Introduction to Econometrics

INTRODUCTION TO ECONOMETRICS
Session 01
Created by : Sibashis
Chakraborty.

BACKGROUND
The term “Econometrics” was first used
by Pawel Ciompa as early as 1910.
Ragnar Frisch (Nobel laureate) is
credited for coining the term and
establishing Econometrics as a subject.
Econometrics  Oikonomia (Economics)
+ Metron (measurement) = “Measurement
in Economics”.

DEFINITION
“The method of econometric
research aims, essentially, at a
conjunction of Economic Theory,
and actual measurements, using
the theory and technique of
statistical inference as a bridge
pier” – Haavelmo, 1944.
As the preceding deﬁnition
suggests, econometrics is an
amalgam of economic theory,
mathematical economics,
economic statistics, and
mathematical statistics.

NEED FOR A SEPARATE DISCIPLINE
Economic theory makes
statements or hypotheses that
are mostly qualitative in nature,
But the theory itself does not
provide any numerical measure
of the relationship.
1
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.
2
Economic statistics is mainly
concerned with collecting,
processing, and presenting
economic data in the form of
charts and tables. It is the job of
an economic statistician to
collect raw data for econometric
work.
3
Mathematical statistics provides
many tools used in the trade, 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.
4

CONTINUED
In brief we can say that the
primary objective of
Econometrics is to provide
empirical content to Economic
theory.
It is crucial to understand that
neither ‘theory without
measurement’ nor ‘measurement
without theory’ is sufficient to
explain an economic
phenomenon. It is precisely
their UNION that is important.

METHODOLOGY
Using the model for Control/Policy Purposes.
Forecasting/Prediction.
Hypothesis Testing.
Estimation of Parameters of the model.
Obtaining the data.
Specification of the statistical or econometric model.
Specification of the mathematical model of the theory.
Statement of theory or Hypothesis.

1. STATEMENT OF THEORY/HYPOTHESIS
We shall take the example of Keynes consumption function where he stated that,
“The fundamental psychological law…is that men and women are disposed, as a rule on
the average, to increase their consumption as their income increases, but not as much as
the income increases”.
In short Keynes postulated that Marginal propensity to consume (MPC) is greater that
0 but less than 1.
0<MPC<1
Although Keynes postulated a positive relationship between consumption and
income, he did not specify the precise form of the functional relationship between the
two.

2. SPECIFICATION OF THE MATHEMATICAL MODEL
OF CONSUMPTION
A mathematical model of Keynesian Consumption function could be represented as
follows,
𝑌 = 𝛽1 + 𝛽2 𝑋 ; 0 < 𝛽2 < 1
where Y= consumption expenditure and X=income, and where β1 and β2, known as
the parameters of the model, are respectively, the intercept and slope coefﬁcients.
The model is an algebraical representation of the real world process. At the stage
of model specification we decide on the precise form of functional relationship
between the variables, as in this case Income and Consumption.
The purely mathematical model of the consumption function is of limited interest to
the econometrician, for it assumes that there is an exact or ‘deterministic relationship’
between consumption and income.

3. SPECIFICATION OF THE ECONOMETRIC MODEL
OF CONSUMPTION
Relationships between economic variables are generally inexact. This type of
situation calls for a “Stochastic” specification of the function. To allow for the inexact
relationships between economic variables, the econometrician would modify the
deterministic consumption function as,
𝑌 = 𝛽1 + 𝛽2 𝑋 + u
where u, known as the disturbance, or error term, is a random (stochastic) variable that
has well-deﬁned probabilistic properties.
The above equation is an example of a linear regression model which will be our
focus topic for most part of our discussion.
The econometric consumption function hypothesizes that the dependent variable Y
(consumption) is linearly related to the explanatory variable X (income) but that the
relationship between the two is not exact; it is subject to individual variation.

SIGNIFICANCE
OF THE TERM
‘STOCHASTIC’
The word stochastic comes from the Greek word
stokhos meaning “a bull’s eye.” The outcome of
throwing darts on a dart board is a stochastic process,
that is, a process fraught with misses.
The consumption income relationship represented in
the previous slide is Stochastic because of the inclusion
of the stochastic term ‘u’. The term stochastic means
that for each value of X, there is a whole distribution
of values of Y which makes impossible to forecast the
value of Y exactly
This uncertainty concerning Y arises precisely because
of the inclusion of the unobservable error term, without
which the model would continue to remain exact or
deterministic.

JUSTIFIABILITY OF INCLUSION OF THE
STOCHASTIC TERM
The inclusion of the disturbance term or our preference for a stochastic specification
may be justified at least in three important ways – Kennedy, 2008
Human Indeterminacy.
Influence of omitted variables.
Measurement error.

4. ESTIMATION
Post the data obtaining part the next task is to
estimate the parameters of the specified model.
The numerical estimates of the parameters provide
empirical content to the model.
In our specified econometric model of consumption
we obtain the estimates of 𝛽1 and 𝛽2.
Some popular estimation techniques used by
econometricians today are Ordinary Least Squares
(OLS), Maximum Likelihood (ML), Moments, etc.

5. HYPOTHESIS TESTING
At this stage we develop
suitable criteria to examine
whether the estimates
obtained are in conformity
of the expectations of the
theory that is being tested.
“A theory or hypothesis that
is not veriﬁable by appeal to
empirical evidence may not
be admissible as a part of
scientiﬁc enquiry” – Milton
Friedman.
For this purpose we depend
upon the branch of Statistical
theory known as Statistical
Inference.

6.FORECASTING/PREDICTION
If the chosen model does not refute the
hypothesis or theory under consideration, we
may use it to predict the future value(s) of the
dependent, or forecast variable Y on the basis
of known or expected future value(s) of the
explanatory, or predictor, variable X.
At times if the predicted values exceed
recorded/actual values we term them as
Forecast errors.

7. CONTROL OR POLICY PURPOSES
This is the final stage of econometric
modelling where the estimated model
may be used for control, or policy
purposes.
1
By appropriate Fiscal and Monetary
mixes the Government/competent
authority can manipulate the Control
Variable (X) to produce/achieve a
desired level of Target Variable (Y).
2

BRANCHES OF
ECONOMETRICS
Econometrics
Theoretical
Classical Bayesian
Applied
Classical Bayesian

THEORETICAL
ECONOMETRICS
VS APPLIED
ECONOMETRICS
Theoretical econometrics is
concerned with the development of
appropriate methods for measuring
economic relationships speciﬁed by
econometric model. It relies heavily
on Mathematical statistics.
In applied econometrics we use the
tools of theoretical econometrics to
study some special ﬁeld(s) of
economics and business, such as the
production function, investment
function, demand and supply
functions, portfolio theory, etc.

REFERENCES
Gujarati N Damodar, Porter C Dawn, Gunasekar Sangeetha ; Basic Econometrics
(Fifth Edition).
Bhaumik K Sankar ; Principles of Econometrics: A modern approach using Eviews (First
Edition).
Wooldridge M Jeffrey ; Introductory Econometrics: A modern approach (Fifth
Edition).

Introduction to Econometrics

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