All articles by Divya Dhuria

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 a high variability in a series over time. Read more »

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. Read more »

VECM in STATA for two cointegrating equations

In the previous article, 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 variables. Read more »

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. Read more »

Lag selection and stationarity in VAR with three variables in STATA

The previous article explained how to perform the lag selection, Johansen co-integration test and Vector Auto Regression (VAR) with two variables, Gross Domestic Product (GDP) and Private Final Consumption (PFC). This article incorporates Gross Fixed Capital Formation (GFC) and again performs the lag selection test and check for stationarity for both, GFC and PFC. Thus this article incorporates the VAR with three variables in STATA. The process also includes Johansen cointegration test for the model including all three-time series. Read more »

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 Regression (VAR) with two variables will help check the same. Read more »

How to perform unit root test?

Unit root indicates a stochastic trend in the time series. Sometimes it is known as “random walk with drift”. A time series dataset will show a systematic unpredictable pattern if it has the unit root. If a time series dataset has the unit root, the regression result will be unreasonable and provide a spurious result (in which there is large r-squared value even if the data is uncorrelated) and errant behavior (in which t-rations will not follow at- distributions). Therefore it is important to perform unit root test. Read more »

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 assumes that there is the occurrence of stochastic non-stationary series, underlying two or more process (p). Read more »

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