What are the advantages, we can find them in the next slide.

The advantages are basically that transparency, and

the fact that it's also based on expert.

These are respect inside the pairwise evaluations,

and therefore it is not relying on technical manipulations.

Okay, in the next slide,

we are going to talk about price-based weights.

In a previous section of this course,

we called your attention by the fact that we related the choice of the weights with

the marginal regression of substitution between partial indicators.

As you probably remind on this time, we highlight that from economic theory and

from utility maximization problem.

We remember that the equilibrium condition was that the marginal relation of

substitution between two goods should be equal to each relative prices.

So somehow, since weights are a function of this marginal relation of substitution.

We can find analytic relationship between relative prices and

relative weights for two different partial indicators.

That's clearly the intuition that is behind this type of techniques.

And it basically consisting of assigning prices to, or

that weights should reflect prices.

Or in some cases, shadow prices if real prices are not available.

In the next slide, we talk about stated preference weights,

this is a very standard technique.

And we wouldn't like to spend too much time on it, so

please have a look to the corresponding slides.

And let me just end up by the hedonic weights.

Hedonic weights are basically a way to choose weights that is well-spread around,

and technically very commonly used by experts.

Hedonic weights basically means to approximate the composite index,

the target we want to take, through endogenous variable, through a variable Y.

And then we try to explain this variable Y through

a linear combination of the different partial indicators.

The regression coefficient of this regression is going to be or

are going to be the weights, conveniently normalized.

So basically, it's a regression-based approach.

What are the drawbacks of this hedonic price selection technique?

The main drawbacks are basically those related to the regression techniques.

That is, first, if the partial indicators are highly correlated,

the weights are going to have a lot of variation.

And secondly, the problem can be that the endogenous variable,

the Y variable we want to explain,

and that needs to be close to the composite indicator, is not available.

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