Tag: time series for econometrics
Applying Granger causality test in addition to 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 another variable. This article shows how to apply Granger causality test in STATA.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometricsVolatility only represents a high variability in a series over time.This article explains the issue of volatility in data using Autoregressive Conditional Heteroscedasticity (ARCH) model. It will identify the ARCH effect in a given time series in STATA.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometricsThis 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.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometricsUnrestricted 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.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometricsThe 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.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometricsAfter 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) .
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometrics, trend analysisThis article explains how to test ARIMA models and identifies the appropriate one for the process of forecasting time series GDP.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometrics, trend analysisAutoregressive Integrated Moving Average (ARIMA) is popularly known as 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 be explained by its own past or lagged values.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometrics