Econometrics - Meaning, Techniques and its Application

Published - 2022-07-07

Basics of Econometrics - Techniques and its Application

The quantitative application of statistical and mathematical models to data is known as econometrics. It is used to create new economic theories, test old ones, and predict future trends based on historical data. It performs statistical tests on actual data before contrasting the findings with the theory or ideas under investigation.

Econometrics can be classified into two main groups: theoretical and applied, depending on whether your goal is to test an existing theory or to use current data to create a new hypothesis based on those observations. The term "econometricians" refers to people who regularly engage in this profession.

Meaning of Econometrics

To test or advance economic theory, econometrics analyses data using statistical techniques. These techniques use tools like frequency distributions, probability, and probability distributions, statistical inference, correlation analysis, simple and multiple regression analysis, simultaneous equations models, and time series methods to quantify and analyze economic theories based on statistical inferences. Concepts like fundamentals of accounting, inflation and deflation, and basics of taxation are some broad concepts that come under the umbrella term of economics but econometrics is something different, let's find out how.

Utilizing observable data to analyze the income effect is one example of how econometrics is applied. An economist would assert that as someone's income rises, their spending will follow. If the data support such an association, a regression analysis can be performed to determine the magnitude of the association between income and consumption and whether or not it is statistically significant—that is, whether it is unlikely that the association is the result of pure chance.

Techniques and Its Applications

It is challenging to describe all the approaches, tools, and strategies that belong under the umbrella of econometrics because there is no definite border. Descriptive statistics, hypothesis testing, regression, and forecasting are the four major categories I've divided econometrics methods into because of this and the fact that I'm creating this essay for data scientists.

Let's explore the concepts of econometrics in more detail -

Statistical descriptions

In data science initiatives, descriptive statistics are crucial to exploratory data analysis (EDA). Using statistical methods, descriptive statistics calculates the central tendency, dispersion, and distribution of the data. Measures of central tendency in econometrics consist of a group of "middle" values that are typical of all the observations in a dataset. The dispersion of data is described, emphasizing the hub around which all other data are gathered. One of the central tendency measures is

A data point's average is measured by the mean. Some of its variations include arithmetic, geometric, weighted, and harmonic means. An alternative to the mean, the median is the data's midpoint. The advantage of median over mean is that it is less sensitive to outliers. The distribution's mode quantifies the values that occur most frequently.

Dispersion: Unlike central tendency, measurements of dispersion quantify the variability in a dataset, that is, how the data are spread in relation to the central values.

Range, Interquartile Range (IQR), Standard Deviation, Variance, Mean Absolute Deviation, Coefficient of Variation, Gini Coefficient, etc. are examples of commonly used measures of dispersion in econometrics.

Testing Hypotheses

Generally speaking, hypothesis testing relates to comparing a claim to known facts (sometimes known as the "null hypothesis"). The assertion about the entire population is validated using sample data. The assertion that residents of Arlington County live longer than those of Fairfax County seems plausible. Since it is impossible to survey everyone, a researcher would select samples from the populations in both counties to compare the null hypothesis with the hypothesis (i.e., the claim) (i.e. there is no difference in life expectancy between counties).

Cats, dogs, and birds make up the expected outcome (null). However, the survey's observed results showed that there were two cats, ten dogs, and three birds. The hypothesis that pet preferences are significantly different will be tested using the Chi-Squared test.

In hypothesis testing, there are two other crucial ideas to consider:

The assertion (hypothesis) is supported or refuted using a statistic known as the p-value; a low p-value allows the null hypothesis to be rejected (i.e. the claim is statistically valid). According to statistics, the null hypothesis (known facts) might be rejected because there is only a 1% possibility that the outcome is the result of chance.

Confidence intervals (CI), a metric for the level of uncertainty, are another idea in hypothesis testing. A parameter's possible range of values is provided by CI.

Regression

I could write endlessly about regression because it is such a broad subject. However, I've summarised the primary approaches and related econometrics-related methodologies and models below.

For continuous dependent variables, linear models are a common strategy. Simple and multiple regression are two distinct methods in the family of linear models. One independent variable and one dependent variable make up simple linear regression (e.g. weight vs height). On the other hand, multiple linear regression uses many explanatory variables (e.g. weight explained by height and age). There are numerous variations.

Multiple regression model conceptualization

In order to model time-series data, panel data models are specialized regression approaches. The approach is effective at predicting time-dependent observations. Pooled OLS (ordinary least square), Fixed Effects models, and Random Effects models are a few of the methods utilized in panel data models.

Modelling count data (such as the number of crimes) as a function of covariates is done using count data models (e.g. unemployment, income). Ordinary regression fails because it predicts non-integer or negative values, which is illogical for counting numbers. Poisson and Negative Binomial are two techniques for count data regression.

When the dependent variable is binary (e.g., yes/no, approve/disapprove), binary outcome models are utilized. It resembles machine issues with two classes of classification. In econometrics, binary outcomes are modelled using the Logit and Probit models.

GLM (Generalized Linear Models)

When linear models are unsuccessful, either because the result is count data or because it is continuous but not normally distributed, GLMs (Generalized Linear Models) are utilized. A GLM is made up of three parts: an exponential family of probability distributions makes up the random part; a linear predictor makes up the systematic part; and a link function, which generalizes linear regression, makes up the third part.

Forecasting

Similar to regression, forecasting is a well-studied, important issue. It should come as no surprise that data scientists have access to a wide range of forecasting tools. Again, I won't get too deeply into theory; instead, I'll concentrate on the specific tools and methods utilized in the field of econometrics. These methods are frequently intimately related to one another, and the shortcomings of one method influenced the creation of another.

- Benchmark forecasting: This category of models is also referred to as "baseline" forecasting. Although these methods are rarely used in practice, they aid in developing forecasting intuition, which can then be strengthened by the addition of more layers of complexity. Some benchmark forecasting methods include: Mean, Seasonal Naive, Drift, Linear Trend, Random Walk, and Geometric.

- Exponential Smoothing: A time series can be broken down into three different parts: trend, seasonality, and white noise (i.e., random data points). We can foresee the predictable elements (such as trend and seasonality) for forecasting purposes, but not the unpredictable terms, which arise at random. This kind of variation within a series can be handled by exponential smoothing, which eliminates white noise. Simple exponential smoothing, Holt's Linear Trend, and Holt-Winter exponential smoothing are a few examples of exponential smoothing.

To summarise

Let's go over the various subjects that are covered in this post. An old subfield of economics is econometrics. It models economic and social processes using statistical and mathematical methods. Many of those methods and tools can also be used to address conventional data science and machine learning issues.