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**EC220 - Introduction to econometrics (review chapter)**

Christopher Dougherty EC220 - Introduction to econometrics (review chapter) Slideshow: estimators of variance, covariance and correlation Original citation: Dougherty, C. (2012) EC220 - Introduction to econometrics (review chapter). [Teaching Resource] © 2012 The Author This version available at: Available in LSE Learning Resources Online: May 2012 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This license allows the user to remix, tweak, and build upon the work even for commercial purposes, as long as the user credits the author and licenses their new creations under the identical terms.

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

We have seen that the variance of a random variable X is given by the expression above. 1

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

Given a sample of n observations, the usual estimator of the variance is the sum of the squared deviations around the sample mean divided by n – 1, typically denoted s2X. 2

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

Since the variance is the expected value of the squared deviation of X about its mean, it makes intuitive sense to use the average of the sample squared deviations as an estimator. But why divide by n – 1 rather than by n? 3

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

The reason is that the sample mean is by definition in the middle of the sample, while the unknown population mean is not, except by coincidence. 4

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

As a consequence, the sum of the squared deviations from the sample mean tends to be slightly smaller than the sum of the squared deviations from the population mean. 5

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

Hence a simple average of the squared sample deviations is a downwards biased estimator of the variance. However, the bias can be shown to be a factor of (n – 1)/n. Thus one can allow for the bias by dividing the sum of the squared deviations by n – 1 instead of n. 6

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

A similar adjustment has to be made when estimating a covariance. For two random variables X and Y an unbiased estimator of the covariance sXY is given by the sum of the products of the deviations around the sample means divided by n – 1. 7

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

The population correlation coefficient rXY for two variables X and Y is defined to be their covariance divided by the square root of the product of their variances. 8

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

The sample correlation coefficient, rXY, is obtained from this by replacing the covariance and variances by their estimators. 9

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**ESTIMATORS OF VARIANCE, COVARIANCE, AND CORRELATION**

The 1/(n – 1) terms in the numerator and the denominator cancel and one is left with a straightforward expression. 10

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**Copyright Christopher Dougherty 2011.**

These slideshows may be downloaded by anyone, anywhere for personal use. Subject to respect for copyright and, where appropriate, attribution, they may be used as a resource for teaching an econometrics course. There is no need to refer to the author. The content of this slideshow comes from Section R.7 of C. Dougherty, Introduction to Econometrics, fourth edition 2011, Oxford University Press. Additional (free) resources for both students and instructors may be downloaded from the OUP Online Resource Centre Individuals studying econometrics on their own and who feel that they might benefit from participation in a formal course should consider the London School of Economics summer school course EC212 Introduction to Econometrics or the University of London International Programmes distance learning course 20 Elements of Econometrics

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