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Good afternoon, this is doctor pradhan here.
So, welcome to on nptel project on econometric
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modelling. So, today we will start the introduction
part of econometric modelling. So, we will
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basically highlight what is all about means
econometric modelling? And how it is very
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useful for you know our academic point of
view?
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So, now basically you know we are living in
a dynamic world where everything is you know
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problematic so we are always bounded with
various socio-economic problems and its very
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dynamic, very uncertains and totally unpredictables.
But with with this this problems so we need
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to have a solutions.
So, that means we have to apply proper strategy
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how you have to tackle such type of dynamic
problems. So, without without having proper
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strategy it is very difficult to handle you
know various such as economic problems.
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So, econometric modelling is means in this
scenario econometric modelling is very important
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tools to solve these particular problems so
means it will give you guidelines or it will
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give you green signal what are the strategic
aspects you have to apply through which you
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can solve this particular problem so which
is very volatile barely uncertain and it is
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totally unpredictable in nature.
So, econometric modelling will give you fundamental
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idea so how you to solve this you know uncertain
problem or volatile problem in to stable problem
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or certain problems. So, this is how the beginning
of this econometric modelling.
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So, now what is all about this econometric
modelling the econometric modelling is divided
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into two parts.
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So, it is divided into two parts one is called
as econometrics and another is called as a
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modelling ok.
So, let me first ah before I highlight the
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econometrics and modelling. So, what is my
core agenda here? The core agenda of econometric
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modelling is to fit data into a particular
problem so that is we need to have fit means
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best fit, best structure of data into that
particular problem.
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So, data is usually represented as a informations
so the way you will process the information
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so that the problem can be solved immediately
or you can say you can transfer these uncertain
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problem or volatile problem to certain problem
or stable problem so that you can wipe out
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that socio-economic problem.
So, now to have or to fit data or to get a
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structure of goodness of fit is not straight
forwards ok it is very very complex problems.
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So, now we like to know what are these complexities.
So, how to get this means how how to get this
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best fitness of the model or you can see best
fitness of the data setup so that we can solve
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the problems.
So, we have various strategic issues, various
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strategic fundamentals so that we can come
to a particular conclusion. So, it requires
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huge skill setup first thing is, you know
you need huge skills, huge mathematical knowledge,
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huge statistical knowledge and you know relevant
softwares because nowadays without softwares
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to tackle multivariate problem is very difficult
because we we are always having time constants
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ok.
So, in this particular scenario so you must
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be very careful so how with limited limited
resource base you know with respect to time,
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methodology, issues, setup, etcetera. So,
we have to quickly find out a solution and
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within that solutions we have to get best
fitness of the particular model and by the
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way that model can be used for solving your
problems or you can see use for forecasting
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etcetera.
So, now I will highlight few things here is
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so in our today’s with this basic introduction
so today we are going to discuss these are
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the aspects so first of all what is econometrics?
so why we know econometrics its historical
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background and some sighted examples then
econometrics outputs, inputs, econometrics
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process then finally, the basic framework
of econometrics modelling and basic you know
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some of the basic knowledge about econometrics
because it should be mandatory you know something
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little bit before we go for econometric modelling
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Econometric modelling is a very complex problem
and it is very difficult problem without knowing
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all such aspects or we can say basics it is
very difficult to answer to this econometric
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modelling ok.
So, let me first we start with econometrics
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so let me econometrics is a a branch you know
econometrics is a branch branch of science
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where you can say a its which theory and statistical
methods are integrated in the analysis of
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numerical and institutional data.
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It is otherwise called as a scientific study
of numerical data based on variation in natures
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so that is means it is a set of information,
collection, summation, estimation and interpretation
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of you can say a data. So, this is what finally,
one called as a econometrics aspect ok.
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Econometrics is a set of informations, collections,
summation, estimations and interpretation
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of data so in the in the case of modeling
it is a process so it is the integration of
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physical simulation and mathematical simulations
so that means it is a process of presenting
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a real world object as a set of mathematical
equations ok.
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So, now so we have to transfer the physical
problem into mathematical problems ok. So,
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now econometrics will apply so whether in
these transformation this modelling is perfectly
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ok or not so that is how econometrics play
a big role. So, econometrics means a means
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you know modelling will transfer the real
world problem into mathematical problems to
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the equations so that is means it will build
a mathematical form of the model and econometrics
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will you know enter to that particular aspect
and investigate whether that problem is perfectly
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ok for ok or not. So, the way it will be investigated
that is this structure of econometrics ok.
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So, econometrics all together it is you know
divided into four aspects is the it is the
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you know integration of four different papers
all together it is called as starting with
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economics you know then a finance then mathematics
then statistics ok statistics.
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So, economics is the principle of consumption,
distribution and wealth management. So, it
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is the branch of social science which is concerned
with principle of consumption principle of
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you can say principle of production, consumption
and distribution wealth and their management
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so this is what the economics all all about.
Similarly in finance it is the branch of economics
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that studies the management of money and other
assets now so a means finance is route from
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economics so economics is the bigger concepts
where we are studying the principle of production,
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consumption, distribution of wealth and their
management.
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So, for finance is part of it it is the branch
of economics that is studies the management
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of money and other assets ok so similarly,
so we can define the the term mathematics
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it is branch of science dealing with a logic
of quantity and its arrangements ok.
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Similarly statistics we can define branch
of science where we can plan gather analyze
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information about a particular collection
of individuals or object under considerations
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so ok. This is how these structure of you
know econometrics so that means econometrics
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is the a cluster of economics, finance, mathematics
and statistics there is many way we can integrate
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all these thing because nowadays it has spread
in a many areas but, all together so in fact
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it is the means in brief we can saw that it
is the integration of economics problem, finance
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problem, mathematics use and statistics use.
So, we use mathematics and statistic how to
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solve the socio-economics problems or financial
problem that is what the core agenda of this
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econometric modeling ok.
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Now there is a quickly I will highlight here
ah once again what is all about this econometrics.
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So, then you see here is so all together so
econometrics is a branch of this four heads
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so this is the clusters which we called as
a econometrics so this is you can say a economics
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then this is finance then this is math and
this is start so the cluster will called as
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a econometrics ok.
So, it is a that is why I can analyze here
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is it is set of procedures and rules for reducing
large masses of data into manageable proportions
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along allowing us to draw conclusion from
those data so this is the fact of you know
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I I have very strictly mentioned in the beginning
that econometric modelling is the basic core
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agenda is means core objective is to fit the
data and that should be very structure, very
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reliable, very accurate.
