Introduction to Econometrics
Economics is a vast subject, and there are some subtopics that many people don’t really understand – econometrics is one of them. As much as economics is part of our modern daily life, many people look at it very shallowly. But there is more to it than just knowing where world economies are heading to and what should be done in case of an issue. And when it comes to digging dipper, one must be able to understand numbers. In other words, economics is highly tied to mathematics, and understand one leads to the best knowledge of the other.
One thing is clear that without economic studies, there would be no civilization and growth for the human race. Therefore, you should be proud as a student taking studies in this subject. It lets you make better judgments and decisions that may later affect what others do altogether. Economic students have been a major boost to world economic growth for many years, and they continue to hold a central place.
Understanding the in-depth concept of economy leads to a better understanding of consumer and firm decisions – which is necessary for making important decisions. Policymakers need to compare numbers and measure possible outcomes to save their economies. And this is the reason this subject has been introduced, whereby those who take it as their major are exposed to a large world of data and statistics. It is hard to imagine a world of economics without these two ideas. No business or economy will make decisions without comparing available data and creating a stable understanding of every information involved in creating economic ideas. This is where econometrics comes in, playing a vital role, or presenting economic data, comparing previous and current information.
What is Econometrics?
Economic studies and understanding are all about comparing economies in order to make proper decisions. Econometrics is the branch of economics that deals with the measurements of these economic relationships. It is the understanding of the relationship between economics, mathematical economics, and statistics. This integration aims at providing numerical values to the parameters of economic relationships. There is usually the involvement of economic theories presented in mathematical forms and combined with empirical business studies.
The methods in econometrist are used in obtaining values and figures of parameters, which are basically coefficients of the mathematical form of these relationships. There is also the use of statistical methods, which assists in presenting a clear comprehension of the economic idea. These methods are used as econometric methods as part of understanding data. The econometric relationships are vital because they show random behavior of economic links, in general, are not regarded in economics and mathematical formulas.
It is also important to note that econometric methods can be applied in certain areas of economic growth. For instance, experts in engineering sciences, biological, medical, geo, and agricultural sciences, among other fields, require this information to make estimates on important decisions affecting their firms and general human behavior in consumption. In other words, there is always a need to discover the stochastic relationship in mathematical format, in which experts turn to econometric approaches and tools for assistance. The tools in econometric ideas help in presenting clarity on the relationships as well as other variables.
Models in econometrics
In order to understand and help other people understand economic relationships, experts need to establish a clear path to follow. It is, therefore, necessary to create a model – a simplified representation of a real-world procedure – which makes it easy to follow data in economics and economies. A model is like a and should, therefore, contain representative features, in that it must carry the salient characteristics of the area under study. In other words, when someone looks at a model, they should be able to link directly with real-life situations. Modeling is used to, among other objectives, create oversimplified models, which leads to unrealistic assumptions sometimes. Practically, there should be variables that the person carrying out and experiment on think are vital in explaining the subject under study. And this is where the difference between economic models and econometric models arise. All other variables that are not important at any stage are thrown aside as mere “disturbances” (which sometimes could be a random variable. This line of understanding also creates the difference between economic modeling and statistical modeling. A mathematical model is naturally exact, but the statistical model comes with an additional stochastic term.
It is important to understand the differences that make economic models and the other models describes above. Ideally, the economic model is a set of assumptions applied in the description of the behavior of an economy. Or sometimes, it flies under the general umbrella as a phenomenon. An econometric model is characterized by:
- A group of equations drawn from the economic model, which is used to explain the behavior. The equations come in two parts, one as observed variable and the second as disturbances.
- It also consists of a statement on the errors arising from the involved values of the variables.
- Thirdly the model bears a set of specifications on the distribution of probability in disturbances.
Considering these features, we can say that econometric models are specific ideas used to understand econometric data. It is a vital aspect of understanding this critical subject of economics.
Why is Econometric important?
As stated above, the world of economics highly depends on data for building the decision process. For this reason, it is important to understand the main aims of econometric. There are three major aims of econometrics.
First, econometrics aims are the creation and specification of econometric models. Note that there could be several models drawn from an economic model. The models are chosen based on different functional forms that differ due to specifications within the model. It also differs due to specifics of the stochastic structure of the variables, among other features.
