Iterated Expectations With Nested Conditioning Sets
The following formulation of the law of iterated expectations plays an important role in many economic and finance models:
where the value of I1 is determined by that of I2. To build intuition, imagine an investor who forecasts a random stock price X based on the limited information set I1. The law of iterated expectations says that the investor can never gain a more precise forecast of X by conditioning on more specific information (I2), if the more specific forecast must itself be forecast with the original information (I1).
This formulation is often applied in a time series context, where Et denotes expectations conditional on only the information observed up to and including time period t. In typical models the information set t + 1 contains all information available through time t, plus additional information revealed at time t + 1. One can then write:
Read more about this topic: Law Of Total Expectation
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