Nominal Scale: Nominal scales simply classify data into
categories with no inherent order or meaning. The categories
are not ranked in any way.
• Examples of nominal scales include hair color (blonde, brunette,
redhead), blood type (A, B, AB, O), and political affiliation (Democrat,
Republican, Independent).
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35. The key difference between ratio and interval scales
• Ratio Scale:
• Has a true zero point that signifies a complete absence of the variable being
measured.
• You can perform all mathematical operations (compare order, difference,
ratios) on ratio scale data.
• Ratios between values are meaningful. For example, 10 kg is twice as heavy
as 5 kg.
• Interval Scale:
• Lacks a true zero point. The zero point is arbitrary and doesn't represent a
complete absence of the variable.
• You can compare the order and differences between values on the scale, but
zero itself doesn't hold meaning.
• Ratios between values are not meaningful. Saying 20°C is twice as hot as
10°C isn't accurate (they differ by 10 degrees, not a factor of 2).
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36. End of chapter one.
Thank You!!
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