## How to perform Heteroscedasticity test in STATA for time series data?

By Rashmi Sajwan and Priya Chetty on October 16, 2018

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 variable.

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## ARCH model for time series analysis in STATA

By Saptarshi Basu Roy Choudhury and Priya Chetty on October 4, 2018

The previous article showed how to initiate the AutoRegressive Conditional Heteroskedasticity (ARCH) model on a financial stock return time series for period 1990 to 2016. It showed results for stationarity, volatility, normality and autocorrelation on a differenced log of stock returns.

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

By Divya Dhuria, Priya Chetty and Riya Jain on October 4, 2018

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?

By Divya Dhuria and Priya Chetty on October 4, 2018

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

By Divya Dhuria and Priya Chetty on September 27, 2018

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?

By Divya Dhuria and Priya Chetty on September 27, 2018

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

By Divya Dhuria and Priya Chetty on September 27, 2018

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.

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## Lag selection and cointegration test in VAR with two variables

By Divya Dhuria and Priya Chetty on September 27, 2018

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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