This article explains testing and diagnosing VECM in STATA to ascertain whether this model is correct or not. Among diagnostic tests, common ones are tested for autocorrelation and test for normality.
STATA for data analysis, time series analysisUnrestricted Vector Auto Regression (VAR) is not applicable in such cases. Vector Error Correction Model (VECM) is a special case of VAR which takes into account the cointegrating relations among the variables.
STATA for data analysis, time series analysisThe previous article showed lag selection and stationarity for Vector Auto Regression (VAR) with three variables; Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFC) and Private Final Consumption (PFC). This article shows the co-integration test for VAR with three variables.
STATA for data analysis, time series analysisThe previous article showed that the three-time series values Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFC) and Private Final Consumption (PFC) are non-stationary. Therefore they may have long-term causality. The general assumption, in this case, is that consumption PFC affects GDP, therefore these variables might be cointegrated.
STATA for data analysis, time series analysis, trend analysisIn multivariate time series, the prominent method of regression analysis is Vector Auto-Regression (VAR). It is important to understand VAR for more clarity.
exploratory model analysis, STATA for data analysis, time series analysisTo test cointegration, Johansen cointegration test is widely used which determines the number of independent linear combinations (k) for (m) time series variables set that yields a stationary process. The test gives the rank of cointegration.
STATA for data analysis, time series analysisAfter performing Autoregressive Integrated Moving Average (ARIMA) modelling in the previous article: ARIMA modeling for time series analysis in STATA, the time series GDP can be modelled through ARIMA (9, 2, 1) .
STATA for data analysis, time series analysis, trend analysisIn the previous article, all possibilities for performing Autoregressive Integrated Moving Average (ARIMA) modeling for the time series GDP were identified as under. S. No ARIMA 1 (1,1,1) 2 (1,1,2) 3 (1,1,3) 4 (1,1,4) 5 (1,1,5) 6 (1,1,6) 7 (1,2,1) 8 (4,2,1) 9 (9,2,1) Table 1: ARIMA models as per ACF and PACF graphs.
STATA for data analysis, time series analysis, trend analysis