BEPP 305 Lecture Notes - Lecture 3: Probability Distribution, Moving-Average Model, Cumulative Prospect Theory

• School: UPenn
• Department: Business Econ & Public Policy
• Course: BEPP 305
• Professor: Tobacman

Document Summary

Need a lot of data, (cid:271)ut (cid:449)e do(cid:374)"t al(cid:449)a(cid:455)s ha(cid:448)e e(cid:374)ough data. Must estimate the parameters of the distribution, but not the entire shape: 2), then ax + (cid:271)~n(cid:894)a x + b, a2 x. 2), then x + y~n(cid:894) x + y, x: for any numbers a and b. 2 + 2*covx,y + y (cid:862) ta(cid:374)da(cid:396)d (cid:374)o(cid:396)(cid:373)al(cid:863) (bell shaped curve) Probability distribution function (pdf) - shows the probability that the outcome of a random variable will fall within a certain range. Cumulative distribution function (cdf) - shows the probability that the random variable takes on a value less than or equal to a given value; another way to present the same info. For each outcome x, the cdf shows the probability that a random variable x takes on a value less than or equal to. Let {xi} (cid:271)e a se(cid:395)ue(cid:374)(cid:272)e of (cid:862)u(cid:374)(cid:272)o(cid:396)(cid:396)elated(cid:863) (cid:396)a(cid:374)do(cid:373) (cid:448)a(cid:396)ia(cid:271)les (cid:449)ith the sa(cid:373)e distribution.