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.
stock categorization, stock market analysis, stock market page intro, time series analysis, trend analysisThis 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 analysisTime 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.
assumption tests in STATA, empirical analysis with econometrics, STATA for data analysis, time series analysisThis 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.
assumption tests in STATA, correlation, empirical analysis with econometrics, STATA for data analysis, time series analysis, trend analysisApplying 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.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometricsHeteroskedastic 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.
assumption tests in STATA, empirical analysis with econometrics, STATA for data analysis, time series analysisThe 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 analysisVolatility 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.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometrics