Econ 2011: Introduction to Econometrics
Chapter-1: Introduction
Obsa U.
Addis Ababa University
obsaurgessa@aau.edu.et
October 5, 2023
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Outline
1
Definitions
2
Approaches of Econometrics
3
Purpose of Econometrics
4
Types of Economic Data
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1.1: Definitions
Economic theories suggest many relationships among economic
variables.
These, however, have to be checked against data obtained from the
real world.
We use econometrics, to provide a better understanding of economic
relationships and a better guidance for economic policy making.
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Different scholars defined econometrics as:
1
Econometrics simply means “economic measurement”.
The “metric” part of the word indicates measurement and hence it is a
branch of economics concerned with measuring the empirical
estimation of economic relationships among economic variables.
(Gujarati, 2003)
2
Econometrics is the application of statistical and mathematical
methods to the analysis of economic data, with a purpose of giving
empirical content to economic theories and verifying them or refuting
them. (Maddala,1992).
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Econometrics is the interaction of economic theory, observed data
and statistical methods. These interactions make econometrics
interesting, challenging and difficult. But “econometrics is much easier
without data”. (Verbeek, 2008)
Econometrics is based upon the development of statistical methods
for estimating economic relationships, testing economic theories, and
evaluating and implementing government and business policy.
(Woodridge, 2004)
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Why a separate discipline?
Economic theory makes statements that are mostly qualitative in
nature, while econometrics gives empirical content to most economic
theory.
Mathematical economics is to express economic theory in
mathematical form without empirical verification of the theory, while
econometrics is mainly interested in the later.
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Economic statistics is mainly concerned with collecting, processing,
and presenting economic data in the form of charts and tables. It does
not go any further. The one who does that is the econometrician.
Mathematical statistics provides many of tools for economic studies,
but econometrics supplies the later with many special methods of
quantitative analysis based on economic data.
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Economic vs Econometric Models:
A model is the simplified representation of actual/real phenomena.
Economic models shows the relationships between/among economic
variables.
Econometric models - stochastic model that includes one or more
random variables.
1
An econometric model will either be linear or non-linear in
parameters and variables.
2
Econometric models can be either static or dynamic
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Economic models: Any economic theory is an observation from the
real world.
A deliberately simplified analytical framework is called an
economic model.
Economic models consist of the following three basic structural
elements.
1
A set of variables
2
A list of fundamental relationships and
3
A number of strategic coefficients
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Example of Economic Model:
Model of job training and worker productivity
1
What is effect of additional training on worker productivity?
Formal economic theory not really needed to derive equation:
Hourlywage = f (educ, experience, training)
Other factors may be relevant, but these are the most important (?)
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Econometric models: contain a random element.
1
Mathematical or economic models
Qd = b0 + b1 P + b2 Y + b3 t
2
Econometric model would be of the stochastic form:
Qd = b0 + b1 P + b2 Y + b3 t + u
where u stands for the random factors which affect the quantity
demanded
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Example of Econometric model:
Econometric model of job training and worker productivity
wage = β0 + β1 educ + β2 exper + β3 training + u
u stands for the unobserved determinants of wage, e.g, innate ability,
quality of education, family background . . .
Most of econometrics deals with the specification of the error, u
Econometric models may be used for hypothesis testing
1
For example,β3 the parameter represents effect of training on wage
2
How large is this effect? Is it different from zero?
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Approaches of Econometrics
1.2. Approaches of Econometrics
The traditional/classical econometric methodology includes:
1. Statement of theory or hypothesis:
For example, Keynes stated: ”Consumption increases as income
increases, but not as much as the increase in income”.
It means that “The marginal propensity to consume (MPC) for a unit
change in income is grater than zero but less than unit”
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2. Specification of the mathematical model of the theory:
Y = β0 + β1 X, 0 < β1 < 1
where Y =consumption expenditure,
X=income,β0 &β1 are parameters,β0 is intercept,β1 is slope
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3. Specification of the econometric model of the theory
The relationships between economic variables are generally inexact.
In addition to income, other variables affect consumption expenditure.
For example, size of family, ages of the members in the family, family
religion, etc., are likely to exert some influence on consumption.
