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Take a square root of it, and this is the Root MSE.Divide it by the error's degrees of freedom, this will give you the "Mean error sum of squares," MS in Stata output.Add them up, this will give you the "Error sum of squares," SS in Stata output.Calculate the difference between the observed and predicted dependent variables.
#STATA MP VS E SERIES#
The time series result will identify the residuals from the regression analysis.
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Thereafter proceed to the regression analysis. The first step is to set the time series data. Private Final Consumption Expenditure ( PFC) (independent variable).Gross Fixed Capital Formation( GFC) (independent variable).Gross Domestic Product ( GDP) (dependent variable).This article uses quarterly data of the following variables of the Indian economy for the time period 1997- 2018: This article shows two tests Skewness Kurtosis and Jarque Bera tests because they are simple and popular. There are several normality tests such as the Skewness Kurtosis test, the Jarque Bera test, the Shapiro Wilk test, the Kolmogorov-Smirnov test, and the Chen-Shapiro test. The normality test helps to determine how likely it is for a random variable underlying the data set to be normally distributed.
#STATA MP VS E HOW TO#
This article explains how to perform a normality test in STATA. Time series data requires some diagnostic tests in order to check the properties of the independent variables. The preceding articles showed how to conduct time series analysis in STATA on a range of univariate and multivariate models including ARIMA, VAR (Lag selection, and stationarity in VAR with three variables in STATA) and VECM (VECM in STATA for two cointegrating equations). Rashmi Sajwan and Priya Chetty on October 31, 2018
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How to predict and forecast using ARIMA in STATA?.ARIMA modeling for time series analysis in STATA.How to build the univariate ARIMA model for time series in STATA?.Handling unit root problem from Dickey-Fuller test in time series analysis.
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