By A.H. Studenmund
Combining single-equation linear regression research with intuitive real-world examples and routines is essential to the good fortune of utilizing Econometrics. transparent writing and a pragmatic method of econometrics that eschews using advanced matrix algebra and calculus facts this crucial text's accessibility. because the subtitle, a pragmatic consultant, implies, this booklet is aimed not just at starting econometrics scholars, but additionally at regression clients searching for a refresher and at skilled practitioners who need a handy reference.
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Extra resources for Using Econometrics: A Practical Guide (5th Edition)
As will be seen, on one hand they share many properties, but on the other hand there are some fundamental differences. 1 The Similarities Between GMD and the Variance The first similarity between the GMD and the variance is the fact that both can be written as covariances. The variance of X is cov(X, X), while the GMD of X is cov(X, F(X)). This similarity serves as the basis for the ability to “translate” the variance world into the Gini world. The second similarity is the fact that the decomposition of the variance of a linear combination of random variables is a special case of the decomposition of the GMD of the same combination.
By doing so they may affect the correlation between the variables, which in turn may affect the decision on which variable has a higher explanatory power. The conclusion is that one should be careful when applying seemingly innocent monotonic transformations to variables when using Pearson’s correlation coefficient. 2 Spearman Correlation Coefficient Spearman’s rank correlation coefficient or Spearman’s r was first introduced by Charles Spearman (1904). It is often denoted by rs and it is a nonparametric measure of correlation.
Random variables. However, the difference between them is in the distance function used. The effects of the distance functions on the properties of the indices will be illustrated when we deal with the properties of OLS and the Gini regression coefficients in Chap. 7. The source of this difference will be discussed in the next section. 2 The Differences Between the GMD and the Variance: City Block vs. Euclidean Let Dxk denote the difference between adjacent observations. That is, Dxk ¼ Xkþ1 ÀXk, where the observations are arranged in an increasing order.