National open university of nigeria introduction to econometrics I eco 355

1.0.
 
INTRODUCTION 
Econometrics contains statistical tools to help you defend or test assertions in economic 
theory. For example, you think that the production in an economy is in Cobb-Douglas 
form. But do data support your hypothesis? Econometrics can help you in this case. 
To be able to learn econometrics by yourself, you need to have a good 
mathematics/statistics background. Otherwise it will be hard. Econometrics is the 
application of mathematics, statistical methods, and computer science to economic data 
and is described as the branch of economics that aims to give empirical content to 
economic relations. 
 
2.0.
 
OBJECTIVES 
At the end of this unit, you should be able to: 

know the meaning of Econometrics and why Econometrics is important within 
Economics. 

know how to use Econometrics for Assessing Economic Model 

understand what is Financial Econometrics.
3.0.
 
MAIN CONTENT 

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36 
3.1. WHY IS ECONOMETRICS IMPORTANT WITHIN ECONOMICS?
So Econometrics is important for a couple of reasons though I would strongly urge you to 
be very wary of econometric conclusions and I will explain why in a minute.
1. It provides an easy way to test statistical significance so in theory, if we specify our 
econometric models properly and avoid common problems (i.e. heteroskedasticity or 
strongly correlated independent variables etc.), then it can let us know if we can say 
either, no there is no statistical significance or yes there is. That just means that for the 
data set we have at hand, we can or cannot rule out significance. Problems with this: 
Correlation does not prove causality. It is theory which we use to demonstrate causality 
but we most definitely cannot use it to "discover" new relationships (only theory can be 
used to tell us what causes what, for example, we may find a strong statistical 
significance between someone declaring red is their favorite color and income, but this 
obviously 
not 
an 
important 
relationship 
just 
chance)
Another problem is that many times people run many regressions until they find one that 
"fits" their idea. So think about it this way, you use confidence intervals in economics, so 
if you are testing for a 95% confidence interval run 10 different regressions and you have 
a 40% chance of having a regression model tell you there is statistical significance when 
there isn't. Drop this number to 90% and you have a 65% chance. Alot of shady 
researchers do exactly this, play around with data series and specifications until they get 
something that says their theory is right then publish it. So remember, be wary of 
regression analysis and really only use it to refute your hypotheses and never to "prove" 
something.
Regression analysis is your friend and you will see how people love to use it. If you don't 
understand econometrics very well, particularly how to be able to sift through the 
different specifications so that you rule out any poorly specified models, and so that you 
understand what all these crazy numbers they are throwing at you mean. If you don't 
know econometrics yet try reading some papers using regression analysis and notice how 
you don't know what any of the regression analysis means. This should give you an idea 
of 
why 
you 
need 
to 
learn 
it.
However, many people use it, and believe me many people get undergraduate degrees in 
economics without knowing econometrics and this makes you less capable then those 
peers of yours who did learn it.