## How to identify ARCH effect for time series analysis in STATA?

Volatility 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.

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## How to test and diagnose VECM in STATA?

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.

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## VECM in STATA for two cointegrating equations

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.

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## 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.

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## Lag selection and stationarity in VAR with three variables in STATA

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.

## 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.

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## How to perform Johansen cointegration test?

To test cointegration, Johansen cointegration test is widely used which determines the number of independent linear combinations (k) for (m) time series variables set that yields a stationary process. The test gives the rank of cointegration.

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## Importance of Granger causality test

Granger causality is a method to examine the causality between two variables in a time series. “Causality” is related to cause and effect notion, although it is not exactly the same. It is a statistical concept which is based on the prediction.