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What is the strict exogeneity assumption?
Exogeneity is a standard assumption made in regression analysis, and when used in reference to a regression equation tells us that the independent variables X are not dependent on the dependent variable (Y).
What is contemporaneous exogeneity?
Contemporaneous exogeneity, he said, means that “the mean of the error term is uncorrelated to the explanatory variables of the same period.
What is strong Exogeneity?
Strong or strict exogeneity is E[u|x]=0 (“zero conditionnal mean”).
What is weak Exogeneity?
Weak exogeneity (WE) is the case where statistically efficient. estimation and inference can be achieved by only considering the. conditional model and not taking the rest of the system into. account.
The problem your refers on is about strict exogeneity and lagged dependent variables. The two things together are contradictory. However if other lagged variables as x t − j are included, and dependent are not, the contradiction disappear.
Which is a weaker version of strict exogeneity?
This is a slightly weaker version of strict exogeneity since it does not require a lack of correlation over longer time spans. The first point to make here, which is not my main point, is that researchers should, generally speaking, pay more attention to justifying the assumption of strict exogeneity when applying panel data models.
How is strict exogeneity used in time series estimation?
My understanding of strict exogeneity is that a variable must be uncorrelated with error terms in all periods. But isn’t exogeneity always a necessary assumption for estimation? If x t and u t are uncorrelated, and x t − 1 and u t − 1 are uncorrelated, how would it violate strict exogeneity if we have a specification with x t and x t − 1?
When are explanatory variables not stochastic they are exogenous?
When the explanatory variables are not stochastic, then they are strong exogenous for all the parameters.