01479nas a2200205   4500000000100000000000100001008004100002260001200043653002100055653003100076653001900107653004100126100003100167245011000198856007900308300001000387490000600397520085600403022001401259       2022                            d  c03/202210aNonlinear Models10aNumerical Solution Methods10aOptimal Growth10aParameterized Expectations Algorithm1 aA. Jesús Sánchez-Fuentes00aFinite Sample Properties of Parameterized Expectations Algorithm Solutions; Is the Length So Determinant?  uhttps://www.ijimai.org/journal/sites/default/files/2022-02/ijimai7_3_3.pdf  a26-340 v73 aThe solution of the Parameterized Expectations Algorithm (PEA) is well defined based on asymptotic properties. In practice, it depends on the specific replication of the exogenous shock(s) used for the resolution process. Typically, this problem is reduced when a sufficiently long replication is considered. In this paper, we suggest an alternative approach which consists of using several, shorter replications. A centrality measure (the median) is used then to discriminate among the different solutions using two different criteria, which differ in the information used. On the one hand, the distance to the vector composed by median values of PEA coefficients is minimized. On the other hand, distances to the median impulse response is minimized. Finally, we explore the impact of considering alternative approaches in an empirical illustration.  a1989-1660