So, the way we will fit the data that is what
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this specialty of econometrics so that is
what the definition is also talks about. So,
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in short it is the application of statistical
methods to socio-economic data that is nothing
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but, socio-economic problems so under it may
be purely social problem it may be purely
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economic problem say it may purely financial
problem that is coming under socio-economic
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issues.
So, there are various definition many definitions
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we have to interpret the econometrics so it
is not possible to analyze all these definition
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here so I am straight forward I will I am
giving one definition here, econometrics may
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be defined as the social science in which
the tool of economic theory mathematics statistical
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inference are applied to the analysis of economic
problem so this is how the broad definition
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of this you know econometrics ok.
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Now come come to this why economics? Why we
will know econometrics? In fact we have already
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discussed why why we know econometrics because
we we are in the dynamic world where all problems
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are very uncertain in nature, very problematic,
very complex so we need proper proper models
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where through which we can apply proper strategy
and to tackle that problem.
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So, economics you know come forward to solve
all these issues so that means it will give
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you the best fit models through which you
can apply proper strategy and you can solve
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your particular problems ok.
So, econometrics is used to test and refine
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the existing theory so that means I have very
briefly mentioned that so for econometric
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modelling is concern it is a application orientated
subject.
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So, you must have a proper theory and with
within the particular theory you have to being
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objective specification hypothesis specification
model formulation then our idea is econometric
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idea is whether this particular model basically
to transformation to mathematical transformation
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is perfectly ok or not.
So, how it is consistent with theory that
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is how the econometrics play fantastic rule
or key rules it is very useful as the existing
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theory may be may be very confusing in nature
because of various factors like policy change
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some of the unnecessary means not countable
factors like earthquake, terrorist attack
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etcetera ok.
Econometrics can evaluate the program which
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is useful for various stockholders in that
society. Because once you set the problem
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suppose I have a problem so I need to fit
a models so through which I can make a prediction
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and I can you can solve my problem but, ultimately
that model can be used by other people other
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other stockholders so that it will be multi
multiple use ok.
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So, econometrics is not a you know one it
is not one means utility it is multi multi
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means utility so it is very that is why its
its important is much much higher than the
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other subjects ok.
So, econometrics analysis very valuable to
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decision maker so actually since it is it
is the question of fitting the data into a
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proper structures so suppose is a data is
concerned there are many ways we have to classify
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the data structure sometimes it is called
as a one way which I like to highlight a experimental
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data and non-experimental data here experimental
data very rare in various socio economic problems
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so that is why econometrics is very handy
to handle non-experimental data and observational
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data to make the inferences ok.
So, that is very important fact in the case
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of econometrics modelling it is applicable
to apply any theory provided the real world
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data must be very supportive.
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One of the interesting because the starting
point of econometrics which I have mentioned
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the core agenda is that fitting data into
a proper structures.
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So, if data is not there then econometric
is handicapped econometrics hand will be very
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handy very helpful if you have a theory consistent
theory and you have a consistent informations
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so then they will use this information in
the theory so they can predictive or they
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can give you better strategy how you have
to go in future so ok.
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So, if something is missing then obviously
econometrics is meaningless so econometrics
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in requires entering of econometrics require
proper theory and proper information with
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if anything is missing in the process then
econometrics is meaningless so we need consistent
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theory we need consistent informations.
So, econometrics will we will give basic to
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input so one is how strategic skill power
you have how you have to handy and what are
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the techniques available and how you are integrating
the system till to get a particular you know
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answers so so these use of econometrics there
are many use in fact so other uses you know
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it is useful in financial market market in
product market wherever there is a problems
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so you apply these model then you know you
get to know how you have to fit the data and
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that will give you idea or strategy how you
have to apply strategy for your future direction
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that is how the you can say structure of econometric
modeling.
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So, modelling long term relationship between
price exchange rates then determinants of
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bond credit ratings then forecasting volatility
of bond returns or you can say capital a surprising
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model these are the most of the applications
where you you means I am just giving you some
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kind of examples here but, in reality they
it may be useful many cases most of the cases
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you know ninety nine percent cases whatever
problems you have you can apply the econometrics
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so that is why it is very handy subject and
very interesting subjects ok.
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Ah similarly, these are all other of you have
to means I am taking about you can apply in
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the stack market etcetera so anyway now so
you get to know what is all about econometrics
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and why you know econometrics then finally,
we will just get to know what is its background
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how it is coming into these pictures ok.
So, historically econometrics you know started
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with the foundation of econometrics society
in 1931 and cowles commissions research in
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1932 so you know so from 1931 onwards econometrics
play major roles in fact in the mid of 1960
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the rule of econometrics is very very you
know at the higher rate and right now in this
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particular era era the use of econometric
modelling is you know at a you know at the
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highest level so we have to using econometrics
modelling its very very difficult to go for
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any forecasting or any policing matters ok.
So, econometrics is very useful so that is
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why you need to have information about econometric
knowledge without econometric knowledge or
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without econometric modelling so it is very
difficult to go for any policy discussion
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or policy use it is very difficult.
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Yes so by by the way you can get to know here
what is the utility and usefulness of econometrics
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so it is started in the year 1931 but, purely
to solve socio economic problems that to you
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know mostly on economic related problem and
financial related problem so this is what
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we you called as in the social science but,
it has many application in other areas like
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in the physical science also so like you know
biometrics, technometrics, sociometrics, anthropometrics,
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cliometrics, chemometrics these are the applications
where now econometrics is a applied you see
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econometrics is a supporting subject you just
bring some theory.
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So, any any branch of science you see chemical
engineering, civil engineering or mechanical
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engineering you bring theory so then econometrics
is just like a tool so it will see how theory
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is there so what type of information you need
then we have to either you have to test some
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theory or we have to redesign the theory re-estimate
the theory so that you can bring something
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new or we can use in a more strategic way
more better way for policy matter or you can
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say feature prediction so this is how the
econometrics all about.
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So, you know there are certain you know I
will sight a few example so what is all about
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econometrics a structural together now be
careful here.
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So, I will I will put a models here c equal
to a plus b y so this is what we have bought
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from the theory of consumption function consumption
function
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So, c stands for consumptions and y stands
for income and a b are parameters which are
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supporting so that means the theory says that
c j function of y so this is how the theory
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that means so how you have to justify whether
there is this theory has a fact or meaningful
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interpretation or not so statistics has to
play.
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So, there are suppose I start with the concept
like a consumption as a function of income
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so that means a consumption if we dependent
variables so it depends upon y income levels
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so here consumption level will depends upon
your income levels ok.
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00:19:37,340 --> 00:19:42,830
Now whether this a relationship is very positive
or it is significant so that econometrics
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will help you in later so I have put the mathematical
equation c equal to plus a b y c equal sorry
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c equal to a plus b y so let let us means
what is all econometric modelling now this
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is mathematical form of the model so I will
infelicity format I will put it in explicitly
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format x equal to a plus b y so this is called
as a mathematical form of the models ok.
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So, now what I will do so I will transfer
this model into statistical form of the model
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so the moment I will transfer into statistical
form of the model so the model will be delete
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in like a plus b y plus u so that means initially
there are two two parts this is part one this
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00:20:25,049 --> 00:20:30,630
is part two so this is dependent cluster this
is independent cluster that to this is supporting
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00:20:30,630 --> 00:20:36,990
component and this is the main component ok.
So, now now once we will transfer into statistical
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00:20:36,990 --> 00:20:41,770
form of the model statistical model then it
is a three parts so this is dependent cluster
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00:20:41,770 --> 00:20:44,820
this is independent cluster and this is error
cluster ok.
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00:20:44,820 --> 00:20:50,679
So, we will get to know details when will
move into a direct econometrics problem so
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after couple of lectures means basic of all
econometrics now less than we will move to
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that pure econometrics model so how will start
and how you have to end with the a particular
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issues.
So, now in this particular setup so we have
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00:21:05,870 --> 00:21:12,870
consumption equations so c g e s consumptions
then uh it is function of incomes so here
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is a and b are the supporting components.
Now what is the main agenda econometrics is
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that so every times so that is how we called
as a one type of exploration so what you have
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to do here so every times that means here
u is in a hidden natures it is in a hidden
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So that mans some of the machine components
are there so is it possible to find out what
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are the hidden components are there so this
is once means this is give you signal for
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search it will give you a search. If not,
then first of all you have to find out what
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is the error terms so what is the contribution
of error is it 0 or is it something greater
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than 0 ok.
So, there are two aspects in fact so what
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is the error contribution such the model will
be best fitted if you know consider a you
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can consider as the best models if the error
will be at the minimum levels so if you will
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say 0 then obliviously it will be perfectly
fit model.
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So, now in most of the cases because it is
a simple models consumption is function of
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income only but, consumption depends upon
so many other factors in fact similarly, any
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any problems you take lets price and quantity
relationship so quantity dependent function
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of price so it does not mean that quantity
always depends upon price there are certain
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other factors are there just like a you know
stock price, stock price depends upon you
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can say market news so there are several other
factors like business condition, impression,
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exchange rate, so many things are there so
these are the factor which can you know influence
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stock price.
So we like to know first of all here the idea
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00:22:56,610 --> 00:23:01,870
is so you have to identify the particular
problem from that particular theory what is
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the main variable, instrumental variable so
that will that will declare as a core variables
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00:23:07,150 --> 00:23:13,030
and we call as a dependent variable then others
are is the you can supporting variables so
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how the supporting variables are you can see
influence in the core variables that is one
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00:23:17,140 --> 00:23:21,530
way of econometric modeling.
But you I I will clarify one thing you that
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when will enter to the econometric modelling
there are various types of structures you
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00:23:27,730 --> 00:23:33,260
will find basically the entire structure are
classified into four different games so one
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00:23:33,260 --> 00:23:38,780
is purely cross sectional modelling then there
is a time series modelling then panel data
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00:23:38,780 --> 00:23:42,830
modelling in structural equation modeling.
So, you know structural equation modelling
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00:23:42,830 --> 00:23:48,010
is a completely one part of the game and other
you know time series modelling means cross
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00:23:48,010 --> 00:23:52,450
sectional modeling panel data modelling another
part of the problem so means what I like to
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00:23:52,450 --> 00:23:59,140
say so there are two different games all together
in the econometrics system.
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So one system it is one one way classification
and another is a multi-way classification.
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One way classification I mean so it is the
every time there is one dependent variable
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00:24:10,220 --> 00:24:16,929
with one independent or multiple independent
variables so this is one system in where means
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00:24:16,929 --> 00:24:21,970
it is this particular stack structure it is
called as a one way causality ok.
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00:24:21,970 --> 00:24:28,140
So, basically this econometric modelling is
the mostly on causality issues so means causality
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00:24:28,140 --> 00:24:34,580
is very important in this particular ah frame
work of econometric modelling now in this
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00:24:34,580 --> 00:24:40,400
particular first uh particular system first
system so we assume that there is always one
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00:24:40,400 --> 00:24:46,429
dependent variables and one independent or
several independent variables so we will like
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00:24:46,429 --> 00:24:51,470
to know how these independent variables are
influencing you can see dependent variable
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00:24:51,470 --> 00:24:56,510
that is the structure of causality.
So, now in the second structure so we have
261
00:24:56,510 --> 00:25:01,429
series of dependent variables or we have series
of independent variables I get to know what
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00:25:01,429 --> 00:25:07,419
is this dependent independent variables means
in particular equation the right left side
263
00:25:07,419 --> 00:25:11,669
we will call is a dependent and right side
we will call is a independent variables ok.
264
00:25:11,669 --> 00:25:18,669
So, now so here this structure is that so
you have to see so how many are in the dependent
265
00:25:19,400 --> 00:25:25,750
how many are in the independent side so most
of the cases you will find one dependent with
266
00:25:25,750 --> 00:25:31,520
multiple independent so another case multiple
a multiple dependent or multiple independent
267
00:25:31,520 --> 00:25:36,330
so if that is the that is the structure then
we we we have a different problem called as
268
00:25:36,330 --> 00:25:39,210
a simultaneous equation modelling and structural
equation modeling.
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00:25:39,210 --> 00:25:45,910
In other cases so we have this system called
as a one way causality so that is one dependent
270
00:25:45,910 --> 00:25:51,340
variable with multiple independent variables
so this is how the system all about so means
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00:25:51,340 --> 00:25:58,320
these are all basic examples so what all together
so we have we have theory different setup
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00:25:58,320 --> 00:26:05,070
all together dependent setup independent setup
and to justify that ones we have a error setup
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00:26:05,070 --> 00:26:10,480
in fact we have a different games for error
component we have a different game about dependent
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00:26:10,480 --> 00:26:13,740
and independent.
