The term 'Econometrics' is included in the Economics edition of the Financial Dictionary. Get your copy on Amazon in Kindle, Paperback or Audio edition. Check for lowest price here...
Econometrics refers to the utilization of math and statistics in the discipline of economics. Economists include these branches of study in order to test their hypotheses and theories. They attempt to predict future trends by employing it. The idea is to consider economic models and test and re-test them by using statistical trials. They finally contrast and compare the ultimate results against known real-world examples. This is why economists often divide up the study into the two groups of applied and theoretical.
Economists work econometrics by merging math, economic theory, and statistical hunches. Through these combinations they are able to analyze various theories. They harness a variety of tools including probability, frequency distributions, regression analyses, statistical inference, times series methods, and simultaneous equations models.
It is always helpful to consider a real life example to understand a difficult concept like Econometrics. Economists might choose to work this discipline in order to consider the idea of income effect. Many economists will theorize that individuals who boost their income will also expand their spending levels. The way they can test out such an economic hypothesis so that it becomes proven and accepted is with the tools of this discipline. These include multiple regression analysis and frequency distributions.
The field of Econometrics became discovered and advanced by the three renowned economists Ragnar Frisch, Lawrence Klein, and Simon Kuznets. The lot of them received the Nobel Prize in economics for their achievements and work with this discipline of economics.
Utilizing Econometrics in practice is not as difficult as it might at first seem. Step one is to consider a data set so as to come up with a particular hypothesis. This must give reasons for the shape and nature of the data. In such a first step, the variable which the employing economists will consider they must specifically define. Relationships between independent and dependent variables must also be detailed. It is this stage of the discipline which depends enormously on the economic theories to be tested for their usefulness as the study progresses.
In the next step, the economists will have to select their particular statistical model or tool with which they will test out the economic hypothesis. For a model to be considered effective, it will have to outline particular relationships mathematically between the dependent variables and the variables which explain them in the given test. The most typical tool economists use is the multiple linear regression model. It is because they consider this to be the most practical tool in the discipline. The reason for this is that the relationships can be expressed in a linear fashion. It is appropriate to many situations since the most typical relationship between data sets proves to be linear. All that this means is that a change in one variable will lead to another positive correlation with the other variables.
A third step revolves around entering in all of the data set information to a software program specifically created for econometrics. Such a program will utilize the economist chosen statistical model in order to tabulate the preliminary results. It employs the entered economic data to come up with these results.
Finally they come to the most critical (and also coincidentally last) step for proving a hypothesis using Econometrics. The economists in question gather the program’s outputted results and prepare a real-world type of test. It is this test that gives the economist the knowledge regarding the validity of the model that was proposed and tested. For the model to be useful, it must deliver reliable and accurate predictions which can be back tested and so proven. When the economists uncover the results they anticipated, then they know that their hypothesis is in fact a theory. Should the results not be what they anticipated, additional inferences or other hypotheses will be required.