Secondly, we have already seen that these models are estimated based on observed sets of information and tested for stability. Statistical inference of modeling relies on this idea to build more reliable data. There must be the application of different estimation ways in order to understand the numerical values of the hidden parameters within the model. Creating a suitable and appropriate model should be chosen based on different formulas within the statistical modeling mode.
Third, econometrics aims are proving the most suitable use of models. Ones obtained, these models are applied in the forecast8ing and policymaking. This is very important in the formulation decision of any policy decision. We already know how fiscal, monetary and other policies are vital to the growth of an economy when there is an issue – which happens a lot due to economic cycles – specific policies are built to carb its impact. For this to happen, policymakers need crucial data, which comes from economic relationships, to determine which models suit the specific condition. Besides, economic experts use these ideas to forecast a possible turn within the economy. Hence, it assists policymakers in judging the importance of and applicability of the fitted model, enabling them to take necessary steps in re-setting relevant variables within the economy.
There is no denying that data is a critical asset for economic processes. Without data, there would be no proper estimation of what would happen next within the economy and how to deliver the best results. This should not come as a surprise since almost everything in the modern world depends on data. Consumers and firms are faced with decisions on a daily basis for making the best choices. For this reason, they need to understand what may happen next as a result of their decisions. And that is why econometrics is very vital.
Econometrics vs. Statistics
There are all sorts of data in economics, which comes from various methods of data sourcing. Mostly, people use statistics and probability to get this information. This is why it is easy for one to assume that statistics is also econometrics. After all, word statistics do appear in the definition of econometrics. However, it is crucial to note that econometrics is widely different from both mathematical statistics and economic statistics.
For the case of economic statistics, there is the use of empirical data, which is collected, tabulated, and applied in the description of the pattern in the growth over a certain period. Economic statistics is ideally an aspect of describing economics through statistical data. Economists compare information on past economic processes and put them together as a way to understand what might happen later. This means economic statistics do not give us either an explanation of the growth of different variables or measurement of the parameter of the links.
Statistical methods have become critical to modern economists as a method of measurement, which is created based on experiments. These methods may not be the best methods for the economic phenomenon because they cannot be used in the framework of controlled researches. A good instance of this is in the real world, where variables keep changing, and simultaneously, which means the set up of controlled experiments may not be necessary.
Even though econometrics is not statistics, it needs statistical methods after adapting them to resolve economic problems in real life. These statistical ways are usually referred to as economic methods. They are altered from time to time so that they are appropriate for measuring stochastic relations. Adjustments are crucial because they try to create specific attempts in a stochastic element that works in real-world information and can be used in determining real data. In this case, data is called a random sample, just like in probability, and is needed for applying statistical ideas.
The idea of theoretical econometrics has become an important part of modern economics. It includes the development of critical methods for measuring economic relations, which cannot be found within the confines of labs, but in the real world. Econometric methods are basically used to develop the analysis of non-experimental information.
Also, there is the aspect of applied econometrics, which is the use of econometric methods in specific areas of econometric theory. It is also used in solving issues like demand, supply, production, investment, consumption, and many other problems. In applied econometrics, there is a need for the use of econometric theory tools in the process of analyzing the economy and forecasting it the paths it may take next.
There are various terms that are crucial to understanding econometrics. It is important to know them because they form a wider perspective of the subject. Consider the following.
Data is the backbone of econometrics. And there are various types of data, including:
- Time series data: this is the data that gives information about the numeral value of a variable at different times. They are collected over a specific period. A good example could be the data within the years 1990 to 2010 for monthly wages.
- Cross-section data. Cross-sectional data offer information on the variables about individual agents (like consumers and producers) over a given time. Information such as a sample of consumers can be a sample of family budgets that include different commodities as consumed with the family.
- Panel data: This is data from repeated research of one specific sample at different times.
- Dummy variable data. Data is recorded as an indicator function when the variables are qualitative. Their values don’t show the weight of data. They only show the presence or absence of certain parameters.
Sometimes aggregative variables are used to represent functions. In this case, they lead to aggregation problems. Such variables may be used as aggregation over individuals, aggregation over commodities, aggregation overtime periods, and spatial aggregation.
Econometrics, as seen before, carries a critical function in economic analysis. One of these important roles is to offer the tools of modeling based on data. Regression modeling is a method that is fundamental in the process. The regression model may be linear or non-linear, which leads to linear regression analysis and non-linear regression analysis. Linear regression analysis is important because it helps to establish the most critical data.
Author: James Hamilton