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The Least Squares Criterion

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II.I.1 The least squares criterion

There are many techniques in econometrics and statistics that use the least squares criterion. In regression techniques this criterion is of immense importance.

Why should a criterion be used at all? The answer to this question is quite obvious: one has to have an objective measure for discrepancies between the estimated values (generated by the statistical model) and the (true) observed values. In fact we wish to create mathematical models of our surrounding world in order to be able to describe it, to draw conclusions from it, to forecast future behavior of some (economic) phenomena, and to explain why certain things happened in the past.

For obvious reasons these mathematical models are not deterministic but instead, probabilistic or stochastic. This is the reason why we have a need for a good criterion to decide whether our model does describe the real world as good as possible.

Since we cannot hope for a model to describe a real phenomenon perfectly, the only thing we can do is to design a method for getting as close to the real behavior as possible. This can be achieved by minimizing the error of the mathematical model.

The most obvious way to express the error made by a probabilistic model is to calculate the sum of the deviations between the forecasted values and the real values:

The Least Squares Criterion

(II.I.1-1)

A much better criterion is obtained when using the absolute values of the deviations:

(II.I.1-2)

since this will ensure that large positive errors are not compensated by large negative errors.

Another criterion can be defined by computing the sum of squared deviations:

(II.I.1-3)

Using the square of the deviations results in generating only positive values (like in the previous criterion) but above that, it tends to give more weight to large discrepancies in stead of small ones.

Remark that eq. (II.I.1-3) is not always an improvement with respect to eq. (II.I.1-2). This is because in some cases, where a very long structural shift (in time) exists, the second criterion (II.I.1-2) will describe specifically the long shift better than the third criterion whereas the latter performs better in regard to overall predictive power. Moreover, criterion (II.I.1-2) is more robust in the context of outliers.

From now on we will always use the criterion of minimizing the Sum of Squared Residuals (SSR) from equation (II.I.1-3), because this criterion is most commonly used in econometrics. Above that, the SSR criterion can be proved the be equivalent to another important criterion (c.q. maximum likelihood) in certain circumstances.

The SSR criterion should never be confused with the Ordinary Least Squares technique (OLS)! In fact, OLS does use the SSR criterion but so do a lot of other techniques like for instance Multiple Stage Least Squares, Weighted Least Squares, Generalized Least Squares, the Maximum Likelihood Estimation (MLE) under certain conditions, etc...

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