Multi-variate Time-series Analysis Using VECM Identifies the Best Set of Exogenous Predictors for Rainfall and Temperature in India: A Data Analytical Approach

Abstract

The complex nature of climate change with multitude of underlying factors poses a major hindrance in data analysis and decision making by policy makers. Here, we utilize data analytic techniques to identify the best set of climate change indicator variables that could predict precipitation and temperature data for India. The observed values of important climatic parameters namely, rain, maximum temperature, minimum temperature, and mean temperature in India were analyzed along with the observed values of selected socio-environmental indicators featured by WHO for climate change as exogenous variables for a period of 61 years. Data were pre-processed to identify ten exogenous indicators which were then modelled using Vector Error Correction Model (VECM). 1024 VECM models were built and evaluated for the prediction of the four endogenous variables using all possible combinations of the selected indicators. Seven exogenous variables were determined as the best set of indicators based on the AIC of the different models. The model built using the identified variables was compared to others to illustrate the probable impact of this combination of variables. The study thus demonstrates a simple but rational data-driven approach for use in decision making.