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1. How would you define econometrics?
mathematical models to develop theories or test
existing hypotheses in economics and to forecast future
trends from historical data. It subjects real-world
data to statistical trials and then compares the
results against the theory being tested.
Depending on whether you are interested in testing an
existing theory or in using existing data to develop a
new hypothesis, econometrics can be subdivided into two
major categories: theoretical and applied. Those who
routinely engage in this practice are commonly known as
2. How does it differ from mathematical economics and
Mathematics vs Statistics
There may be some who would baulk at the mere idea of
differentiating between mathematics and statistics as they feel
that statistics is just a special branch of mathematics that has
been developed to tackle with practical problems in real life
situations. Though most of the concepts and formulae used in
statistics are derived from the vast knowledge base of
mathematics, it is treated as separate and independent branch of
math that has many applications. In fact, it is one of those
branches of mathematics that are collectively referred to as
applied mathematics. Let us take a closer look.
Mathematics is a basic subject that is taught from elementary
levels in schools. Initially it deals in numbers and basic
operations are taught to kids so as to be able to deal with
real life situations such as counting, addition, subtraction,
multiplication, and division. Later in depth knowledge of the
subject is given to students, which introduces algebra,
geometry, calculus, and finally statistics. Mathematics is an
academic discipline that allows one to understand the concepts
of quantity and structures. There are many who feel that math
is all about seeking about patterns whether found in numbers,
science, space, computers, designs, architecture, and so on.
Logical reasoning and application of theorems allows a student
of math to find relations and to reason with the ability to
authenticate their assumptions.
If you look at colleges and universities, they seem to have two
separate departments of mathematics and applied mathematics, and
this is where differences between mathematics and statistics
become easy to comprehend. Applied mathematics is widely used in
research and has industrial applications. Statistics is that
branch of mathematics that deals in probability, graphical
representation of mathematical data, and interpretation of
uncertain observation that is not possible with formulae and
principles of mathematics, and so on.
Statistics is mainly concerned with collection, analysis,
explanation, and presentation of data. It also helps in
forecasting and predicting results based upon insufficient data.
Statistics finds applications in a wide variety of fields such
investment practices, and stock markets besides helping those
involved in betting. Statistics can improve the quality of any
data and make interpretations from it easy.
3. Describe
the main steps
econometrics research.
Major Steps in an Econometric Research Project Step
1: Formulate a methodologically sound research design, or
research plan, that effectively utilizes the available sample
data to provide credible empirical evidence on the empirical
question(s) you are investigating. Requires knowledge and
understanding of
(1)the principles of econometric model specification and (2)
the methods of estimation and inference in econometric
Step 2: Execute the research plan in accordance with
good econometric practice – i.e., conduct the econometric
analysis required to assemble credible empirical evidence
on the empirical question(s) you were asked to investigate.
Requires a good working knowledge of both econometric
methods and econometric software.
Step 3: Write an accurate, complete, and logically
coherent research report in which you fully and accurately
explain what you did, how you did it, and what you found.
Explaining what you did and how you did it involves:
• Describing the sample data you used;
• Specifying the econometric models you estimated;
• Identifying the estimation methods you used;
• Specifying the hypothesis tests you performed on each
model. Reporting what you found involves:
• Tabulating the results of your econometric analysis,
including the coefficient estimates of all reported models
and the results of all hypothesis tests performed on these
• Interpreting and
econometric analysis;
• Assessing the strength of the evidence you obtained on
the empirical questions you were asked to investigate; •
Identifying limitations of your analysis and suggestions
for further research. Requires excellent organizational and
technical writing skills.
4. Differentiate
between economic and econometric model
Economic model
Econometric model
-it shows the economic r/ship -it measures the value of
b/n d/t economic variables.
parameters in economic r/ship
-the economic model is the
-an econometric model is the
concepts that represents the
mathematical estimate of the
variables or parameters there
in the identified models.
qualitative but by nature they
models as they ignore residual
-econometric models is are
future forecast eriented
-economic models attempt to -econometric models focus on
exhibit the logical r/ship b/n calculating
d/t variables considered in values
the model
variables considered in the
5. What are the goals of econometrics?
Goal Number 1: Analysis
Disclaimer: the example above is not suited to econometric
analysis unless the econometrist examining those economic
savings and
borrowing trends across
population. And, even then, they would not make recommendations
for individuals to follow but for policymakers
raise or lower interest rates.
perhaps to
It's impossible to formulate a statistic from a single data
No matter the phenomenon under consideration, it takes lots of
whatsthatpicture on VisualHuntEconometrics cannot apply to
individual situations because the discipline draws on statistics
as well as economics, and involves the use of mathematics.
However, that's just the type of information that econometrists
consider when tasked with proving an economic theory. This
process starts when a specific economic phenomenon comes under
consideration. Throughout this article, we'll use the wage gap
between workers with and without a university degree in a
particular sector - maybe manufacturing or marketing and sales
to illustrate salient points. To make the analysis more
specific, the econometric theory written to address this
question might specify to only consider workers of a certain age
or level of experience in that field. That's because time on the
job, particularly with the same company, is a variable that
could influence workers' wages, too. Side note: find out how
large a role theoretical econometrics plays in shaping economic
policy worldwide... To start the analysis process, econometrists
gather all of the relevant empirical evidence that tests the
economic theory in question. Does the observed economic
behaviour - the empirical evidence support the economic theory?
