# Core : Introduction to Econometrics : The basics

Learning Econometrics can be a daunting task. The financial sector and its revolving industries assume that Econometrics is the dividing quality between someone who dabbles in economic theory and is a *”economist”. *Philosophies may differ but in the pursuit of practical solutions the business world sees econometrics as one of the defining aspects in the arsenal of an economist. After all you wouldn’t want a architect who couldn’t translate his art and ideas into practical models.

This series aims to simplify and break down the basic tenets of Econometrics to give you a solid foundation and bank of information to tackle the field with confidence.

We are assuming you understand the gist of what Econometrics is about. If you don’t you can read up on Econometrics and its uses and/or why it is studied for a sharper context. That will aid your understanding.

In the first article we will be covering a few notions:

** 1) Statistical Inference**

** 2) The Research Format**

** 3) Gathering Data**

** 4) Introduction to the Econometric Model
5) Recommended reading to become a Econometrics guru!
6) Video version of article for revision
**

*1 – Statistical* Inference

Statistical Inference is a powerful word which can be important to your understanding of the field. Many students may do econometrics calculations but have a very poor understanding of the ”whys” and the ”hows”. The word ”Statistical” is a reference to the compiled stats for your project. The word ”inference” is using the evidence to see its support (or lack thereof) the hypotheses. All in all statistical inference is the notion of learning something about the real world through analyzing your data.

1. Statistical inference usually involves estimating economic parameters (such as elasticities, a basic economic concept most of you economics students will be aware of) using *econometrics methods.*

2. Predicting economic outcomes such as the level of income business X may make over a 15 year period.

3. Testing hypothesis rooted in economics such as questioning the impact of using more marketing on a firms profit level.

** **

**2 – The Research Format**

Empirical economics research follows a template format and it is important to know this. If you are a higher-education economics student if you haven’t come across this yet, you will.

1. Question

2. Relationships & Variables

3. Constructing Models

4. Choosing Data Methods

5. Sample Data & Analysis

6. Estimates & Tests

7. Validity

8. Consequences/Analyzing/Impact/Evaluation

**Confused? Yeah, me too. Those were just little pointers. Now lets explain all this ker-fluffle.**

More detail will be dispensed in following articles, but for now lets wrap our head around this simple structure.

1. Every project will start with a question. Any topic that you choose to tackle will involve some sort of question that you are working against. This will be the basis of all economic research.

2. Economic theory essentially opens up a new perspective of looking at the situation in your question. In this way we can start to see : what variables would be involved in the relationship(s)? Around this a economic model can be built on the hypotheses of interest. Questions will naturally occur as research gains traction but it is wise to have a solid foundation to guide you.

3. With our economic model we can start to assume a econometric model. This will make more sense later, but your ideas must now take on a **functional** form. It would be wise to make assumptions about possible error term candidates and its inherent nature.

4. Sample data is now obtained (see next section for more detail) and a method of analysis of stats can be chosen. This will be based on our assumptions made at the start of the project and our assumptions.

5. Estimates of the unknown parameters need to be calculated. This can be done with a statistics program. Predictions are made and tests are performed against the hypotheses.

6. This helps us to see the validity of our assumptions. We use our econometrics models and diagnostics are performed. E.g. was the correct functional form used? Are the variables relevant?

7. Lastly the implications of the empirical results are now analyzed. Your data and the tests performed against it now undergo statistical inference. This involves understanding the relationship of the variables, how they react with each other, the relationship they possess, the economic consequences and impact directly in line with your hypotheses.

**3 – Gathering Data**

*Understanding the difference between non-experimental and experimental data*

It is important to grasp how data is gathered. Most economic data is non-experimental. This is because it is usually ”observed” versus being drawn from a controlled experiment. You could imagine a controlled experiment easily in a field like science where variables are clearly defined and the outcome is observed. This is rare in the social sciences; which makes it especially challenging to define economic parameters.

**Non-Experimental Data**

An example of non-experimental data is **surveys.** In these cases variables are not repeatable or fixed hence being ”non-experimental”. This includes but is not limited telephone surveys, mail surveys, face-to-face surveys and so on.

These non-experimental data types can be *collected in various* ways:

*1-Time-Series Form Data*

This is data collected over discrete interval of time. For example : the price of item X from 1880 – 2007; or the value of Item Y from the time period 2001 – 2014.

*2- Cross-Section Form Data*

This type of data is collected over sample units. This means units in a [particular] time period. For example : Income of a firm in the year 2001, or the level crime went down in 2006.

*3- Panel-Data Form*

This data follows individual micro-units over time. This is not to be confused with time-series form data. For example : the U.S. Department of Education has ongoing surveys tracking students from the 8th grade to their mid-twenties. Such data sources are useful because they are rich in studies related to economics of labor, household, health, education and more. Usually collecting this data is to explore a deeper issue or to tackle a issue from beyond the surface level.

**There is also two other basic denominations of data:**

All economics students should be aware of this : but still I will briefly refresh your memory.

- *Micro* : Data collected against individual units such as, individuals, households, or singular firms.

- *Macro*: Data that is an aggregate (or total sum) : such as, individuals in a county/state/country, firms at a local/state/national level.