So, we get to know details when we will enter
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00:26:13,740 --> 00:26:18,090
to this pure econometric modelling so in the
in the beginning we showed we very careful
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00:26:18,090 --> 00:26:24,960
that so we we are in the core objective that
is our dependent structure so this is main
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00:26:24,960 --> 00:26:31,960
ok then others are you can say just supporting
factors so these are all instrumental trough
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00:26:32,669 --> 00:26:38,000
which how the objective can be achieved so
this is the core fundamental knowledge of
279
00:26:38,000 --> 00:26:41,240
econometrics ok.
So, I have just highlighted here you know
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00:26:41,240 --> 00:26:48,000
consumption model similarly, capital a surprising
model so how this you know this and you know
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00:26:48,000 --> 00:26:49,030
market premium will
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00:26:49,030 --> 00:26:55,789
influence this is a a set value so that is
can be discuss in detail so we will discuss
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00:26:55,789 --> 00:26:59,299
details when we will got to the analysis first
ok.
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00:26:59,299 --> 00:27:06,140
So, now econometrics all together just like
you know in the system of business or productions
285
00:27:06,140 --> 00:27:10,780
so we have input and we have output so we
have to process inputs we will get the output
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00:27:10,780 --> 00:27:16,850
so econometrics is just like a technical process
ok so it is a technical or mechanical process
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00:27:16,850 --> 00:27:22,020
so we need we need to insert inputs then we
will come out with a output take because you
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00:27:22,020 --> 00:27:27,840
know there are two way we have to handle this
type of structures as usual you know recently
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00:27:27,840 --> 00:27:34,669
we have a multi numbers of softwares through
which we have to solve this problem.
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00:27:34,669 --> 00:27:39,390
But you know we can go for manually also manually
there is a systematic structure how you have
291
00:27:39,390 --> 00:27:44,179
to process this inputs and how you will get
out the outputs that means you should know
292
00:27:44,179 --> 00:27:47,580
what should be your output levels so output
classification is very important and in the
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00:27:47,580 --> 00:27:53,150
same times input classification is very important.
So, the thing is that means output classification
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00:27:53,150 --> 00:27:59,700
is means that is your objective specification
what what is your exact objective so means
295
00:27:59,700 --> 00:28:06,130
it tentatively your objective is very much
very much you can say known to you now what
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00:28:06,130 --> 00:28:12,390
what you have to do so you have to you have
to process the inputs, whether these are tentative
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00:28:12,390 --> 00:28:18,030
objective is obtained or not if these not
obtaining then what is the difficult and why
298
00:28:18,030 --> 00:28:23,340
it is not obtaining or whether there there
is any way to redesign or restructure till
299
00:28:23,340 --> 00:28:28,720
you get your objective or you can say whatever
analysis you are getting according you have
300
00:28:28,720 --> 00:28:33,240
to change your objective setup ok.
So, this is how econometrics will give you
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00:28:33,240 --> 00:28:39,940
signals means it is just like it may it say
it is a very strategic subjects it is very
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00:28:39,940 --> 00:28:44,929
strategic subject it will give you means very
dynamic knowledge for various problem so how
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00:28:44,929 --> 00:28:51,309
you means it will design redesign a structure
is structures till you get a a best models
304
00:28:51,309 --> 00:28:58,309
so through which you can solve your problems
carefully perfectively ok that is how is called
305
00:28:58,909 --> 00:28:59,570
econometrics.
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00:28:59,570 --> 00:29:05,720
So, now you see here is in the econometrics
there are two groups of things one is called
307
00:29:05,720 --> 00:29:11,030
as a output side another side is called as
a input side so in the output sides we need
308
00:29:11,030 --> 00:29:16,620
estimation that is measurements, inference
hypothesis testing and forecasting predictions
309
00:29:16,620 --> 00:29:21,039
then evaluation assessment.
So, these are things we have to do in this
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00:29:21,039 --> 00:29:27,390
output side that means these are our objective
core agenda here is so we need to estimate
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00:29:27,390 --> 00:29:31,210
measurement inference hypothesis testing that
means whatever object is specification is
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00:29:31,210 --> 00:29:36,280
there so we have to test it properly means
as for the hypothesis construction hypothesis
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00:29:36,280 --> 00:29:43,280
that means the a structure is that ah first
first you have a theory that is what we will
314
00:29:44,340 --> 00:29:47,980
be generate means from the theory you have
to identify a particular problem
315
00:29:47,980 --> 00:29:53,289
So now once you have a problem so you have
setup hypothesis that means the objective
316
00:29:53,289 --> 00:29:59,580
has to be constructed in the form of a hypothesis
that means a this the way you are transferring
317
00:29:59,580 --> 00:30:05,320
objective to hypothesis that means it is the
give you theoretical knowledge about the mathematical
318
00:30:05,320 --> 00:30:10,270
transformation into statistical transformation
Hypothesis testing means it is a statement
319
00:30:10,270 --> 00:30:15,549
which is not verified which is to be verified
so that means in which is to be verified means
320
00:30:15,549 --> 00:30:22,220
it will use of statistics only so statistics
will verify these ones so it will give it
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00:30:22,220 --> 00:30:28,659
will a means ah it is the econometrics job
is to verified the fact whether it is or not
322
00:30:28,659 --> 00:30:32,659
ok.
Ok so these are the outputs means object is
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00:30:32,659 --> 00:30:39,590
specification how you will a how you will
achieve this particular items but, we do not
324
00:30:39,590 --> 00:30:42,780
have any idea about the inputs specification
ok.
325
00:30:42,780 --> 00:30:48,090
So inputs specification here you see you must
have a theoretical knowledge for means sound
326
00:30:48,090 --> 00:30:54,000
theory behind this modelling without having
sound theory and idea so it is it is totally
327
00:30:54,000 --> 00:31:00,950
unnecessary to enter to the econometric modelling
because without theory econometric is totally
328
00:31:00,950 --> 00:31:06,100
meaningless totally meaningless ok.
So, now so theory is the most then mathematical
329
00:31:06,100 --> 00:31:11,980
knowledge see you must have a sound mathematical
knowledge you must have sound practical knowledge
330
00:31:11,980 --> 00:31:17,159
then you must have a information ok so theory
problem will give you but, sometimes information
331
00:31:17,159 --> 00:31:22,010
will not give you so information you have
to generate sometimes it may be readily available
332
00:31:22,010 --> 00:31:29,010
you bring that information or its not readily
available it create or you have to you have
333
00:31:29,620 --> 00:31:35,820
to means creation means either it is somewhere
possible to create or sometimes you in artificial
334
00:31:35,820 --> 00:31:39,809
creation you can create like you know there
is technique called as a dummy variable modeling
335
00:31:39,809 --> 00:31:42,760
so you will get to know when we will enter
to that particular problem ok.
