How can you teach econometrics to students with no statistical background?

The concept of causation is intrinsically relevant to us, human beings, since we want to understand the structure of reality, how phenomenon X has effects on phenomenon Y. In the lack of a proper experiment, our scientific knowledge about causation links can improve only thanks to multivariate regression ("econometrics"), i.e., by taking into account other confounding factors that -if left omitted in our statistical analysis- would confound the measurement of the causal link etween phenomenon A and B. Let's be honest: in social sciences it is still not that common to run a proper experiment, hence we strongly need the help of econometrics and multivariate regression.

I strongly advise students to complete Calculus 1 and 2, Linear Algebra, Statistics 1 and 2, one programming course, and both Micro and Macro Economics 1 and 2 prior to undertaking Econometrics. This foundational knowledge enriches the learning experience in Econometrics, linking theoretical concepts to real-world applications with greater depth and understanding. While a basic introductory course in Econometrics can be conducted without these prerequisites, its educational value would be significantly diminished.

Although its approaches can easily be extended to other fields, econometrics can be broadly summarised as the way to use statistics within the field of economics to find relationships between variables, if there is, causality, making forecasts, etc. There are multiple ways to introduce it via real-world examples (e.g., the news of the day/week: microeconomists like to show the relationship between education years and salary, while macroeconomists often start from the very simply accounting breakdown of GDP's components. Regardless of the chosen approach, it is key to instill curiosity and show how relevant a sound measurement of relationship is. The next step is put the "hands" on these issues, introducing software, e.g., Stata/Python.

Explaining the importance and relevance of econometrics for economics at the course's beginning with examples will surely motivate students. Exploring what students had learned in their macroeconomics or microeconomics classes and relating it with real-world data will be interesting. However, as econometrics heavily relies on statistical tools, students must understand these tools well. A three-hour crash course in statistics can help students follow the econometrics course more effectively.

Possible, as long as students have the willingness to study. They can start exploring simpler models, such as simple linear regression, the logit model, two-way ANOVA, and time series regression. Instead of focusing on theory, students should have the opportunity to work with actual data, enabling them to understand the rationale for each approach. By beginning with these basic models, students can gradually build their understanding of the principles and methods of econometrics and apply their knowledge to real-world economic problems, preparing them for practical challenges in the field.

I teach the crucial concept of "omitted variable bias" by using an example, i.e., the estimation of the causal effect of education on lifetime earnings. On one side we cannot run an experiment by randomising years of education across individuals. On the other side, by looking at the correlation between years of schooling and earnings we are likely to overestimate the effect, because of omitted variables, such as ability of the person. This is the case because ability is positively correlated with education (easier to study more if you are smart) and with earnings (if you are smart you earn more). So, you wrongly attribute to education the effects of ability on earnings, unless you control for it within a multivariate regression.

Here, I think an incremental learning approach can be highly effective. Start with the basic principles of statistics and gradually introduce more complex econometric models. This step-by-step progression allows students to build a solid foundation before tackling advanced topics. Employing interactive tools such as econometrics software in practical sessions can also help in demystifying complex concepts, making them more approachable and less intimidating for beginners.

Start with the basics of statistics, covering concepts such as mean, median, mode, standard deviation, and probability. Make sure students understand these foundational statistical principles before moving on to more advanced topics in econometrics. and Introduce econometric concepts using real-world examples that students can relate to. Relate statistical concepts to economic scenarios and problems to make the material more engaging and practical.

In addition to this, I use daily life scenarios to better explain the course. Something we all can relate to. I can paint a story for example using the methodology of econometrics.

Most of us have some operating hypotheses. How would go about stating & testing these hypotheses? A useful illustration is building simple time series models and evaluating them for their usefulness in constructing forecasts.

The best way to help students with econometrics (in the lack of formal training in statistics) is to make them work with a statistical package such as Stata or R. With data on years of education and lifetime earnings you can directly show the role played by omitted variable bias, by letting them run a simple regression without control variables, take note of the estimated coefficient on education, and then compare it with a simple multivariate regression, whereas you add a proxy of ability such as the level of IQ (intelligence quotient). The students can clearly see that the coefficient on years of education in this multivariate model is significantly lower than in the simple regression model. Here is omitted variable bias at work!

