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Introduction to Econometric Application

Econometrics is one of the major subjects in the study of economics. It is a subject that deals with the measurement of economic relationships. In other words, econometrics is the link between economics, mathematical economics, and statistics. It aims at offering numerical values to the parameters of economic relationships. Economists use theories to determine the cause and action of specific economic processes. Econometric theories are expressed as mathematical figures, and they appear in the form of combined econometric relations. The use of econometric techniques for the sake of identifying the values and parameters that are crucial coefficients of the mathematical forms.

Economic statistics help inn defining the real nature of the economic phenomenon, whereby it is adapted as econometric methods. The relationship created by the econometric process reveals a random behavior of economic links that may not generally be economics and mathematical formulas. It is important to mention that econometrics is applicable in economics and in other areas of study as well. For instance, econometric methods largely appear in engineering sciences, biological sciences, medical sciences, geosciences, and many other areas where a stochastic relationship is expected in mathematical forms. In other words, econometrics is applied to almost every human existence field as it is used to drive economies to the end. Wherever there is a need to explain a relationship between variables, econometric tools come in as the best tools for finding the right results.

The use of econometric models

As stated above, econometrics studies relationships between variables. When referring to economic processes, there is a need to verify these relationships are part of the real-world process. And that is where economic models come in. An econometric model should always be representative in nature, in that should carry all the salient aspect of the subject, or area being studied. Generally, modeling has many features, and one of them is that is should have a simple model that explains a more complicated phenomenon. A model can be seen as a tangible representation of a real-life situation, which means its objectives must be clearly defined. These objectives may, at times, lead to the oversimplified model, and sometimes it makes unrealistic assumptions. It is all in the nature of models, and the person carrying out a project should be aware of these aspects. The model carries all the variables relevant to the study and which the experimenter thinks are crucial to the study. This is where the main difference between economic models and econometric models appear. It differentiates mathematical models and statistical models. On the one hand, math models are exact in nature, whereas statistical models are also characterized by a stochastic term.

Sometimes understanding an economic model and drawing the difference with econometric modeling can be a bit tricky. However, a few things should make it easy for you to understand these two crucial aspects of modern economies. Nobody said understanding economies has to be easy, yet it is fundamental to humanity’s general well-being. An economic model can be simply termed as a set of assumptions that described an economy’s behavior. For instance, when there is a bubble, an economic model uses different assumptions to explain how it got there and what might happen next. An econometric model is more specific because it contains three major components: a group of equations derived from an economic model, describing the phenomenon – and it features two parts, observed variables, and disturbances; a statement about the errors within studied values of variables and; a specification of the probability distribution of disturbances.

What are the aims of econometrics?

When studying econometric applications, it is important to consider the aims that econometric studies seek to achieve. This will help you describe every application based on its aims. And since econometrics studies relationships, such aims should form a critical aspect of studying the subject. Based on this understanding, there are three major goals econometrics seeks to achieve.

Formulating and specifying econometric models

As discussed above, there is a huge difference between economic modeling and econometric modeling. Econometrics models are described by numbers and figures, which means that they are easily verifiable. Again, economic models are created in an empirical testable from. A single economic model can give rise to several econometric models. This means economic modeling is more general, but when you look at it from an econometric point of view, the models can differ based on different choices of function and specification of the stochastic structure of the components, among other factors. When the experimenter chooses to go with econometric models, they seek to establish a deeper connection between the variables and create specific points of action. This means econometric models touch the most basic areas of human existence. This aim lets the examiner split down economic models until they have reason to take a specified cause.

Estimating and testing models

Again, this aim is linked to the definition of econometrics, where relationships are involved. In this case, the models are estimated based on what has been observed, and the data is tested for suitability. Econometrics is not only about finding data but clearly defining how useful the data is in different setups. Different estimation procedures are applied in understanding the numerical values of the unknown parameters of the theory. There is always a need to select the most suitable and appropriate model to explain events with econometric approaches. The model is chosen based on the differed formulation of statistical models.

Model application

Econometric findings are built on figures and crucial data. Hence, the models are used to forecast and policy formulation. These are very crucial aspects of making policy decisions. Policymakers look for real evidence that a certain cause of action will bring out the most desired results. As such, these forecasts assist the policymaker in judging the efficacy of the fitted model. They can then take the necessary measures to re-adjust the relevant aspects of an economy. Policymakers are crucial to the success or failure of the economic process; this is why they have to be certain about every decision they make. They don’t have the luxury of going with the flow but creating a suitable economic growth environment.

