modeling and forecasting of climatic parameters: univariate sarima versus multivariate vector autoregression approach

Agriculture sector throughout the world including Bangladesh is extremely vulnerable to the negative consequences of climate change as evident in a good number of studies. Accurate climate forecasting may prove a valuable resource in mitigating these consequences in agriculture. The study aims to identify the best performing forecasting method by comparing the forecasting abilities of univariate seasonal autoregressive integrated moving average (SARIMA) and multivariate vector autoregression (VAR) models in forecasting monthly maximum and minimum temperatures, humidity, and cloud coverage in Bangladesh. Though the univariate time series investigate the influence of the past values of a single time series on the future values of that specific series, the VAR approach forecast multivariate time series simultaneously incorporating the interrelationship among the groups of variables. Monthly forecasts of climatic parameters for in-sample over the period 1972-2008 and out-of-sample from 2009-2013 were generated via a univariate SARIMA and a VAR approach. Different forecast accuracy measures reveal that VAR model give better forecast than univariate SARIMA model. The forecast results using VAR(9) model from January 2014 to December 2021 show that maximum and minimum temperature, as well as humidity are increasing while the cloud coverage is decreasing, that is, consistent with global warming. Moreover, the impulse response function results exhibit the fluctuated and significant dynamic relationships in future among the foresaid climatic variables. Thus, findings of the study can potentially allow Bangladeshi farmers and other actors in the agriculture sector to make proper planning to abate unwanted impacts or reap the expected benefits of favourable climate.