How to perform point forecasting in STATA?
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 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 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 […]
exploratory model analysis, panel data analysis, 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.
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.
exploratory model analysis, panel data analysis, STATA for data analysisThis article of the module explains how to perform panel data analysis using STATA. In the case of panel data, the observations are present in time and space dimensions. For instance, a survey of the same cross-sectional unit such as firm, country or state over time.
panel data analysis, 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.
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.
STATA for data analysis, time series analysis