Category: Learning modules
The problem of multicollinearity arises when one explanatory variable in a multiple regression model highly correlates with one or more than one of other explanatory variables. It is a problem because it underestimates the statistical significance of an explanatory variable (Allen, 1997).
STATA for data analysisThis article talks about the application of Singular Value Decomposition (SVD) technique MDPREF using Hamlet II. It is performed on the same matrix of profiles or context units saved while performing joint frequency analysis.
analysis and scaling, hamletThis 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 analysisCorrespondence analysis is a diagnostic tool which helps to present the content of a single text or the context unit profiles containing the joint frequency.
analysis and scaling, hamletHierarchical clustering uses methods to segregate the texts according to the similar vocabularies and then similar words or context are clustered together.
hamlet, text data 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 analysis