336
00:31:42,760 --> 00:31:49,539
So, data or information is must then computing
powers so means it is not every times you
337
00:31:49,539 --> 00:31:56,100
hand is or you can see your mind brain power
is always suppose when you handling huge setup
338
00:31:56,100 --> 00:32:01,860
of data or huge number of variables and that
way you can say take a case of problem say
339
00:32:01,860 --> 00:32:08,830
structural equation modelling it is very difficult
to handle any hand particularly if your problem
340
00:32:08,830 --> 00:32:14,669
is very big and very means too much multivariate
in nature then that time it is very difficult
341
00:32:14,669 --> 00:32:19,419
to solve in a class rooms yes you can solve
this problem in the class room provided I
342
00:32:19,419 --> 00:32:23,419
will give you support that is what it called
as a softwares ok.
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00:32:23,419 --> 00:32:26,690
So, we will call it is a computing power,
computing power means we have a statistical
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00:32:26,690 --> 00:32:32,840
software through which we have to just operate
it properly the programs are design in such
345
00:32:32,840 --> 00:32:37,620
a way so it will give you means you give those
input command you process the inputs then
346
00:32:37,620 --> 00:32:42,270
automatically output will comes ok.
So, that is how its computing power is all
347
00:32:42,270 --> 00:32:48,260
about so then once you get the computation
means computation is above then you have a
348
00:32:48,260 --> 00:32:53,320
model results so then you have to interpret
then you have to check with you can say your
349
00:32:53,320 --> 00:32:57,500
existing theory so interpretation then knowledge
must be required how you have to interpret
350
00:32:57,500 --> 00:32:59,490
then how you have to integrate with the proper
theory ok.
351
00:32:59,490 --> 00:33:06,490
Ah you see here is econometric process is
all together to to means is to means job so
352
00:33:09,610 --> 00:33:15,240
one is called as a deterministic models another
is called as a statistical model I have already
353
00:33:15,240 --> 00:33:20,909
mentioned so econometric starting with you
know question of mathematical you know theory
354
00:33:20,909 --> 00:33:26,620
then the theory will be transfer into mathematical
form of the model then mathematical form of
355
00:33:26,620 --> 00:33:31,669
the model will be transfer into statistical
form of the models so that is how econometric
356
00:33:31,669 --> 00:33:35,690
modelling because I I have means very beginning
I have mentioned econometrics modelling is
357
00:33:35,690 --> 00:33:38,049
nothing but, the integration of econometrics
and modelling
358
00:33:38,049 --> 00:33:43,289
So, modelling will give you the signal of
physical transformation to mathematical transformation
359
00:33:43,289 --> 00:33:49,370
and econometrics will see whether the transformation
is perfectly or not so it it will give you
360
00:33:49,370 --> 00:33:56,370
a common type of job so you have to check
yourself whether you are in the right track
361
00:33:56,539 --> 00:34:00,289
ok.
So, the way we will move in the process so
362
00:34:00,289 --> 00:34:07,260
I have just represented here in that technically
now what is what is the core foundation here
363
00:34:07,260 --> 00:34:14,260
see because you see I mentioned here economics,
finance, and math mathematician statistics
364
00:34:17,069 --> 00:34:24,069
but, generally econometrics mathematical statistics
is the main core elements and economics finance
365
00:34:24,429 --> 00:34:30,720
they will give you the theoretical funda only
so theoretical funda or you can say mathematical
366
00:34:30,720 --> 00:34:37,020
formulation of the models but means mathematical
formulation of models you will you will you
367
00:34:37,020 --> 00:34:41,490
will design through the mathematical knowledge
only but, economics and finance will give
368
00:34:41,490 --> 00:34:46,820
you perfect fundamental theory so through
the theory you have to apply your mathematical
369
00:34:46,820 --> 00:34:52,220
knowledge and transfer this theory into mathematical
form of the model then ultimately you have
370
00:34:52,220 --> 00:34:57,570
to apply this statistic to investigate the
problem so that the theory can be tested or
371
00:34:57,570 --> 00:35:03,330
you can say retested the region a as for the
need of the stockholders ok.
372
00:35:03,330 --> 00:35:10,330
So, basically so theory from the theory you
have the deterministic models then that is
373
00:35:10,930 --> 00:35:15,839
you know mathematical theory then you you
have this apply this statistical theory then
374
00:35:15,839 --> 00:35:20,210
deterministic model will be transfer into
statistical form of the model that means you
375
00:35:20,210 --> 00:35:27,210
see when there is a theory here is so then
we have to apply mathematics to transfer this
376
00:35:27,780 --> 00:35:32,550
theory into deterministic model that will
call it mathematical form of model then you
377
00:35:32,550 --> 00:35:37,430
have to apply this statistics then there is
deterministic models will transfer into statistical
378
00:35:37,430 --> 00:35:41,900
form of the models that means statistical
application is applied to the mathematical.
379
00:35:41,900 --> 00:35:46,390
So, the mathematical form of the model will
transfer into statistical from of the model
380
00:35:46,390 --> 00:35:50,589
then once you will transfer the statistical
from of the models then you need to verify
381
00:35:50,589 --> 00:35:56,330
so as I have already mentioned so when when
we will put put into statistical from of the
382
00:35:56,330 --> 00:36:01,810
model so this is what the you know transformation
here this mathematical form of the model and
383
00:36:01,810 --> 00:36:06,330
this is statistical form of the model,
So, I will write it her once again so c equal
384
00:36:06,330 --> 00:36:13,099
to a b y simply so I will write it here c
equal to a plus b y plus u u is state as a
385
00:36:13,099 --> 00:36:19,000
error component ok error component so because
what is mean by verification verification
386
00:36:19,000 --> 00:36:24,790
means something is not hundred percently correct
ok so we need to check whether it is hundred
387
00:36:24,790 --> 00:36:28,410
percent correct or not correct.
