Law of Total Expectation - Iterated Expectations With Nested Conditioning Sets

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:

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