Vector Error Correction Model to Forecasting Spot Prices for Coffee Commodities During Covid-19 Pandemic

Abstract

Abstract Forecasting time series commonly shows non-stationer behavior and involves interrelated variables, so a method that able to obtain a good forecasting result from a non-stationary multivariate time series data is needed. Vector Error Correction Model (VECM) as one of multivariate time series model is a vector form of restricted Vector Autoregressive (VAR) for non-stationary data which has Cointegrity relationship. Prices of agricultural commodities futures usually show variability and unsystematic behavior, especially in times of facing the Covid-19 pandemic as it is today, so getting complete information about data requires data handling while still considering the concept data-driven. Coffee, as one of agricultural commodity data, is often not known for its stationarity. The purpose of this study is to identify the reliability of VECM to predict the spot price of commodities futures coffee during the Covid-19 pandemic in Indonesia. The used data is several observed variables, there are: spot price of Robusta Coffee, futures prices of Robusta Coffee, exchange rates and daily case positive Covid-19 in Indonesia. The results of this study described the Robusta Coffee data in Indonesia as non-stationary and there is a long-term cointegration relationship to the Robusta Coffee futures price movement due to the influence of the Covid-19 pandemic.