The 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 minimize...
The goal of scenario reduction is to approximate a given discrete distributionwith another discrete ...
The Expectation-Maximization (EM) Algorithm is well-known in the literature of machine learning and ...
This paper proposes a testing strategy for the null hypothesis that a multivariate linear rational e...
Parametrized Expectation Algorithm (PEA) is a powerful tool for solving nonlinear stochastic dynamic...
Comparing numerical solutions of models with heterogeneous agents (Model A): a simulation- based par...
We start with a set of equilibrium conditions on the following form. f(xt; dt; et) = 0 xt+1 = g(xt;...
This is the final version of the article. It first appeared from Neural Information Processing Syste...
The parameterized expectations algorithm (PEA) involves a long simulation and a nonlinear least squa...
A new algorithm called the parameterized expectations approach(PEA) for solving dynamic stochastic m...
The main focus of this article is to provide a mathematical study of the algorithm proposed in [6] w...
This paper presents a framework for the theoretical analysis of Estimation of Distribution Algorithm...
Algorithms typically come with tunable parameters that have a considerable impact on the computation...
This paper investigates the finite sample properties of confidence intervals for structural vector e...
In this paper we develop a theoretical analysis of the performance of sampling-based fitted value it...
We compare and evaluate the performance of four widely used numerical solution methods to dynamic ra...
The goal of scenario reduction is to approximate a given discrete distributionwith another discrete ...
The Expectation-Maximization (EM) Algorithm is well-known in the literature of machine learning and ...
This paper proposes a testing strategy for the null hypothesis that a multivariate linear rational e...
Parametrized Expectation Algorithm (PEA) is a powerful tool for solving nonlinear stochastic dynamic...
Comparing numerical solutions of models with heterogeneous agents (Model A): a simulation- based par...
We start with a set of equilibrium conditions on the following form. f(xt; dt; et) = 0 xt+1 = g(xt;...
This is the final version of the article. It first appeared from Neural Information Processing Syste...
The parameterized expectations algorithm (PEA) involves a long simulation and a nonlinear least squa...
A new algorithm called the parameterized expectations approach(PEA) for solving dynamic stochastic m...
The main focus of this article is to provide a mathematical study of the algorithm proposed in [6] w...
This paper presents a framework for the theoretical analysis of Estimation of Distribution Algorithm...
Algorithms typically come with tunable parameters that have a considerable impact on the computation...
This paper investigates the finite sample properties of confidence intervals for structural vector e...
In this paper we develop a theoretical analysis of the performance of sampling-based fitted value it...
We compare and evaluate the performance of four widely used numerical solution methods to dynamic ra...
The goal of scenario reduction is to approximate a given discrete distributionwith another discrete ...
The Expectation-Maximization (EM) Algorithm is well-known in the literature of machine learning and ...
This paper proposes a testing strategy for the null hypothesis that a multivariate linear rational e...