Modeling Multivariate Time Series by Vector Error Correction Models (VECM) (Study: PT Kalbe Farma Tbk. and PT Kimia Farma (Persero) Tbk)

Abstract

Time series analysis (time series) is one method with the aim to find out events that will occur in the future based on data and past circumstances. Time series are widely used in economics, business, environmental science, and finance. The analytical tool that is widely used to answer quantitative research problems is the Autoregressive Vector (VAR). The VAR model is used if the data is stationary. If the variable has cointegration and stationary at the first difference value, the VAR model is modified to become the Error Correction Model (VECM). Then we can find out the influence of variables with other variables by looking at the Impulse Response Function and Granger Causality. In this research, PT Kalbe Farma Tbk’s stock data will be analyzed. (KLBF) and PT Kimia Farma (Persero) Tbk (KAEF). The data used are weekly data from January 2010 to June 2020. Based on data analysis, it is known that the data is not stationary and there are unit roots. Furthermore, first differencing is done to make the data stationary. Because there was cointegration, a VECM analysis was performed and a VECM (p) was obtained with a lag of p = 4. So the best model for this research is VECM (4) with rank = 2. Causal relationships between variables using Granger Causality showed that KLBF influenced KAEF in the past. Based on IRF analysis, each variable gives a fluctuating response with itself and with other variables.

Assessment of the impact of climate and non-climatic parameters on cocoa production: a contextual analysis for Cameroon

“…However, some of these methods have limitations in capturing both short-and long-run relationships, which may lead to inaccurate results. For instance, Loves et al (2021) and Adinew and Gebresilasie (2019) used the cointegration and VECM estimation technique. Coulibaly et al (2017) employed the Autoregressive Distributed Lag (ARDL) method, while Coulibaly and Erbao (2019) tested the ECM to find significant short-run relationships between climate and the sector.…”

“…. , p, indicating no Granger causality effect (Lutkepohl, 2005;Loves et al, 2021). The Granger causality test bases on the Ftest, which aims to determine whether a change in another variable causes a change in one variable.…”

“…If cointegration between variables is established and confirmed to be stationary at the first difference value, the VAR model transforms into a Vector Error Correction Model (VECM). The VECM determines the influence of climatic and non-climatic variables on cocoa yield using the impulse response function and Granger causality (Tsay, 2014;Adinew and Gebresilasie, 2019;Loves et al, 2021). The VECM is a powerful tool for investigating the short-and long-term crop responses to changes in climatic and non-climatic factors.…”

Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM

“…In that case, the Vector Error Correction Model (VECM) is utilized. VECM modeling is used for multivariate time series data, which will then detect the causal relationship between variables using Granger Causality to visualize the effect of variable variability compared to other variables using the Impulse Response Function (IRF) [36].…”