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In Davidson & McKinnon - Estimation and Inference I read that in a competitive market which is always in equilibrium we observe:

$Q^d_t = Q^s_t = Q_t$

where $Q^d_t$ is the quantitiy of demand, $Q^s_t$ the quantitiy supplied.

If one would estimate the equation (1) by OLS in a model given by

(1) $Q_t^d = \alpha P_t + Z^d_t\beta + u^d_t$

(2) $Q_t^s = \gamma P_t + Z^s_t\delta + u^s_t$

with $P_t$ the price in period t, $Z^d_t$ correspods to a bunch of exogenous variables which determine the quantity, we would get a problem caused by the endogenity of $P_t$.

This can be seen if we rewrite the equations (1) and (2) in terms of the observable variables $Q_t$ and $P_t$.

My question is: Is this true in general should accounted for if one does a regression with real market data?

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My answer would be yes to both questions. Yes, this is true in general, and yes, it should be taken into account if you want to estimate a regression with real market data. For example, empirical work on estimation of demand functions usually includes a third variable, income, that shifts the demand curve or uses two stage least squares.

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  • $\begingroup$ Thank you very much. This (fortunatly) confirms what I already assumed. $\endgroup$ – Druss2k Jan 30 '13 at 1:20

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