To allow for the inexact relationships between economic variables, the
mathematical equation is modified as follows:
Y = β0 + β1 X, 0 < β1 < 1
u is disturbance term or error term. It is a random or stochastic
variable.
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4. Obtaining Data
An econometric model requires data on all the variables in the model.
Example:
Year
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
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Y
2447.1
2476.9
2503.7
2619.4
2746.1
2865.8
2969.1
3052.2
3162.4
3223.3
3260.4
3240.8
X
3776.3
3843.1
3760.3
3906.6
4148.5
4279.8
4404.5
4539.9
4718.6
4838.0
4877.5
4821.0
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5. Estimating the Econometric Model
Ŷ = −231.8 + 0.7194X
MPC was about 0.72
Note: A hat symbol (Ŷ ) above one variable will signify an estimator of
the relevant population value
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6. Hypothesis Testing
Are the estimates accord with the expectations of the theory that is
being tested?
Is M P C < 1 statistically?
7. Forecasting or Prediction
With given future value(s) of X, what is the future value(s) of Y?
Example: if GDP = $6000Bill in 1994, what is the forecast
consumption expenditure?
Answer:Ŷ = −231.8 + 0.7196(6000) = 4084.6
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8. Using model for control or policy purposes
Y = 4000 = −231.8 + 0.7194X ⇒ X ∼ 5882
M P C = 0.72, an income of 5882 Bill will produce an expenditure of
$4000Bill.
By fiscal and monetary policy tools, Government can manipulate the
control variable X to get the desired level of target variable Y
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Purpose of Econometrics
1.3 Purpose of Econometrics
The principal purposes of econometrics are:
1
structural analysis,
2
forecasting and
3
policy evaluation
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Types of Economic Data
1.4. Types of Data for Econometrics Analysis
Based on sources, data is classified as:
1
2
primary and
secondary.
Data types can also be classified as:
1
2
experimental
non-experimental
Non-experimental data are obtained from observations of a system
that is not subject to experimental control,
Experimental data are obtained from controlled experiments in
laboratory.
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Based on measurability;
1
Qualitative versus
2
quantitative data
Data, as a matter of definition, is quantitative. Thus facts, which are
already expressed as numbers.
There are also variables, which are qualitative by nature and variables
which show qualitative shifts over time or space.
Such qualitative information is usually quantified by what are known
as dummy variables.
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Classifications of Economic data
1
Cross-section data
2
Time-series data
3
Pooled cross-sections
4
A panel data (or longitudinal data) set
Econometric methods depend on the nature of the data used
1
Use of inappropriate methods may lead to misleading results
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1. Cross-sectional data
are collected across several units at a single point or period of time
Units:economic agents", e.g. individuals, households, investors, firms,
economic sectors, cities, countries.
In general: the order of observations has no meaning.
Popular to use index i.
Optimal: the data are a random sample of the underlying population,
Cross-Sectional data allow to explain differences between individual
units.
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2. Time series data
are sampled across differing points/periods of time.
Popular to use index t.
Sampling frequency is important
Time series data allow the analysis of dynamic effects.
Univariate versus multivariate time series data.
Example: Trade flow from Ethiopia to Germany and GDP in Ethiopia
(in current US dollars), 1990 - 2023, T = 43
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3. Panel Data
are a collection of cross-sectional data for at least two different
points/periods of time.
Individual units remain identical in each cross-sectional sample (except
if units vanish).
Use of double index: it where i = 1, ..., N and t = 1, ..., T .
Typical problem: missing values - for some units and periods there are
no data.
Example: growth rate of imports from 54 different countries to
Ethiopia from 1990 to 2023 where all 54 countries were chosen for the
sample 1990 and kept fixed for all subsequent years (T = 43, N = 54).
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4. Pooled cross sections
Two or more cross sections are combined in one data set
Cross sections are drawn independently of each other
Pooled cross sections often used to evaluate policy changes
Example:
1
Evaluate effect of change in property taxes on house prices
2
Random sample of house prices for the year 2010
3
A new random sample of house prices for the year 2015
4
Compare before/after (2010: before reform, 2015: after reform)
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End of the Chapter!
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