There are several ways to define econometrics statistics. Economists using statistical methods to test a hypothesis lies under the realm of econometrics. Econometrics uses quantitative methods for analyzing real-time data. This article will shed some light on Econometrics and its five important aspects.
These techniques base on the previously established theories. An econometric technique makes several assumptions to analyze a dataset. These techniques allow economists to respond to economic questions by analyzing real-time data. Econometrics techniques convert real-time data into statistical models. Once the real-time data becomes a statistical function, it compares the findings. It compares the findings against the established theories.
In simple words, this field analyses theoretical economic models. It then uses the results for economic policymaking. Some economists make it even more simple. They suggest that econometric techniques convert qualitative arguments into quantitative functions. Depending on its use, there are two categories of econometrics. These two categories are theoretical and applied econometrics statistics.
Types of Econometrics
As mentioned above, the two types of econometrics statistics are theoretical and applied. Theoretical econometrics is the study of the existing statistical models. The field of econometrics aims to find new statistical procedures. The reason for developing them is to find valid statistical procedures despite the nature of economic data. These procedures change with the nature of economic data as per the need.
Theoretical econometrics relies heavily on quantitative procedures. This reliance implies the ability to assess new procedures. The assessment of these new procedures indicates their ability to draw conclusions. Theoretical econometrics allows discussing several issues which are on hand. It includes issues related to linear modeling, simultaneous equations modeling, and distributed lags. Analysts face these issues in empirical research (Moffatt, 2018).
The role of applied econometrics is different than theoretical econometrics statistics. Applied econometrics enables to the conversion of qualitative statements into quantitative ones. Applied econometrics is opposite of the theoretical econometrics. Under applied econometrics, it requires the econometricians to work closely with data. It can expose them to certain issues related to the data. In this regard, the attributes of data become important for econometricians. Issues with data attribute or show the points of error with existing techniques.
In this way, the issues with attributes of the data alert the theoretical econometricians. Through this process, theoretical econometricians get to know about the anomalies. Let’s take an example of the demand for labor from an economic phenomenon. Applied econometrics can help in understanding the nature of the data. For instance, it can help in analyzing if the data shows a large variance that is ever-changing.
Tools of Econometrics
According to a dissertation help firm, econometrics statistics presents a variety of models which are suitable for different datasets. A suitable model for a particular dataset fulfills the initial assumptions. These models help in observing a particular dataset. It also helps in finding a correlation between the datasets. Multiple linear regression is one of the most used econometric models. Multiple linear regression indicates a dependent variable as a function of the independent variable.
In other words, the multiple linear regression model is an estimation of one dependent variable on independent variables. This model predicts the dependent variable as a function of the independent variable. When a multiple regression model presents an interesting graph. The graph of multiple linear regression is a straight line presenting paired values. Under the multiple linear regression, the aim is to find unbiased estimators. Aside from that, efficient and consistent estimators are aimed at. Over the period, several other techniques are introduced in econometrics, but multiple linear regression is still the most used technique.
Step-by-Step Methodology of Econometrics
The methodology of econometrics statistics consists of a few simple stages. It is a straightforward methodology.
The first step in econometrics methodology is pretty simple. It implies choosing a particular theory and hypothesis. This theory and hypothesis are related to the objective of a particular study. Deciding upon a theory and developing a hypothesis enhances the ability to examine a dataset. The theory also helps in deciding the explanatory variables of the study. In addition, the magnitude and direction of the relationship are decided beforehand. Applied econometricians rely heavily on pre-determined economic theory. The hypothesis development also involves the expertise of applied econometricians.
Once the underlying theory is decided and a hypothesis is developed, the next step is choosing a statistical model. This statistical model should be able to capture the essence of the theory which is to be applied. This model must depict a unique relationship between the dependent variable and explanatory variables (Rawat, 2021).
The most convenient approach in finding a specific model is the assumption of linearity. According to this assumption, the change in the dependent variable due to the independent variable is similar. Linear relationship indicates that change in explanatory variable induces a similar change in the dependent variable. It is not possible to account for the little influence in a relationship.
Adding a variable to the model enables to cater to this influence. This variable nullifies external disturbances. The new variable will represent the determinants of the dependent variable. These determinants of the dependent variable should be accounted for by the new variable. These determinants are mostly caused by the complexity of data.
The next step indicates the estimating of the variables. This step indicates the estimation of the unknown in the model by using the economic data. This step can be achieved by selecting the right model and using software to fit the data. Model estimation can be the most convenient part of the econometrics statistics methodology. The use of efficient statistical software has made it relatively easy.
Once the model estimation is completed, the next step is proofreading. It also involves comparing the findings with the theoretical background. The findings are assessed as if they are consistent with previous results and the theoretical background. In this manner, the findings are used to test the hypothesis which was made previously in the study. Thus, empirical validity is ensured by assessing the findings of the study.