Variance Decomposition using Cholesky (d.f. adjusted) Factors Variance Decomposition of INV: SE. cs Period INV INC 18.30781 24.81084 29.10212 32.18731 34.49133 36.25023 27 61342 100.0000 99.95752 99.89224 99.82075 99.74921 0.000000 0.000934 0.000935 0.000932 0.002944 0.008958 0.000000 0.041543 0.106825 0.178321 0.247849 2 3 4 6 99.67911 0.311932 07 496 0.369431
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
9. Explain these results when the results of the predictive error variance obtained in the three-variate VAR (1) model are as shown in the table below. At this time, the variables INV are investment, INC is input, and CS is concumption.
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