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 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 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 normality in STATA
The preceding articles showed how to conduct time series analysis in STATA on a range of univariate and multivariate models including ARIMA, VAR (Lag selection, and stationarity in VAR with three variables in STATA) and VECM (VECM in STATA for two cointegrating equations). Time series data requires some diagnostic tests in order to check the properties of the […]
How to predict and forecast using ARIMA in STATA
After performing Autoregressive Integrated Moving Average (ARIMA) modelling in the previous article: ARIMA modelling for time series analysis in STATA, the time series GDP can be modelled through ARIMA (9, 2, 1) equation as below: Figure 1: ARIMA Results in STATA ARIMA results as in the above figure can be analyzed through several components: Log-likelihood: the value of log-likelihood is 535.8 which is minimum […]
ARIMA modeling for time series analysis in STATA
In 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. Testing ARIMA models in STATA for […]
How to build the univariate ARIMA model for time series in STATA
Autoregressive Integrated Moving Average (ARIMA) is popularly known as the Box-Jenkins method. The emphasis of this method is on analyzing the probabilistic or stochastic properties of a single time series. Unlike regression models where Y is explained by X1 X2….XN regressor (like the introductory case where GDP is explained by GFC and PFC), ARIMA allows Y (GDP) to […]
The problem of non-stationarity in time series analysis in STATA
Determining the presence of stationarity Creating a visual plot of data is the first step in time series analysis. Graphical representation of data helps understand it better. To plot a graph, follow these steps: Click on ‘Statistics’ in the ribbon of the ‘Output’ Window. Select ‘Time series’. Select ‘Graph’. Click on ‘Line plots’. The figure […]
How to set the ‘Time variable’ for time series analysis in STATA
Time series analysis works on all structures of data. It comprises methods to extract meaningful statistics and characteristics of data. Time series test is applicable on datasets arranged periodically (yearly, quarterly, weekly or daily). This article explains how to set the ‘Time variable’ to perform time series analysis in STATA. The following points are covered […]