So, now error let us assume that the this
388
00:36:28,410 --> 00:36:32,210
is not hundred percent correct that is why
we are starting with the error component we
389
00:36:32,210 --> 00:36:36,980
are saying that an hundred percent not correct
that means some percent is lucking so that
390
00:36:36,980 --> 00:36:43,430
some percent is lucking something nothing
but, u here is now we have to see what is
391
00:36:43,430 --> 00:36:49,869
the value ultimately if the u value is minimum
or 0 then these this is fact this model is
392
00:36:49,869 --> 00:36:55,900
perfectly so that is our so if u is is something
positive then obviously this model is not
393
00:36:55,900 --> 00:37:00,480
perfectly so that is what the econometric
will teach you ok.
394
00:37:00,480 --> 00:37:05,480
So, now this is statistical from of the model
then statistical form of the model transfer
395
00:37:05,480 --> 00:37:12,119
into data means we have to process the data
with computing power then finally, will get
396
00:37:12,119 --> 00:37:18,740
the estimated model so once we will get the
estimated model then obviously this estimated
397
00:37:18,740 --> 00:37:25,740
model has to be checked or you can say investigated
properly a through sound statistical test
398
00:37:26,470 --> 00:37:32,869
so we get to know details when we will go
to the particular process generally these
399
00:37:32,869 --> 00:37:39,869
these test are you can say three in nature
one is called as a goodness fit test, specification
400
00:37:40,589 --> 00:37:45,180
test and out of sample prediction test it
is called as a digi test ok.
401
00:37:45,180 --> 00:37:52,180
So, now we we like to first have the estimated
model then that part is called as a reliability
402
00:37:52,880 --> 00:37:58,599
checking so for a reliability checking concern
then these three test has to be taken care
403
00:37:58,599 --> 00:38:03,420
of so through the again econometrics then
finally, you have to find out the inference
404
00:38:03,420 --> 00:38:10,359
so whether this model is perfectly reliable
one or your fit of the data is structured
405
00:38:10,359 --> 00:38:14,500
one or not so if it is reliable then it will
be structured if it is not reliable then it
406
00:38:14,500 --> 00:38:19,760
means reliable test will not give you signal
green signal then it is unstructured data.
407
00:38:19,760 --> 00:38:24,050
So you have to again redesign reprocess till
you get the structured fit this structured
408
00:38:24,050 --> 00:38:29,160
fit means it is correctly specified and that
can be used for you can say prediction and
409
00:38:29,160 --> 00:38:36,160
forecasting so with with help of you know
once you get the a proper reliable model or
410
00:38:36,400 --> 00:38:43,400
best fitted models then obviously that has
to be tested ah the hypothesis and finally,
411
00:38:43,550 --> 00:38:48,210
you have to give your conclusions so how you
have gone through all together ok.
412
00:38:48,210 --> 00:38:55,099
So similarly, econometric technology so there
is need of a information information is must
413
00:38:55,099 --> 00:39:02,099
its three parts theory then techniques then
information that is what is called as a data.
414
00:39:02,130 --> 00:39:07,950
So, that means econometrics all together it
is the cluster of theory, information and
415
00:39:07,950 --> 00:39:14,070
techniques theory information and techniques
theory data and techniques or tools
416
00:39:14,070 --> 00:39:21,070
So these three components are very very important
that means it three fundamental issue is very
417
00:39:22,280 --> 00:39:29,280
important theory then informations informations
then then theory information then techniques
418
00:39:32,520 --> 00:39:39,520
then you have to apply the your mind power
your skill how you have to apply or process
419
00:39:42,470 --> 00:39:48,080
this theory information to consistent the
theory and how technique you have to apply
420
00:39:48,080 --> 00:39:49,849
to justify the theory ok.
421
00:39:49,849 --> 00:39:55,400
Then collection, summation, estimation and
interpretation what is means what is all about
422
00:39:55,400 --> 00:40:01,500
the econometrics so econometrics it is the
set of information collection, summation,
423
00:40:01,500 --> 00:40:06,810
estimation and interpretation so this is somewhat
we can called as a statistics but, here only
424
00:40:06,810 --> 00:40:13,500
converts the extra part is you can say theory
theory is the must its that is why it is not
425
00:40:13,500 --> 00:40:17,980
purely statistics ok
So it is majority part is ninety percent activity
426
00:40:17,980 --> 00:40:23,359
is statistics but, ten percent must be consistent
with theory so that theory is very important
427
00:40:23,359 --> 00:40:28,390
means ten percent theory is very important
so for is econometrics is constant ok.
428
00:40:28,390 --> 00:40:35,280
So, as I have mentioned here so very means
just like I have already mentioned here so
429
00:40:35,280 --> 00:40:40,320
for econometrics modelling is concerned so
we assumed that this model is not deterministic
430
00:40:40,320 --> 00:40:47,320
it is you know it is in the form of some error
environment so there are three specific components
431
00:40:48,730 --> 00:40:55,730
here so first thing you know when we will
put here is you can say c equal to a plus
432
00:40:56,000 --> 00:41:03,000
b y b y plus e b y plus e.
So, now we start with c is we start with c
433
00:41:03,760 --> 00:41:10,760
equal to a plus b y and ultimately we transfer
with transfer to a plus b y plus u now the
434
00:41:11,010 --> 00:41:16,630
obviously the question is why u why u why
error term is always there there are many
435
00:41:16,630 --> 00:41:22,260
reasons you know that is how I have mentioned
here why errors first things there are lots
436
00:41:22,260 --> 00:41:28,010
of omitted variables so for instance c equal
to c is the function of y here.
437
00:41:28,010 --> 00:41:32,070
So, that means consumption is function of
income but, do you think that consumption
438
00:41:32,070 --> 00:41:37,650
is always only variable which can influence
by income so it is obviously then answer is
439
00:41:37,650 --> 00:41:43,430
not so there are certain other variables which
can influence the consumption current consumption
440
00:41:43,430 --> 00:41:48,869
say instead of putting c like this way I can
put here c t equal to a plus b y current consumption
441
00:41:48,869 --> 00:41:52,940
level depends upon current income in fact
current consumption may be depend upon past
442
00:41:52,940 --> 00:41:58,700
income ok so that is current consumption may
be depends upon your needs so that is how
443
00:41:58,700 --> 00:42:05,700
some of the factor which cannot be it is not
possible to a you know capture sometimes it
444
00:42:06,359 --> 00:42:10,750
is possible but, you are not interested for
because of some reasons anyway whatever may
445
00:42:10,750 --> 00:42:14,820
be the case so these are the reasons why error
terms are always there ok.
