What is the difference between statistics and econometrics

My training was much broader but in some ways shallower. Because of the nature of economic data, econometricians have developed some specific techniques for handling time series and regression problems.

In particular, econometricians have thought very carefully about causality, because it is usually not possible to conduct experiments within economics and finance, and so they have developed several methods to help identify potentially causal relationships.

These developments do not always filter back to the general statistical community, although they can be very useful. For example, the method of instrumental variables which allows consistent estimation when the explanatory variables are correlated with the error term of a regression model can be used to help identify potentially causal relationships.

For some reason, econometricians have never really taken on the benefits of the generalized linear modelling framework. So you are more likely to see an econometrician use a probit model than a logistic regression, for example. Probit models tended to go out of fashion in statistics after the GLM revolution prompted by Nelder and Wedderburn The two communities have developed their own sets of terminology that can be confusing.

In other areas, they use the same term for different concepts. This obviously has the potential for great confusion, which is evident in the Wikipedia articles on fixed effects and robust regression. Econometrics and statistics have common overlapping areas that some people may find confusing. While both of these fields deal with statistics and the relationship between data, they are different. Before learning how these two differ from each other, it is crucial to understand what they are.

Econometrics is a field within economics that involves the quantification of economic data. Econometrics uses statistical and mathematical models to analyze economic theories. This process has a crucial application within economics.

Similarly, through econometrics, analysts can test and develop economic theories. They can also use the information in predictive modeling. For example, analysts can create time series models using the application of econometrics. Econometrics includes three primary areas. These include mathematics, statistics, and economic theory.

However, econometrics is not the same as mathematical science, economic statistics, or general economic theory. Instead, it combines all of these to help analysts understand the quantitative relations in modern economic life.

Statistics is a much broader concept compared to econometrics. For example, Vytlacil showed that the classic IV assumptions and monotonicity are equivalent to a Roy model with an index switching threshold a variant of a classic economic model , which allows economists to understand statistical assumptions from an economics perspective. Econometrics originally came from statistics.

In general statistics is more general than econometrics, since while econometrics focuses in Statistical Inference, Statistics also deals with other important fields such as Design of Experiments and Sampling techiniques.

However, today I may undoubtedly assert that Econometrics has also largely contributed to statistics as well. The first time I heard about linear regression was in the Physics lab when I was still a student of chemical engineering.

I am not sure the specific class I was really having, but we may consider here that my class was a experiment to estimate the elasticity coefficient of a spring Even if your knowledge of physics is very limited, you can understand this experiment.

Soon, the spring will expand and knowing Hooke's Law , the equilibrium position of the mass will be that in which the weight is equal to the force generated by the deformation of the spring. If you put different masses, you will have different deformations.

This situation is very rare in econometrics. Consider the following social-economic problem that comes from the field of Economics of Crime where cities would like to know how much they would need to increase the number of policemen to reduce crime. Therefore, the model of interest could take the following form:. This model suggests that the number of crimes decreases with the number of policemen. Interpretation : If the number of policemen increase, the incentive to commit crimes reduce.

This model says that mayors respond to the number of crimes, increasing the number of policemen or a higher number of policemen is associated with areas of greater crime. The cause and effect in this situation is not clear. This problem is called endogeneity and it is the rule in economics. In this case, the error term is not exogenous it is easy to prove that and we know that this is the most important assumption that we have to consider to ensure that the estimated parameters of our model are not biased.

Disclaimer: This is a classical model that is very easy to explain in economics. I am not suggesting or not suggesting that the number of policemen should be increased or not given the recent events that took place in USA. I am just talking about simple models to point some ideas.

In many field in statistics, we are able to create experiments to generate the data we need. For instance, we want to test the effect of a drug. We divide the population in two parts and the first part receives the treatment and the second part does not receive it receives the placebo. For instance, we may not play with the interest rate to estimate its effect on inflation. If we do that many people may lose their jobs due to a recession or may cause an hyper-inflation or a scape of international capital.

Having said that in many situations in economics we have to leave with the data is out there, that is subject to lots of problems. So, the focus of econometrics is to arrive to relations as Cause-Effect as we found in the example with a spring above with an imperfect data.

In econometrics the role of theory is very important. Usually economists want to test hypothesys. So the model is build in order to test these hypothesis.

An economic model may then be put in place to help answer the question or complete the study. In short, these models can test economic parameters, review elasticities, predict economic outcomes, or test a hypothesis based on assumptions initially made during the early stages of the study. Statistics and econometrics are necessary to properly define how to use these models and solve economic problems. Other analyses will test the economic consequences of actions, as in what happens when a company makes a poor decision.

Osmand Vitez.