Econometric Modelling: How It Can Help In Business

by November 20, 2017 0 comments

Econometric modelling has become a colloquial buzzword, more so with the advent of the big data age. It is applied in all the fields extensively, be it for academic or commercial purposes. Though the term is used commonly, there are many wrong notions and conceptions surrounding the same. People differ in views related to the application of econometrics in business problems. However, no one can dispute the importance of econometrics in its fundamental operation in all the fields.


What is Econometrics and What It Does?

Literally interpreted, econometrics means ‘economic measurement’. It can be classified as a social science in which tools of economic theory, mathematics, and statistical inferences are applied to get some insight from data. Econometrics is often associated with public policies and forecasts, but it offers a lot more than politically motivated macroeconomic forecasts. So, econometrics is simply a set of tools that have been developed to verify economic theory with the real-world data.

Traditionally, the methodology of econometrics goes along the following lines:

•   Statement of theory or hypothesis or an assertion

•   Specification of a mathematical model of the theory

•   Specification of the framework of a statistical or econometric model

•   Obtaining empirical or real time-structured

•   Estimation of the parameters of the specified econometric model

•   Testing of hypothesis

•   Forecasting or prediction


Using the Model for Business Decisions and Policy Purposes

Econometrics is usually a “model-driven” approach whereas statistics is a “data-driven” approach. An econometric model is used to forecast future developments in the economy. The model is said to be complete if it contains enough equations to predict values for all the variables in the model. If the model is complete, it can, in principle, be used to forecast the behavior of the variables. However, in reality, no econometric model is ever truly complete. All models contain some variables that are affected by external forces and thus cannot be predicted by the model. An economic forecast can go wrong for – (a) incorrect assumptions about the external or exogenous variables, which are known as input errors; or (b) econometric equations that are only approximations to the truth. Deviations of the predictions from these equations are called model errors. The less the model error, the better fitted the model.


Benefits of Econometric Modelling

Some benefits of applying econometrics modelling are listed below:

Analysis of The Impact of Promotions, Advertisements Events: Advertisements and promotions involve a major share of a firm’s costs. With advanced time series forecasting and econometrics modelling, it can be easier to gauge the effectiveness of the campaigns.

Modelling of Customer Choices and Price Elasticities: It is one of the most difficult tasks to match the trend of changing consumer behavior. Modelling customer choices and preferences based on various attributes can improve the firm’s strategy, decision-making and thus, in turn, save costs and resources.

Measurement and Prediction of Marketing and Investment Activities: It is essential to understand the key drivers affecting consumer demand. Modelling of demand based on marketing or media mix activities can measure the impact of pricing, advertising, and other investment activities. Using optimization tools, those investment activities can be modified to drive profitable growth.

Modelling of Risk Factors and Prediction of Economic Outcomes: Decision-making in areas where risk factors are high becomes difficult. Econometric modeling can help minimise the risk and predict outcomes with some probability to make the decision process easier.

Better Resource Allocation Decisions: Forecasting of demand for services can help to allocate staff resources appropriately. Efficient staff allocation facilitates meeting customer needs with no wasted resources and less delay.


Common Forms of Econometric Modelling Used in Business

Marketing Mix Modelling (MMM)

It is one of the most common forms of the model used by firms which gives a complete high-level view of the performance of each marketing channel of the firm against single specific consumer behavior.

Brand Equity Modelling

It gives a high-level view of the performance of marketing channels throughout the consumer journey and against multiple consumer outcomes.

Long-Term (LT) Modelling

It gives a high-level view of the impact of marketing channels in the long run. It identifies the vehicles of marketing growth for long-term.


Types of data and Time-series modelling

There are generally three types of data available for analysis-time-series, cross-section, and pooled data. Time-series data consists of a set of values on a variable over time. Cross-section data is data on one or more variable collected at the same point in time. Pooled data or panel data is a combination of both time-series and cross-section data. Businesses mostly collect time-series data and so most of the modelling done for business purposes is time-series modelling.

The time-series modelling procedure has the advantage of being simple. It can be used to forecast variables without having to worry about the theoretical interrelationship between related variables. Forecasting is done based solely on historical values. So, it can be used to analyze past as well as predict future. Time-series models are thus used extensively by firms.



Econometric models are power tools that can drive the business ahead, helping in smarter decision making, optimisation of costs and better understanding of consumer demands. But, it is essential to choose the right model since there can be different kinds of modelling techniques each of which dealing with different individual tactical, brand or portfolio questions. The modelling results should be incorporated into strategy and operation. It is also necessary to update the model as necessary when the market shifts or fluctuates to get a proper insight from the data.

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