Before delving into the detailed study of econometrics, it’s crucial to understand its basics, specifically what econometrics is. Without a foundational understanding of the discipline, gaining broader knowledge becomes challenging. Let’s initiate our discussion to know its general overview.
Introduction to Econometrics
Econometrics is the combination of Mathematics, Economics, and Statistics. It employs statistical methods to analyze economic data. However, econometric models extend beyond economics; it can be applied to other fields, such as management, political science, and marketing.
Econometrics assists us in various ways, for:
- Estimating the relationship between economic variables,
- Forecasting macroeconomic variables, such as gross domestic product (GDP),
- Testing theories and hypotheses,
- Evaluating and implementing government and business policies, etc.
Note: Its scope isn’t confined to economics; for instance, we can apply econometrics to scrutinize political campaign expenditures on voting outcomes.
Steps in Empirical Economic Analysis:
Our research objective is to address research questions by describing, explaining, generalizing, or predicting through data analysis. Empirical analysis (research) utilizes data to confirm or reject a theory or estimate a relationship between variables to answer our questions.
Generally, the following steps are involved in using econometrics for empirical economic analysis:
Constructing the Economic Model:
The economic model comprises mathematical equations describing the relationship among economic variables. Consider the following demand function as an example:
D = f(P, I) (where D = Demand, P = Price, I = Income).
This represents the relationship between demand and factors affecting the demand for a commodity and this is an example of an economic model.
Constructing the Econometric Model:
The next step is constructing an econometric model based on the economic model, incorporating variables and parameters. Developing an econometric model for the above economic model might look like the following:
D = β₀ + β₁P + β₂I + u (Where β₀ is a constant, β₁ is the parameter to estimate the relationship between Price and Demand, and β₂ and β₃ have similar meanings for their respective variables).
While deciding the econometric model, consideration should be given to dealing with variables that cannot be directly observed or measured, such as motivation. Similarly, if you are studying the wage rate of males and females, you can not develop a model like wage=β₀ + β₁male+ β₂female + u, because of the perfect collinearity issue.
Data Collection and Analysis:
After defining the econometric model, the next task is collecting and analyzing data to find our parameter of interest (beta coefficient) and understand the relationship among variables. In our example, we collect data on quantity demanded, Price, and Income at a specific point in time from different individuals.
Conclusion
In conclusion, econometrics is the study of economic variables (by definition but not limited to economics) using statistical methods. It helps us to transpose an economic model to an econometric model. Advanced econometrics models can analyze different structures of data.
For a detailed understanding, I recommend reading Introductory Econometrics: A Modern Approach
