Econometrics and Statistics Software | INOMIC

Economists very often work with statistical software, which is used to create economic models and perform econometric analysis. Learning how to manipulate and analyze data is therefore an essential skill for young economists. To be competitive in the job market as an economist, demonstrable skills and experience in using some of the popular analysis and forecasting software environments are a must.

Broadly speaking, there are two types of software: proprietary and open source. Some individuals and organizations choose to use proprietary packages developed and copyrighted by a single company. Others rely on free, open-source solutions, like the popular “R” project. Within these two categories, there are dozens, if not hundreds, of alternatives, varying in price, complexity, performance, ease of use, and popularity.

This post by Bob Muenchen is a great resource if you’re looking for an overview of the most popular packages. He uses various measures to evaluate different software options and keep them up to date. Note that this survey is intended for data science in general—not just economics. However, all common economics software programs are represented – such as Python, R, Stata, SAS, SPSS, MATLAB and more.

That’s quite a long list of names, and it can sound intimidating. To help you understand the software tool landscape, in this article we’re going to take a look at the most popular software packages for economists and provide some details on how to learn more about them.

1) Which software should I use?

Before you buy a package or download an open source option, it’s a good idea to talk to peers, visit online forums or communities related to any interesting product, review your course schedules if you’re a student , and free trials to use.

It’s also important to consider your own skills and/or the expertise available in your team (if applicable). If there are several software packages that fit your needs, options with better support can save you time and frustration in the long run.

However, economics students tend to focus their studies on the main non-proprietary open source tools. Many courses use open source tools for teaching. Python and R are two examples. R is very often used by economists. Python, on the other hand, is used a little less frequently in economics, but is used extensively in data science, which increasingly overlaps with econometric analysis that economists may wish to undertake. There are many statistics packages for Python that allow it to perform the same analysis as other popular packages like R.

So familiarity with both packages pays off. There is a lot of free educational material on both R and Python available online; Knowing how to perform regressions and manipulate data in these programs is a major asset for a serious economics student.

However, proprietary software platforms with paid licenses such as Stata, SPSS and MATLAB offer other benefits. The support and training options behind these platforms are often more extensive than with open source software. Additionally, these tools might be easier to use for economists with little programming experience, with well-designed user interfaces that allow economists to manipulate graphs and variables where open-source tools might instead require lines of code.

2) What kind of license should I buy?

If you are a university student or an employee of a company using a proprietary software tool, chances are you already have access to the software without having to purchase it for your personal use.

Otherwise, if you want to use these options, you have to pay for an appropriate license. The criteria for different license types vary from software manufacturer to software manufacturer. Typically, student licenses are the cheapest and are offered for shorter periods (weeks or months rather than one or more years).

Many software packages also have an academic and/or non-profit license available. Enterprise licenses are usually the most expensive – but you probably don’t need to purchase an enterprise license yourself. Review the criteria for each package and contact your local reseller to find out what type of license and appropriate level of training and support is right for you.

3) Should I buy directly or from a dealer?

If you decide to buy, it is almost always possible to buy directly from the software manufacturer through their website. However, consider looking for a local retailer instead. Pricing is usually (with exceptions) more or less the same, but distributors have perks for the “extras” you get as a customer, such as: B. In-house support, newsletters and local user group meetings.

In addition, on-site support and training in your language can be a huge plus. Another benefit is that you have one stop for everyone when purchasing different software packages.

4) How much coding or programming proficiency do I need?

Regardless of the software package you choose, you may be wondering how much programming experience you need to be a successful economist. Do you need in-depth knowledge of data science, programming or coding, and economic theory to be successful?

Most likely, the answer for you is “not really”. Most economists don’t have to be master programmers. Defining your own functions, manipulating data, and understanding the algorithms behind common functions will make your life in economic analysis easier. In-depth expertise is probably not required as the software tools are designed to help you do your job – but of course they will give you an advantage.

For example, most software packages come with built-in commands that are short and easy to execute, allowing you to dive right into statistical analysis. For example, to run linear regression in R, all you have to do is type in a command and tell the program what data to use. Thereafter, entering “Summary” produces a linear regression analysis complete with t-statistics and p-values, the R2 -value, F-statistics and many more, all calculated in a fraction of a second. Many other, more complicated regressions can often be performed in a similarly simple manner.

Running these simple commands doesn’t require advanced programming experience, but a basic knowledge of how to use your chosen software will often come in handy. Suppose you get an error message. Familiarity with your software of choice and the algorithm used to generate your model output will help you troubleshoot quickly so you can get back to the interesting stuff.

Additionally, if you have programming skills, you can often write your own functions and understand what the existing functions do. This gives you extra flexibility if you need to implement a very specific probability distribution, re-parameterize something, customize a plot to emphasize your results, etc.

Overall, the key to success in such economic analysis lies in understanding the model output and interpreting it in the context of economic theory, which does not require advanced programming experience. However, if something goes wrong or you need a particularly complicated setup to run your analysis, some coding skills are a blessing. Studying some of the software packages mentioned in this article will help you get started on your economic analysis journey. Much luck!

Credit: Gilad Lotan