446
00:42:14,820 --> 00:42:21,820
So, that means if I will specifically highlight
then obliviously why errors means its omitted
447
00:42:22,300 --> 00:42:27,510
variables cases because we are omitting few
variables which may be relevant may not be
448
00:42:27,510 --> 00:42:32,440
relevant means omission of variables means
some of the variables relevant variables we
449
00:42:32,440 --> 00:42:39,440
are not including for different regions hence
some of the sometimes you know ah you know
450
00:42:39,820 --> 00:42:44,000
unnecessary variable your also including initially
you may not have idea but, when we will go
451
00:42:44,000 --> 00:42:48,250
for modelling process you get to know whether
it is perfectly or not ok.
452
00:42:48,250 --> 00:42:54,680
So, then second is the measurement error in
the independent variables so because these
453
00:42:54,680 --> 00:42:59,349
some of the independent variables may not
be correctly specified so if it is not correctly
454
00:42:59,349 --> 00:43:04,369
specified then obviously the model may not
be correctly specified so if model further
455
00:43:04,369 --> 00:43:10,720
we assume that there are some error component
means whatever problems we we cannot you know
456
00:43:10,720 --> 00:43:13,280
capture so that will take care by error terms
ok.
457
00:43:13,280 --> 00:43:19,550
So similarly, last but, not the least cause
is randomness of human behavior for instance
458
00:43:19,550 --> 00:43:25,710
we like to know the lessons between c and
y so we have c information we have a y information
459
00:43:25,710 --> 00:43:31,849
so we like to correlate c and y by any chance
suppose I am just entering data ok.
460
00:43:31,849 --> 00:43:37,050
So, instead of thirty I will put three hundred;
instead of you know forty I will put forty
461
00:43:37,050 --> 00:43:42,430
two so like that there may be human errors
so that mistakes if it is severe mistake then
462
00:43:42,430 --> 00:43:48,170
obviously the model will give you rough results
that is how it is it is a continuous process
463
00:43:48,170 --> 00:43:54,390
redesign redesign till you get best fitted
model if it is coming perfectly then obviously
464
00:43:54,390 --> 00:43:59,940
you have to go continuously back back back
back back till you get you know fault where
465
00:43:59,940 --> 00:44:03,820
is exactly your problem.
So, by the way you have to expire and accordingly
466
00:44:03,820 --> 00:44:10,820
you have to sort out it solution yes there
are live examples here is basically the game
467
00:44:13,010 --> 00:44:17,970
is between deterministic model with econometric
model so deterministic model is purely say
468
00:44:17,970 --> 00:44:23,640
straight forward concept econometric modelling
is a complex process we are assume that something
469
00:44:23,640 --> 00:44:28,650
is not straight forward like you know means
it is game between a we mathematically we
470
00:44:28,650 --> 00:44:32,859
can call its linear version non-linear linear
means it is a very straight forward non-linear
471
00:44:32,859 --> 00:44:37,440
means it is not straight forward in between
there is lots of hidden factors in nature.
472
00:44:37,440 --> 00:44:44,440
So, econometric one of the core agenda what
I have mentioned that to fit a data in a proper
473
00:44:46,210 --> 00:44:52,190
structure and second core agenda is to to
explore the hidden information’s so what
474
00:44:52,190 --> 00:44:59,190
is the hidden information which can you can
say which is very much of tackle to your modelling
475
00:45:00,260 --> 00:45:07,260
scenario or which may not be perfectly you
can say not schedule so you have to be very
476
00:45:08,040 --> 00:45:09,089
careful how we have to investigate
477
00:45:09,089 --> 00:45:15,640
So, now you see here live example there is
price versus quantity then productions means
478
00:45:15,640 --> 00:45:22,640
output versus inputs then wage equation wage
productivity versus you know its its determinants
479
00:45:25,329 --> 00:45:31,170
then philips curves which is the game between
money wage rate and unemployment rate so its
480
00:45:31,170 --> 00:45:35,349
impression issue.
So, wagner’s law then government expenditure
481
00:45:35,349 --> 00:45:41,460
then external income these are the things
means these are all you can say these are
482
00:45:41,460 --> 00:45:46,790
all purely theoretical base this here theoretical
model we have a theoretical base so through
483
00:45:46,790 --> 00:45:52,099
the this price quantity that means theory
says that there is some relationship between
484
00:45:52,099 --> 00:45:56,130
price and quantity so we will assume that
quantity is function of price ok.
485
00:45:56,130 --> 00:46:01,240
So, the may be linear or may be non-linear
so we means whether the relationship there
486
00:46:01,240 --> 00:46:07,030
is relationship if there is relationship whether
its linear or non-linear whether it is you
487
00:46:07,030 --> 00:46:13,839
can say in deterministic deterministic stand
or econometric stand so that we have to know
488
00:46:13,839 --> 00:46:20,119
once we have a information why the basis of
information we have to check the setup accordingly
489
00:46:20,119 --> 00:46:24,270
we have to we have to come means we have to
fit the data so that we can come to a better
490
00:46:24,270 --> 00:46:28,210
structure better feasibility ok.
So, similarly, in the production every times
491
00:46:28,210 --> 00:46:32,880
what we have to do so you need information
that is you can say econometric inputs and
492
00:46:32,880 --> 00:46:38,240
ultimately your according to your objective
specification we have to process we have to
493
00:46:38,240 --> 00:46:45,240
collect or we have to redesign design till
you get the objective means verification of
494
00:46:46,599 --> 00:46:50,440
your objective.
So, once your objective is done then obviously
495
00:46:50,440 --> 00:46:54,940
is your process is done so with respect to
particular objective you have to process your
496
00:46:54,940 --> 00:47:01,940
inputs very carefully so that you will have
the objective done yes so I have a I have
497
00:47:03,329 --> 00:47:10,329
a analyzer properly one thing here is that
you need three things in the econometric setup
498
00:47:10,770 --> 00:47:17,770
one is consistent theory information that
is data and you know tools and techniques
499
00:47:19,349 --> 00:47:24,000
that is you have to derive from mathematics
and statistics ok.
500
00:47:24,000 --> 00:47:30,930
So, theory information’s and you know techniques
theory information and techniques three things
501
00:47:30,930 --> 00:47:35,930
I have already highlighted here so theory
information and technique these three things
502
00:47:35,930 --> 00:47:41,609
are very very important for econometric modeling.
