Economic performance analysis of Indian FMCG industry using multiple linear regression

Abstract

Seeing that the steady pace of development and evolution of modern society has left its mark on the phenomena in all fields of activity, including in the field of economics, the analysis of economic performances, as a major preoccupation of firms, has made a huge qualitative leap, by shifting the focus to the exploitation of databases (through adequate techniques) and the thorough interpretation of results.

Starting from the idea that in economics, as well as in other sciences, anything has the tendency to depend on anything else, in this paper we have intended to develop an econometrical model capable of expressing the relation between the FMCG index as a fundamental indicator of expressing the FMCG industry’s financial-economic performance- and the factors affecting the results for the same using the multiple linear regression model. As a matter of fact, there is a focus on the current turmoil in the Indian economy slowdown as well and the effect of the macroeconomic factors related to the same on the economic performance of the FMCG industry. This research paper reflects the relationship between various macroeconomic factors and the NIFTY FMCG Index for a time period of past 3 years i.e. January 2016 – December 2019. The macroeconomic factors included in the research paper are Employment rate, Consumption of petroleum products (light distillates), CPI (Consumer Price Inflation) General Index, IIP (Index of industrial production), Index of consumer sentiments, Index of industrial production (Manufacturing), GDP (Gross domestic product) at current prices and the Total Rainfall Data. The macroeconomic factors over here are independent variables (x1,x2,x3,…) while the NIFTY FMCG index is the dependent variable.

 The multiple linear regression model has been developed through the analysis of data from the Fast-moving consumer goods (FMCG) industry and by using the EViews. Multiple Linear Regression Analysis using multiple tests comprising of t-test, f test, coefficient of correlation i.e. R^2, auto-collinearity using serial correlation LM test, heteroscedasticity, multicollinearity, stability and normalcy of error term(JB test) would be carried out on E-views software. The results will help get and idea about the relationship between various independent variables and their effect on the NIFTY FMCG index and will be helpful getting various conclusions from the same.