How to perform Heteroscedasticity test in STATA for time series data
The previous articles showed how to perform normality tests in time series data. This article focuses on another important diagnostic test, i.e. heteroscedasticity test in STATA. Heteroskedastic means “differing variance” which comes from the Greek word “hetero” (‘different’) and “skedasis” (‘dispersion’). It refers to the variance of the error terms in a regression model in an independent […]
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 […]
Non linear regression analysis in STATA and its interpretation
In the previous article on Linear Regression using STATA, a simple linear regression model was used to test the hypothesis. However the linear regression will not be effective if the relation between the dependent and independent variable is non linear. The non linear regression is used more in the real life as compared to the linear regression. […]
Procedure and interpretation of linear regression analysis using STATA
Linear regression analysis is conducted to predict the dependent variable based on one or more independent variables. The basic regression equation is: Where; Y is dependent variable X is independent variable e is error term In the above regression equation, β1 measures the effect of X1 on Y. Similarly β2 measures the effect of X2 on Y. The constant term (β0) measures the value […]
How to do the correlation analysis in STATA
Correlation analysis is conducted to examine the relationship between dependent and independent variables. There are two types of correlation analysis in STATA. Pairwise correlation treats each pair of variables separately and only includes observations that have valid values for each pair in the data set. The second type of correlation is the normal correlation which […]
