F-test

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F-test

Etymology: Sir Ronald Aylmer Fisher, British statistician, 1890-1962
a statistical test comparing the means of more than two groups simultaneously by comparing two different measures of variance of the observations. One statistic measures the variations between the means of the groups (the between-groups variation), the other the variations within the groups (the within-group variation). If the two measures of variance yield similar results and their ratio, the F-ratio, approximates 1.0, the null hypothesis that all observations came from the same population cannot be rejected, whereas under the alternative hypothesis, the F-ratio is expected to be larger than 1.0. The test is the first step in the analysis of variance.
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478285 *** The truncation lag is based on Newey and West (1987) bandwidth ** and *** denote statistical significance at the 5 and 1% levels, respectively Table 4 Bounds F-test for cointegration Dependent variable Function F-test statistic In y/N In y/N(In UEMP, In GOV) 5.
01) as refer to the p-value of F-Test statistic is at 307.
However, turning to the joint hypothesis test of strong rational expectations (2), the F-test statistic is 10.
7) If the F-test statistic exceeds the upper critical value, the null hypothesis of no long-run relationship can be rejected regardless of whether the underlying orders of integration of the variables are I(0) or I(1).
To this end, an F- test is performed: the production function is estimated with and without the training-related variables, and the F-test statistic for returns to training is calculated using the residual sum of squares of these alternative specifications.
The F-test statistic for each model is as follows: Ml is 5.
In other words a large F-test statistic, and a correspondingly small p-value, indicates that there are statistically significant differences in yields offered in different states.
The following F-test statistic was used to test the homogeneity of the variances of the 1-month percentage changes for the PPI data series and for the corresponding measures based on alternative data sources:
The equation has an improved fit, as measured both by R-bar square and the F-test statistic.
866066 *** I(1) ** and *** denote significance at 5 % and 1 % respectively Table 3 Bounds F-test for co integration Dependent variable Function F-test statistic Uruguay InGrowth InGrowth (InENC, InExpt) 5.
Table 3 Bounds F-test for cointegration Dependent variable Function F-test statistic [DELTA]Iny/[N.
It is now well established in econometric literature that the F-test statistic is not valid if times series are integrated as argued by Toda and Yamamoto [1995], Enders [1995], Zapata and Rambaldi [1997], and Gujarati [1995].