What is Econometrics? - Definition, Principles & Models

Instructor: James Blackburn

James has an MBA from Auburn University and a MA in Humanities from Cal State-Dominguez Hills He writes on leadership, business strategy and finance.

In this lesson we will define econometrics, identify its uses, and explain some basic formulas used to create models. We will also explore the range of uses for econometric models.

Asking Economic Questions

For the year 2003, the World Bank estimated that 6.7% of the population in the Dominican Republic were below the global poverty level of $1.90 USD per day. Ten years later, that number had fallen to about 2.3%.

Likewise, during a comparable 10 year period, Mexico reduced its percent of citizens living below the global poverty line from 4.82% to 3.04%. How did these two countries achieve this level of improvement?


After reading this report from the World Bank, a working hypothesis was formed. It suggested that the off-shoring of jobs to countries with high levels of poverty improves the overall wealth of these countries.

The increase in manufacturing jobs would also stimulate growth in support jobs, things like transportation, food service, retail, and building trades. The wages from these new jobs trickle into the local economy, creating work for the local population and reinforcing growth and a rise in the standard wage. As more jobs are added to support these factories, support jobs increase, and the number of people living in poverty decreases.

But, how do we prove this hypothesis is true? Maybe we should build an econometric model.

Econometrics applies statistical methods and mathematical techniques to data to explain phenomena and create models. The difference between ordinary statistics and econometrics is that econometrics is typically used to explain how the economy works, specifically. Before we begin the hard work of analyzing data, let's look at a simple example.

Econometrics Models

We will start with a set of sample data and make an estimate on the impact of new jobs on an economy. We can start by calculating the mean or average growth per new job added. This calculation provides us with what is known as a statistical inference, a generalization about the growth of the population based on a smaller sample of that population.

From there, we can build a model that may predict the expected growth for each new job added to a local community. This expected growth is called the probability and is often expressed in percentage points. For example, if one new support job is added for every ten new factory jobs, we have a 10% probability of job growth.

Simple Models - Regression Analysis

For our purposes, let's keep with this simple relationship of 1 new job for every 10 factory jobs. If we have 20 new factory jobs, the support jobs will grow by 2. If the economy adds 100 new factory jobs, then the number of new support jobs would be 10.

Let's set x equal to the number of factory jobs and y equal to the number of support jobs. We know that for every 10 factory jobs we add one support job. So, the relationship between x and y is 10y = x. When we convert the formula into its linear form, y = mx + B, we end up with y = x/10. Or, the formula can be expressed y = .10x.

Graphs are great tools when examining data. They can provide a visual interpretation of simple and complex data sets. When we graph this equation, it looks like this:


If you compare the two lines, you may notice the data had a perfect relationship of 1 new support job for every 10 new manufacturing jobs. In reality, this rarely happens.

In a scatter plot, however, we see that the relationship between the manufacturing jobs and the support jobs are not as perfect. The growth of new jobs vary slightly. Although, we still observe a plot that looks like a line. So, we can use the y = mx + B formula to build a model.


The formula for this growth curve is y = .10x + 30. The new company will bring a total of 30 support jobs not dependent on the number of workers. When we add 1,000 new manufacturing jobs we get 100 new support jobs added to the 30 jobs we already have in the economy. The economy has grown to 130 support jobs in addition to the 1,000 new manufacturing jobs.

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