the sample is in fact representative, i.e. the investor types cover the large majority of the capital in the market, and
investors within each type have “similar” behavior—ideally they can all be captured by a representative agent.
(We could also circumvent the need for representative agents by estimating the demand function of each investor class directly, but then with n assets we need to estimate a function from R^n to R^n rather than a function from R^n to R, so the data and computation requirements are dramatically higher. Also, at that point there aren’t clear benefits to breaking out classes of investors in the first place.)
Investor types corresponding to timelines is indeed sensible; I use that a lot in my own models. For instance, I can use data on individual trades to estimate the portfolios held by market makers as a function of price.
That would be correct assuming that
the sample is in fact representative, i.e. the investor types cover the large majority of the capital in the market, and
investors within each type have “similar” behavior—ideally they can all be captured by a representative agent.
(We could also circumvent the need for representative agents by estimating the demand function of each investor class directly, but then with n assets we need to estimate a function from R^n to R^n rather than a function from R^n to R, so the data and computation requirements are dramatically higher. Also, at that point there aren’t clear benefits to breaking out classes of investors in the first place.)
Investor types corresponding to timelines is indeed sensible; I use that a lot in my own models. For instance, I can use data on individual trades to estimate the portfolios held by market makers as a function of price.