Binary regression: Difference between revisions

Revision as of 01:30, 16 January 2020

Revision as of 01:36, 16 January 2020

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: <math>y=1 [y^*>0]</math>

where <math>y^*=x\beta +\varepsilon </math> and <math>\varepsilon \mid x\sim G</math>, where {{math|''&beta;''}} is a vector of [[statistical parameter|parameters]] and ''G'' is a [[probability distribution]].

This model can be applied in many economic contexts. For instance, the outcome can be the decision of a manager whether invest to a program, <math>y^*</math> is the expected net [[discounted cash flow]] and ''x'' is a vector of variables which can affect the cash flow of this program. Then the manager will invest only when she expects the net discounted cash flow to be positive.<ref>For a detailed example, refer to: Tetsuo Yai, Seiji Iwakura, Shigeru Morichi, Multinomial probit with structured covariance for route choice behavior, Transportation Research Part B: Methodological, Volume 31, Issue 3, June 1997, Pages 195–207, ISSN 0191-2615</ref>