Tag: STATA for data analysis
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 analysisThe previous article (Pooled panel data regression in STATA) showed how to conduct pooled regression analysis with dummies of 30 American companies. The results revealed that the joint hypothesis of dummies reject the null hypothesis that these companies do not have any alternative or joint effects. Therefore pooled regression is not a favourable technique for […]
empirical analysis with econometrics, exploratory model analysis, panel data analysis, panel data regression in STATA, STATA for data 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 analysisThe underlying assumption in pooled regression is that space and time dimensions do not create any distinction within the observations and there are no set of fixed effects in the data.
empirical analysis with econometrics, exploratory model analysis, panel data regression in STATA, STATA for data analysisThe 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 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 analysis