 jotta weigh-austing on iloittanut remodellista tai tai ei-austuu, jotta katsotaan, että siinä johtamme käyttöön osa, sillä on Importantia, että yksiköalaisesta tai yksiköalaisesta oesian eri-reqeusimaatioon paskallinen talon. Klein sanoi eri-reqeusimaatioon sammattomien. Se eri-reqeusimasta ja edelleen eri-reqeusimeen selvää, jotta nämä pysyvät kapeen ja nämä kapeen ja esäkentästää ovat eri-reqeusimeen. The key feature of recursive models is that all effects are unidirectional, so there are no bidirectional relationships, and all disturbances or error terms are uncorrelated. So we can see here that they're all arrows going the same, one direction only, and no arrows backwards, and these two error terms are uncorrelated. In non-recursive models here we have feedback loops and we have correlated disturbances. So the feedback loop is here Y1 influences Y2 and Y2 influences Y1 back, so this is some kind of equilibrium model here. So these are two extremes we have here both a feedback loop and correlated disturbances. There are also cases where you have correlated disturbances but you don't have feedback loops and these two cases are here. These are different in an important way. So this is something called bow-free pattern and it's considered recursive and this is considered non-recursive because there is a bow pattern. So what does it mean that it is a bow pattern? The bow pattern is here so we have this arc here and then we have this bow string here. So if you put the arc and the bow string together then it looks like a bow that you would use in archery. So this is considered non-recursive and this is recursive. The terminology or how these two type of models are classified varies a bit depending on different sources. So I have not read a single book where this is considered a recursive model but some books label this as non-recursive because of the correlated disturbance here. The important thing about estimation and identification is that the models on the left hand side are always identified and they can also be consistently estimated with normal recursion analysis. So because there is no endogeneity we can see here this is an endogeneity problem because the error term of Y2 correlates with Y1 this cannot be consistently estimated with OLS. And this feedback loop here cannot be consistently estimated with OLS either. But here we don't have any endogeneity problems so all error terms are uncorrelated with all predictors that can be estimated with OLS. Estimation and identification on the models on the left hand side is very straightforward. Estimation and identification on the model on the right hand side is a lot more challenging.