Econometrics is the quantitative application of statistical conclusions, economic theory, and mathematical models using data to create theories or verify preexisting assumptions in economics and estimate future trends from the massive amount of data gathered over time.

Its purpose is to statistically analyze real-world data and then compare the results to the theory or ideas being tested for patterns.

To put it another way, it evaluates theoretical economic models and applies them to the formulation of economic policy.

The primary purpose of econometrics is to translate qualitative assertions into quantitative ones.

Stock and Watson’s 2007 book states that “Econometric methods are used in many branches of economics, including finance, labour economics, macroeconomics, microeconomics, and economic policy.”

The three men who are credited with founding econometrics—Lawrence Klein, Ragnar Frisch, and Simon Kuznets—also received the 1971 Nobel Prize in economics for their achievements. The likes of academics and professionals like Wall Street traders and analysts use it widely today.

Econometrics can be further divided into theoretical and applied econometrics depending on whether your goal is to evaluate an existing theory or to use current data to create a new hypothesis based on those findings.

Types of Econometrics

Theoretical Econometrics

It is the study of the characteristics of current statistical models and approaches for figuring out the model’s unobserved values. In doing this, we want to create fresh statistical methods that are still reliable in the face of the simultaneous changes in economic data.

The capacity of the new methods to make accurate inferences is demonstrated by theoretical econometrics, which largely relies on tools like mathematics, theoretical statistics, and numerical numbers.

The generic linear model, simultaneous equations models, distributed lags, and other related subjects are the focus of theoretical econometrics. The majority of these issues came up when conducting empirical study.

Applied Econometrics

In contrast to the theoretical approach, econometric procedures are specifically used to translate qualitative economic assertions into quantitative ones. Due of their increased familiarity with the data, applied econometricians frequently encounter issues with data attributes that indicate flaws in the current repertoire of estimate procedures. They also notify their theoretical counterparts about any abnormalities.

The areas covered by applied econometrics include the production of goods and their productivity, demand for labor, the theory of arbitrage pricing, and housing-related concerns.

The Main Tool of Econometrics

The linear multiple regression model, which is the primary tool in econometrics, aids in estimating how changes in one of the explanatory variables impact the model’s operation from the explanatory variable to changes in the Dependent Variable. Even though several statistical procedures have gained prominence in contemporary econometrics, simple linear regression is still the most frequently utilized analytical starting point.

This phase is essential because, after accounting for the variance brought on by the other explanatory variables’ effects on the model, a regression tends to estimate the marginal influence of a particular explanatory variable.

The Stages of Econometrics

Suggestion of Theory

The first step in beginning to examine a specific piece of data is to put up a theory or hypothesis. The model’s explanatory variables are predefined, and the sign and/or magnitude of each explanatory variable’s relationship to the dependent variable are distinctly determined in order to avoid any ambiguity.

Applied economists are now involved, and they largely rely on economic theory to successfully construct a hypothesis out of the available data.

Specifying a Statistical Model

The second phase entails defining a statistical model that perfectly encapsulates the theory. Through the model, the economist seeks to suggest a special relationship between the dependent variable and the explanatory variables.

Assuming linearity is by far the simplest strategy because it guarantees that any change in one explanatory variable will always result in a corresponding change in the other. Since it is hard to take into account every single factor that can have an impact on the dependent variable, a variable is included in the statistical model to account for external disturbances.

The new variable’s function in this situation is to represent all the factors that affect the dependent variable but cannot be taken into account. Mostly brought on by the data’s intricacy.

Economists commonly assume that this “error” term averages to zero and is unpredictable in order to be consistent and ensure that all assumptions for the statistical model are correct.

Estimating Variables

The third stage entails estimating the model’s unknown variables using available economic data. This technique often requires the use of a suitable statistical method and an econometric software program.

Due to the simple accessibility of a wealth of economic data and the superior econometric methods and tools, this is regarded as the easiest element of the analysis. The infamous GIGO (garbage in, garbage out) approach of computation is still used in econometrics.

Proof-Reading

This is the fourth and most crucial phase of them all. Asking ourselves the correct questions is a necessary step in this process. For instance,

• Do the estimated parameters connecting the dependent variable to the explanatory variables’ signs and relationships match those predicted by economic theory?
• How should the statistical model be modified by an econometrician to produce appropriate findings if the estimated parameters are illogical?
• And does an economically significant model follow from a more precise estimate?

Particularly at this stage, the econometrician’s proficiency and knowledge are put to the test.

Functions of Econometrics

• To put to the test the prized econometricians’ proposed economic theories or hypotheses. Is consumption, for instance, a direct result of income? Is the demand for a particular good inversely correlated with its price?
• To offer quantitative estimations for the economic relationship’s variable. These are crucial for making decisions.

For instance, in order to comprehend the stimulative effect of a suggested tax decrease and come to a wise conclusion, a government policymaker has to have a precise estimate of the coefficient of the link between consumption and income.

• To forecast monetary occurrences. This is also essential in order for policymakers to behave in an economically sensible manner in the event that inflation or unemployment rates are expected to increase in the future.

It is no secret that economics rules the globe, and econometrics allows for the daily rejection or approval of statistical models as well as the proof of new hypotheses and inferences.

Econometrics splits the universe into countless theoretical possibilities, which are supplemented by the facts that are given to econometricians.

These facts are particularly informative in the eyes of policymakers, who rely on these conclusions to formulate crucial policies and decisions.