How to identify ARCH effect for time series analysis in STATA
The previous articles showed how to apply Vector Auto Regression (VAR) and Vector Error Correction Model (VECM) based on the assumption that the variables either have a long run or short run causality among them. Some financial time series such as stock returns show wide swings for an extended period of time. Such behaviour is known as volatility. Volatility only represents […]
How to test and diagnose VECM in STATA
The previous article estimated Vector Error Correction (VECM) for time series Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFC), Private Final Consumption (PFC ). 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. LM test for residual autocorrelation and diagnosing VECM To start with the test […]
VECM in STATA for two cointegrating equations
In the previous article, the Johansen cointegration test revealed the cointegration between time series Gross Domestic Product (GDP), Private Final Consumption (PFC) and Gross Fixed Capital Formation (GFC), containing up to two cointegrating equations. Therefore, unrestricted 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 […]
How to perform Granger causality test in STATA
A previous article (Lag selection and cointegration test in VAR with two variables) in this module demonstrated the application of the cointegration test in time series analysis. Applying Granger causality test in addition to the cointegration test like Vector Autoregression (VAR) helps detect the direction of causality. It also helps to identify which variable acts as a determining factor for […]
How to perform Johansen cointegration test
If a series is nonstationary in time series without a constant mean and constant variance, the regression results will be spurious. But regression results can be reliable when a linear combination of non-stationary series (dependent and independent) removes the stochastic trend and produces stationary residuals. Therefore, it is implied that variables are co-integrated. Co-integrated also […]
How to perform Johansen cointegration test in VAR with three variables
The 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. To perform the Johansen cointegration test, follow the below steps. Click on ‘Statistics’ on Result window Select ‘Multivariate Time-series’ Select ‘Co-integrating rank of a […]
Lag selection and cointegration test in VAR with two variables
The 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. Resultantly, they may lead to an estimation of a stationary variable. Johansen cointegration test in Vector Auto […]
How to perform regression analysis using VAR in STATA
The previous article on time series analysis showed how to perform Autoregressive Integrated Moving Average (ARIMA) on the Gross Domestic Product (GDP) of India for the period 1996 – 2016 using STATA. The underlining feature of ARIMA is that it studies the behavior of univariate time series like GDP over a specified time period. Based on that, it recommends an ARIMA equation. This equation then helps to forecast […]
How to perform point forecasting in STATA
This article explains how to perform point forecasting in STATA, where one can generate forecast values even without performing ARIMA. Therefore, it is useful in any time series data. Forecasting is an important part of time series analysis. A point forecast is a singular number which represents the estimate of the true but unknown value of […]
How to test time series autocorrelation in STATA
This article shows a testing serial correlation of errors or time series autocorrelation in STATA. An autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other. Why test for autocorrelation? It is one of the main assumptions of the OLS estimator according to the Gauss-Markov theorem that in […]