One challenge with econometric softwares such as R, STATA etc. is that they can very often be 'black boxes'. Students would know how to use the command, but often would not relate or understand what it is actually happening. They would, for instance, forget that an OLS is actually an optimisation (and can be solved with a simple tool such as Solver in Excel), or put too much attention on the p-values and under-appreciate the difference between statistical significance and economic significance. I think using synthetic data (i.e., constructed data in a controlled environment), such as random draws generated from a given distribution, can be of great help to appreciate the trade-offs and features of econometric outputs.

I think Utilizing statistical software with a user-friendly interface. Tools like R, Python (with libraries like Pandas and Statsmodels), or software specifically designed for econometrics (e.g., EViews, Gretl) can make it easier for students to implement statistical techniques without delving too deeply into programming complexities. and Assign projects that require students to collect and analyze data using econometric methods. Practical applications can reinforce theoretical concepts and help students see the value of econometrics in solving real-world economic problems.

In using software tools, I have found that it's important to start with Excel. Microsoft Excel is widely available and can fuel interest by starting from easier concepts dealing with correlations and graphical presentations, before moving into deeper regressions. Upon grasping Excel and subsequently boosting their confidence in the field, we can introduce SPSS and then R. Of recent R has taken root thanks in part to its increased usage by the MITx online courses. Personally, I believe opting to get acquainted with Python would come easier once the individuals are better equipped at Stata and R.

I taught undergraduate econometrics many years ago at Koc University. The students I taught did have some basic statistical background but had not been exposed to more advanced methods. While we went through the basic OLS model in the course, what really taught them was a very extensive applied problem where they had to derive all the estimates and tests and to interpret them. Students told me later that this clinched the course for them. For many undergraduate Economics students, the introductory econometrics course still remains as perhaps the most feared course in their curriculum. However, my students of past years were able to get past their fear when they understood in concrete terms what an econometric exercise entailed.

Use real-world datasets to illustrate the application of econometric methods. Analyzing actual economic data can make the material more relevant and interesting for students. and Connect econometric methods to economic theory. Show students how econometrics helps economists test hypotheses, estimate parameters, and make predictions based on economic models. So Create an open and inclusive classroom environment where students feel comfortable asking questions. Foster discussions about the interpretation of results and the implications of various econometric techniques.

I Encourage peer learning and collaboration. Students can benefit from discussing concepts with their peers, explaining ideas to each other, and working on group projects that involve applying econometric techniques.

If you are learning online by yourself, try building a simple econometric model to test some hypothesis from the real world. Interpret the results and think about what it would mean. Post the results on a site such as linkedin or stackoverflow, review the comments and revise your model.

Econometrics, in simple terms, is hypothesis testing. Everyone unconsciously hypothesises about many things almost every day, a good teacher should use it and help students think like an economist. Ask students - What do you think are some aspects that will determine their earnings in life? You’ll get a lot of answers - ability, grades, SAT Score, parental income, etc. Then you can tell them how to create a model out of it and how we can find the most important aspect (regression analysis) and why ability is hard to measure (unobservables) and hence it biases the analysis (omitted variable bias) Graphs can also help in teaching first year students. TLDR: Use real-life examples, help people think like an economist, and use visual cues.

Econometrics is best elaborated in the context of modeling.. a student is really engaged when she/he realizes that econometrics is having explanatory and forecasting powers.. relationships between variables are best modeled using econometrics techniques.. students must start from the computer lab and they must not be disconnected from the economic theory.. it is a combination of inductive and deductive methods in the economic research

When teaching econometrics to students without a statistical background, it is important to incorporate visual aids to enhance their understanding. It is also crucial to break down complex topics into smaller, simpler components to avoid overwhelming students.

Recommend supplementary reading materials, video tutorials, or online resources that explain statistical concepts in an accessible manner. Offer extra support for students who need more assistance in building their statistical knowledge. Remember that patience, clarity, and a supportive learning environment are key when teaching econometrics to students with no statistical background. Tailor your approach to accommodate diverse learning styles and pace the material to ensure that students develop a solid foundation in both statistics and econometrics.

Teaching Tips: Visual Aids: Utilize graphs and charts for better comprehension. Simplified Approach: Break down complex topics into smaller components. Real-World Examples: Illustrate concepts with practical, relatable situations. Statistical Software: Integrate tools for calculations, allowing focus on concepts. Patience and Support: Recognize that students may need extra time and assistance in grasping statistical concepts.