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Introduction to the Autoregressive Integrated Moving Average (ARIMA) model

By Riya Jain and Priya Chetty on September 29, 2020 No Comments

Autoregressive Integrated Moving Average (ARIMA) is the statistical tool with a standard structure which though is simpler but provides skillful information about the stock market.

 time series analysis, trend analysis

How to perform point forecasting in STATA?

By Saptarshi Basu Roy Choudhury and Priya Chetty on November 22, 2018 3 Comments

This article explains how to perform point forecasting in STATA, where one can generate forecast values even without performing ARIMA.

 STATA for data analysis, time series analysis

How to test normality in STATA?

By Rashmi Sajwan and Priya Chetty on October 31, 2018 5 Comments

Time series data requires some diagnostic tests in order to check the properties of the independent variables. This is called ‘normality’. This article explains how to perform normality test in STATA.

 STATA for data analysis, time series analysis

How to test time series autocorrelation in STATA?

By Rashmi Sajwan and Priya Chetty on October 22, 2018 7 Comments

This article shows a testing serial correlation of errors or time series autocorrelation in STATA. Autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other.

 correlation, STATA for data analysis, time series analysis, trend analysis

How to perform Granger causality test in STATA?

By Rashmi Sajwan and Priya Chetty on October 16, 2018 12 Comments

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.

 STATA for data analysis, time series analysis

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

By Rashmi Sajwan and Priya Chetty on October 16, 2018 9 Comments

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.

 STATA for data analysis, time series analysis

ARCH model for time series analysis in STATA

By Saptarshi Basu Roy Choudhury and Priya Chetty on October 4, 2018 No Comments

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.

 STATA for data analysis, time series analysis

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

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

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.

 STATA for data analysis, time series analysis
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