**Another two basic denominations for you to memorize:**

Data can either be flow or stock.

- *Flow* : Outcome measured **over** a certain period of time. Example : consumption of fruits in Q3 of 1997.

- *Stock*: Outcome measured **at** a certain period of time. Example : the amount of excess stock held by Adidas in warehouses in March 2001.

**The final two:**

The data can either be qualitative or quantitative.

-*Quantitative*: When data is expressed numbers or a function of numbers, such as real capita per income.

-*Qualitative*: Outcomes that are of an ”either-or” quality; or non-numerical. Example : A consumer did or did not purchase a certain good. Or whether an individual is either married or he isn’t.

**4**** – Introduction to the Econometric model**

Oh no. Time to introduce you to some of the nitty-gritty. Don’t worry we are keeping it as simple as possible. Make sure you get the gist of what is being talked about the rest will align itself eventually.

Economic theory does not claim to be accurate to the point where it can predict exact behavior patterns of individuals and firms. Economic relations are never exact. Rather the ** average systematic **behavior of *many *individuals/firms is considered. When studying a data-set for sales of Nike shoes we can see the actual amount of shoes sold is the sum of the systematic part. The random unpredictable component (which economists call ) is considered a random error. With this data we can use statistical inference against the hypotheses created against Nike; such as ”How has ”X” marketing campaign affected net profit in Q4 of 2014”. The sales reference the systematic and accurate aspect of the equation. The random error ( ) references the unknown and unpredictable part of the equation. This will be present in any scenario at all times as everything can not be predicted. By taking [[facts]] and the [[unknown]] into account; general trends and relationships between variables and their strength can be discerned through econometrics. We may learn that the Marketing Campaign failed due to a large random error; where a natural disaster caused the warehouses holding the first wave of promotional products for the press got flooded.

Lastly it is important to understand that in every Econometric model there are certain assumptions that need to be made. Whether it is a demand or supply equation, or a production function : there is always a systematic part and a unobservable random part as we have just discussed. The represents a ”noise” or ”grey area” which blurs our understanding of the relationships between variables and this assumption is always present.

*5 – Recommended reading to become a Econometrics guru!*

We all want to sharpen our Econometrics swords so we can display our statiscal swordplay in the battlefield, but what books should one use?

These are books I’d use to start off learning about Econometrics and they will lead you to quite a strong position by the end.

I personally used quite a few. You know what they say…steel sharpens steel! Here are my top picks!

There are a quite a few econometrics textbooks and everyone seems to prefer their own so it is worthwhile investing in a two or three and cross-referencing topics between them all.

1. The first book I used was Essentials of Econometrics by Damodar Gujurati. Probably my favourite. Most universities actually use Woolridge or Greene textbooks to start off with but I feel this was one is more…friendly. Compared to other books I’d say its easier to stay on track and it gives you a focused understanding on Econometrics. It is also easier on students that have not practiced maths for a while. I do prefer this to Gujuratis newer book Econometrics by example, but you’re free to try both if you like. Many students vouch by this book, and prefer the exercise-based approach. So do you prefer more theory or more examples?

Essentials of Econometrics (Int’l Ed)

2. Verbeek’s ‘A modern guide to Econometrics’ is another fine contender for first book! This and Damodar are great texts to read before jumping into something like Woolridge. If you are starting Econometrics early you can get both and then get a more dense book next year otherwise. The mix-and-match scenario you create is up to you!

A Guide to Modern Econometrics

3. This next book is actually the one I heavily used for this article. Woolridge’s Introductory Econometrics : A modern approach. Students vouch for this book as their economics bible and that they need no other! Woolridge and Greene fight for the most space in mainstream university syllabus prerequisites. This one is good too guys.

Introductory Econometrics: A Modern Approach

4. Here comes the mighty Greene! This book is great if you actually plan on becoming very proficient at Economics. It may be difficult to work through if you are self-studying and not attending any sort of lectures. So keep that in mind if you choose Greene. On the other hand Greene probably has some of the best ”reference material”. It is a great dictionary of-sorts. So be careful not to try and study everything in this book in one year. Its impossible. Use your syllabus topics. If you are not looking for a reference-centric book pick another title.

5. This is a very underrated book. Kennedy’s econometrics guide has a unique structure where it tackles each topic from a multi-levelled perspective. Many students regret not finding this book earlier, but I wouldn’t recommend it as a first book. This book also shouldn’t be used alone.

6. Once you’re done with your books and want to prepare for advanced econometrics or for PhD studies then Hayashis ‘Econometrics’ title is a great bet. Asides from the semi-complicated notations it is probably the best introduction to advanced econometrics I’ve come across.

**What would I pick?**

If I was starting econometrics I would grab one of the Gujurati books for sure (depending on your learning style), Verbeek, and Woolridge.

Verbeek and Kennedy would be interchangeable for me. Pick either. Woolridge and Gujurati are essentials. If you want Greene (its a popular text) thats on your own discretion.

Move up to Hayashi when you’ve mastered these books and want to continue to learn!

There is also a video version if you prefer watching:

#### Kshatriya

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