So, now suppose is information I am just tracking
503
00:47:41,609 --> 00:47:46,319
one thing so we will get to know lots of things
about the techniques obviously theory is must
504
00:47:46,319 --> 00:47:52,250
then we information techniques will be ultimate
game so these are the instrument we have to
505
00:47:52,250 --> 00:47:56,109
play with this theory so techniques we do
not touch today anything about techniques
506
00:47:56,109 --> 00:48:01,950
we will get to know details in the coming
classes so information basically in the quantity
507
00:48:01,950 --> 00:48:04,630
term technical term is called as a data.
508
00:48:04,630 --> 00:48:11,180
So data basically divided into three forms
so the structure of data first thing a it
509
00:48:11,180 --> 00:48:15,810
may be experimental it may be non-experimental
it may be quantitative it may be qualitative
510
00:48:15,810 --> 00:48:22,140
but, you know if it is qualitative structure
then you know the setup is completely different
511
00:48:22,140 --> 00:48:29,140
So what what I like to say so in the beginning
we like to handle so quantity setup of data
512
00:48:29,859 --> 00:48:36,420
then we will see how qualitative setup of
data will means how do we solve the or handle
513
00:48:36,420 --> 00:48:39,300
the qualitative setup of the data in the econometric
modeling.
514
00:48:39,300 --> 00:48:44,540
So, far as a quantitative data is concerned
so we have three way of collections so we
515
00:48:44,540 --> 00:48:51,540
have three way of you know collecting the
means combining the information so then one
516
00:48:52,859 --> 00:48:57,660
is called as a time series setting. That means
data or information available about the about
517
00:48:57,660 --> 00:49:02,829
the time frame so this is called as a time
series modelling means sorry time series data.
518
00:49:02,829 --> 00:49:09,829
So, then another is called as a cross sectional
data cross sectional data is data or information
519
00:49:09,950 --> 00:49:16,950
collected about the sample units cross sectional
units for instants you know populations across
520
00:49:17,250 --> 00:49:23,430
the country say you say u k india etcetera
this is called as a cross sectional data and
521
00:49:23,430 --> 00:49:28,650
with respect to india what is the population
of india over the three years 1981, 1982,
522
00:49:28,650 --> 00:49:35,650
2001 I say this is called as means stack price
for various years 2001, 2002, 2003 like this
523
00:49:39,819 --> 00:49:45,310
way this is called as a time series a representation.
And finally, there is called as a panel data
524
00:49:45,310 --> 00:49:51,180
panel data panel data is the combination of
combination of time series data and cross
525
00:49:51,180 --> 00:49:58,180
sectional data so the specialty of panel data
is that it will increase increase the sample
526
00:49:59,950 --> 00:50:05,800
size because so for econometric modelling
is concerned you have a theory and then you
527
00:50:05,800 --> 00:50:10,400
have built a model then you have to apply
statistical to investigate whether the theory
528
00:50:10,400 --> 00:50:14,650
model is perfectly for that you need information
then you have to process.
529
00:50:14,650 --> 00:50:19,819
So, information is very important and that
to huge and huge information you need so in
530
00:50:19,819 --> 00:50:26,000
that case we call it it is a sample size so
sample size should be substantially very high
531
00:50:26,000 --> 00:50:30,829
in the case of you can say econometric modelling
higher the sample size higher is the higher
532
00:50:30,829 --> 00:50:35,050
is the model accuracy lower the sample size
lower is the model accuracy.
533
00:50:35,050 --> 00:50:41,880
So, you have a substantially a high sample
size so that you can justify your model fitness
534
00:50:41,880 --> 00:50:47,630
in more perfect way more structure way so
panel data will give you green signal how
535
00:50:47,630 --> 00:50:52,940
you have to increase the sample size sometimes
some of the problem if purely in cross sectional
536
00:50:52,940 --> 00:50:57,890
way and that information is very limited in
nature sometimes some of the information is
537
00:50:57,890 --> 00:51:01,530
purely times series nature in that way very
limited in nature.
538
00:51:01,530 --> 00:51:07,300
So, now if will clog these two then obviously
you will increase the sample size then that
539
00:51:07,300 --> 00:51:14,300
will be very handy for the say econometrics
to verify a particular theory so then these
540
00:51:15,420 --> 00:51:21,920
are all various variables it can be available
through cross sectional unit it can be available
541
00:51:21,920 --> 00:51:26,829
through time series unit then it may be first
available times region cross sectional panel
542
00:51:26,829 --> 00:51:30,310
data you will artificially create the panel
data setup ok.
543
00:51:30,310 --> 00:51:36,300
So, sometimes data may be divide monthly wise
quarterly annually weekly so many ways you
544
00:51:36,300 --> 00:51:43,300
have to you can say see all these structure
of data so that means we get to know the details
545
00:51:44,380 --> 00:51:51,170
about the econometric modelling so what is
the definition what is the what is its utility
546
00:51:51,170 --> 00:51:56,950
then how what are its applications then what
is the core agenda what are the objectives
547
00:51:56,950 --> 00:52:03,950
what is the historical issues behind econometric
modelling then you know proper structure of
548
00:52:04,420 --> 00:52:10,170
econometric modelling then basic themes that
to specifically the modelling rules and you
549
00:52:10,170 --> 00:52:16,030
know the structure of data etcetera.
So, with these basic introductions so we we
550
00:52:16,030 --> 00:52:21,369
I am very serious you get to know little bit
about what is all about econometrics and how
551
00:52:21,369 --> 00:52:27,619
it is important or relevance in the current
current business environment ok.
552
00:52:27,619 --> 00:52:33,980
Ah so we means what my suggestion is that
is very useful useful subject very interesting
553
00:52:33,980 --> 00:52:40,069
subject very relevant subject so that it has
a broad features of academics and some policy
554
00:52:40,069 --> 00:52:45,170
makers without having econometrics very difficult
to handle so many big big problems and big
555
00:52:45,170 --> 00:52:51,690
big issue so econometrics to learning econometrics
is most and it is very essential and very
556
00:52:51,690 --> 00:52:57,589
useful so we just highlighted what are the
basic issues and how interesting this particular
557
00:52:57,589 --> 00:53:04,589
subject and what are the entry point of econometric
modelling so what is the what are the requirements
558
00:53:05,130 --> 00:53:10,300
you need to have so that econometric modelling
can be applied properly and to justify or
559
00:53:10,300 --> 00:53:14,579
to integrate the problem setup etcetera.
So, with this we can conclude this particular
560
00:53:14,579 --> 00:53:16,380
session here; so, thank you very much. Have
a nice day.