More often than not, econometrists end up with a 'sliding scale'
result. To reflect such an outcome in relatable terms, now... They
will likely find that not every single worker within that that
certain age group and/or level of experience will earn the same
amount. For instance, workers living in big cities may earn more
simply because their cost of living is higher and, thus, they
get paid more. That is a variable that was not considered in our
example; another is the size of the company they work for.
Multinational companies can and usually do pay their workers
more than smaller, homegrown concerns. Still, all other
variables aside, after examining all of that data, a clear
picture emerges of whether or not workers with a university
degree earn more. That conclusion could have a wide range of
consequences. Governments may make university education more
affordable or provide grants and subsidies to those who cannot
afford to continue learning. They may raise minimum wage,
forcing employers to increase skilled workers' pay to maintain
the gap between skilled and unskilled labour. Find more
economics tutor here on Superprof.
Goal Number 2: Supplying Estimates
Some people might play around with linear regression just for
the fun of it but most econometrists use the multiple linear
regression model because they have been tasked to find
information that addresses a specific economic concern. Usually,
the bodies assigning those tasks are the same ones that set
fiscal and economic policy for their country or group of
countries. After econometrists have modelled all of the data
applicable to the econometric theory they were given, they have
to do something with the conclusions they've drawn. They can't
simply throw a bunch of scatter plots on the table and claim
their work is done; they have to explain their conclusions and
provide concrete numbers that will help shape economic policy.
That's why, before presenting their analyses, econometrists
winnow their findings down to the coefficients of economic
relationships, a simple numerical estimate that policymakers can
use to decide how they'll move forward armed with the
information at hand. Econometrists arrive at that number by
using correlation coefficients to measure the strength of two
variables' linear relationship.
This model seems to prove that those who eat a lot of cheese are
usually betrayed by their bedsheets! Photo credit: katexic on
You may be familiar with the terms 'positive correlation' and
its inverse, 'negative correlation'.
If the correlation coefficient is more than zero, that number
signals a positive correlation between the two variables. If the
correlation coefficient is less than zero, their relationship is
negative and if the correlation is zero - neither positive nor
negative, there is no relationship between the two variables.
Applying that knowledge to our wage gap hypothesis, now:
the correlation coefficient is a positive number: wages
increase the higher the education level
the correlation coefficient is a negative number: the lower
the education level, the higher the wage and vice versa
the correlation coefficient is 0: the level of education
has no impact on wages and wages do not reflect workers'
education levels
You might wonder how workers with a lower level of education
could out-earn a university-educated worker. In fact, that happens
much more often than you might think! Picture an entry-level
manager with no university degree who, by virtue of a promotion,
has moved from hourly wages to a salaried position. Unlike wager
earners do, s/he won't earn any overtime and, because of that
entry-level status, may not yet qualify for any bonuses. Of
course, that is a vastly oversimplified example - suitable
for this introduction to econometrics, but it does show how wage
earners can out-earn management workers. And, overall, it gives
a fair snapshot of how econometrists turn raw data into concrete
numbers through quantitative analysis. Find various economics
tutor here on Superprof.
Goal Number 3: Forecasting
After all of that modelling and analysis, you might think the
econometrist's job is finished... but it isn't, yet. Now, they
have to forecast what will happen if the current statistics
continue in play and what policymakers should change to improve
the economic outlook. How do they do that? Let's say there's
a positive correlation between university education and wages in
that particular industry. The logical conclusion is that, if
more workers had a degree, they would earn more - meaning they
would also spend (consume) more and pay more in taxes. Find more
online tutor for economics here on Superprof. Those are two main
reasons why policymakers consider this scenario, by the way.
The OECD is just one policymaking body that relies on
econometrists accurately forecasting economic phenomena. Photo
credit: Organisation for Economic Co-operation and Develop on
VisualhuntIn forecasting, econometrists consider risks that
might impact the prosperity of an economic phenomenon, usually up
to a set date in the future - maybe one year or five years, but
often at smaller intervals. In our wage gap scenario, such risks
might include a percentage of higher-paid employees finding work
in other fields or retiring, less demand for employees in that
field because that market is stagnant and how automation might
reduce the need for workers. They select the variables
most relevant to the economic question, develop statistical
models to predict how those variables might behave.
Reaching the three goals of econometrics - analysing, estimating and
forecasting, is exacting and demanding. Many entities concerned with
economic policy rely on econometrists to achieve these goals so those
policymakers can determine which direction to take in setting their
economic goals and boosting economic performance. Those entities
include the World Bank, the International Monetary Fund, and, of
course, the Organisation for Economic Cooperation and Development or
OECD. With that being said, we have to touch on the global economic
downturn of 2008, an event that few forecasters saw coming. With all of
that data scrutiny, how could they have missed the warning signs?
Economists and econometrists struggle to explain that still today. And
not just that event but the fallout from it, too, the effects of which
are still being felt around the world. Now add to those residual
effects to how the pandemic upended global economics... In light of
that revelation, do you think econometrics should be accorded the
importance it currently has?