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If we have the following regression based on the Solow model:

log(yi) = β0 + β1 * log(si) + β2 * log(ni + gi + δi ) + ei

And we know that based on:

y* = 〖(s/(n + δ + g))〗^(α/(1-α))

log(y*) = α/(1-α) * log(s) – α/(1-α) * log(n + δ + g) )

So we can assume that beta 1 and beta 2 are equal to: α/(1-α)

Of course beta 1 should be positive and beta 2 should be negative, but what if they have different values, for example beta 1 equals 1.5 and beta 2 equals -2.5?

What does this mean economically? I am a little bit confused about how beta 1 and beta 2 can be different.

Thank you very much for every answer!

Best regards

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  • $\begingroup$ You say of course beta1 >0 and beta2<0, but you also say that you are confused about how beta 1 and beta 2 can be different. $\endgroup$
    – Bayesian
    Dec 18 '20 at 12:02

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