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Riya Jain and Priya Chetty on August 5, 2023 No Comments
The stock market is an uncertain market consisting of opportunities to gain and lose. The risk presence in the market creates the need for a stock market forecasting model for the movements of stocks and an understanding of the possible position of stocks in position. Information on future stock movements can enable the investor to […]
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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.
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Rashmi Sajwan and Priya Chetty on October 31, 2018 6 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.
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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.
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Rashmi Sajwan and Priya Chetty on October 16, 2018 17 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.
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Rashmi Sajwan and Priya Chetty on October 16, 2018 11 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.
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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.