Your Textbook Gave an Example of Attempting to Estimate the Demand

Your textbook gave an example of attempting to estimate the demand for a good in a market, but being unable to do so because the demand function was not identified. Is this the case for every market? Consider, for example, the demand for sports events. One of your peers estimated the following demand function after collecting data over two years for every one of the 162 home games of the 2000 and 2001 season for the Los Angeles Dodgers. $\widehat{\text { Attend }}=15,005+201 \times \text { Temperat }+465 \times \text { DodgNetWin }+82 \times \text { OppNetWin }$
$(8,770) \quad(121)\quad\quad(169) \quad\quad(26)$

$+9647 \times \text { DFSaSu }+1328 \times \text { Drain }+1609 \times \text { D } 150 m+271 \times \text { DDiv }-978 \times \text { D2001 }$
$\begin{array}{lllll}(1505) & (3355) & (1819) & (1,184) & (1,143)\end{array}$

$R^{2}=0.416, S E R=6983$
Where Attend is announced stadium attendance, Temperat it the average temperature on game day, DodgNetWin are the net wins of the Dodgers before the game (wins-losses), OppNetWin is the opposing team's net wins at the end of the previous season, and DFSaSu, Drain, D150m, Ddiv, and D2001 are binary variables, taking a value of 1 if the game was played on a weekend, it rained during that day, the opposing team was within a 150 mile radius, plays in the same division as the Dodgers, and during 2001, respectively. Numbers in parenthesis are heteroskedasticity- robust standard errors.
Even if there is no identification problem, is it likely that all regressors are uncorrelated with the error term? If not, what are the consequences?