Econometrics for statistical process

It is crucial to understand the difference between mathematical statistics and economic statistics. Even though they both deal with figures and probability, certain variables make them applicable subject-wise. Economic statistics begin with collecting empirical data, which is then recorded, tabulated, and used in explaining how they develop time. In simple terms, it presents the description nature of economics. In economic statistics, there is no explanation of the development of various variables. There is also no measurement of the parameters of the relationships under study.

Statistical methods are used the description of the methods of measurement developed from controlled experiments. Therefore, these methods may not be very applicable to the economic phenomenon since they don’t fit in such experiments. Real-life experiments carry variables that usually change continuously, and at the same time, as such, the setup of a controlled experiment may not give the most desired results.

In econometrics, statistical methods are adapted to the issues facing economic life. Examiners of these real-life situations adopt statistical methods termed as econometric methods, and they are adjusted so that they turn into appropriate use for measuring stochastic relationships. The adjustments are crucial because they try to specify efforts to the stochastic element operating in real-world data. Also, the adjustment facilitates turning the information into determining factors for observed data. When this happens, data becomes a random sample, which is a crucial object in statistical tools.

In theoretical econometrics, there is the development of appropriate methods for measuring relations in economics. The relationships are not applicable in controlled experiments because real-life variables are involved, which can change at any time. The development of econometric methods seeks to analyze non-experimental data. When we consider the aspect of applied econometrics, we meet the application of econometric methods to specified branches of econometric theory. It can also be useful in finding a solution to economic problems like demand, supply, production, investment, consumption, and many other aspects of the general economy. This type of econometrics involves the use of tools of an econometric model for economic analysis and forecasting.

Statistical economics is all about data. And there are various types of data used in estimating the model. They include:

- Time series data. This data deals with information on numerical values. For instance, the monthly income data between 2000 and 2015.

- Cross-sectional data. This data provides information about the variable on specific variables, like consumers or suppliers, at a specific point in time. Say, for instance, a researcher is looking for a sample of consumers, they have to take a sample of a family budget with information on their expenditure on different goods.

- Panel data. Data that comes from repeated research on cross-sectional data at different times is known as panel data.

- Dummy data. Sometimes variables can be qualitative in nature, in which case in data is recorded as the indicator function. The value of the variable doesn’t show the extent of the data. These variables appear as the absences or presence of a variable. For instance, we can look at religion, sex, or taste as qualitative aspects. When taking demographic data about sex, there are only two values, male and female, to be used. For taste, you either go with value-like or dislike. These values are presented as dummy variables. A researcher can, for example, use ‘1’ to represent males and ‘0’ to represent females.

Solving the aggregation problem

The aggregate variable can be used as functions, a case that gives rise to aggregation problems. There are various aggregative variables that can be applied in economic analysis. They include the following:

- Aggregation over individuals.

A good case is when the total income comprises the sum of individual income. When looking at the incomes of a household, for example, to determine consumption patterns, in this case, its individual incomes may be considered in combination.

- Over commodities.

Aggregation can also be over commodities, in which case the number of different commodities can be aggregated over aspects like price, or group of goods. A suitable index is needed for this aggregation.

- Overtime period.

Aggregation over time happens when data is available for shorter or longer periods of time that should be used in the form of a function of an economic relationship. In these situations, data aggregation happens over the specified period of time. For instance, the production of many manufacturing goods happens in less than 12 months. If one uses annual data, there will be errors in the production function. They will need to go back and look at the data more specifically or use a formula that takes the error into consideration.

- Spatial.

Aggregation can also be linked to spatial problems. Variables like the population of towns, countries, or production of a city/region are classified as spatial.

With these aggregation sources, it becomes hard to avoid the issue of “aggregate bias” when estimating the coefficients. For this reason, the researcher must consider the possibility of such errors whenever they embark on estimating the model.

Regression analysis

Econometrics is also defined by its critical role in providing tools for modeling on the ground of present data. It is the regression theory that helps us in undertaking this task. Regression models are defined as either linear or non-linear if they have linear or non-linear regression study. Linear models are the most commonly used, whereby the theory and fundamentals of the linear model create the background for constructing the tools for regression analysis.

Regression analysis follows a basic framework that includes the following steps:

- The statement of the issue being examined

- Choosing appropriate variables

- Collecting data on the variable

- Model specification

- Choosing the right method for fitting the data

- Fitting the model.

- Validation and criticism

- Applying the chosen model


There is no denying the econometrics is an important aspect of modern economics. As seen aboveEconometric Application, it can be applied in a wide range of fields that require data authentication and determining relationships. Studying econometrics should get your ready for the normal economic phenomenon.

Author: James Hamilton

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