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Scaled and Adjusted Restricted Tests in Multi Sample Analysis of Moment Structures

Satorra, Albert - 2001

We extend to score, Wald and difference test statistics the scaled and adjusted corrections to goodness-of-fit test statistics developed in Satorra and Bentler (1988a,b). The theory is framed in the general context of multisample analysis of moment structures, under general conditions on the...

Persistent link: https://www.econbiz.de/10014179647

On the Computational Complexity of MCMC-Based Estimators in Large Samples

Belloni, Alexandre; Chernozhukov, Victor - 2007

This paper studies the computational complexity of Bayesian and quasi-Bayesian estimation in large samples carried out using a basic Metropolis random walk. The framework covers cases where the underlying likelihood or extremum criterion function is possibly non-concave, discontinuous, and of...

Persistent link: https://www.econbiz.de/10014052489

The Gibbs Sampler with Particle Efficient Importance Sampling for State-Space Models

Grothe, Oliver - 2017

Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the … generic and easily implementable SMC approach known as Particle Efficient Importance Sampling (PEIS). By using SMC importance … sampling densities which are approximately fully globally adapted to the targeted density of the states, PEIS can substantially …

Persistent link: https://www.econbiz.de/10012970355

Markov Chain Monte Carlo Models, Gibbs Sampling, & Metropolis Algorithm for High-Dimensionality Complex Stochastic Problems

Malhotra, Yogesh - 2015

Algorithms, Gibbs Sampling and Metropolis-Hastings Algorithm. Network and security risk management application focus is on how …

Persistent link: https://www.econbiz.de/10013029835

Sequential Monte Carlo Sampling for DSGE Models

Herbst, Edward P. - 2013

We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples -- an artificial state-space model, the...

Persistent link: https://www.econbiz.de/10013074664

Scalable MCMC for large data problems using data subsampling and the difference estimator

Quiroz, Matias; Villani, Mattias; Kohn, Robert - 2015

observations. A key feature of our approach is the use of the highly efficient difference estimator from the survey sampling …

Persistent link: https://www.econbiz.de/10011300365

Finite sample bias corrected IV estimation for weak and many instruments

Harding, Matthew C.; Hausman, Jerry A.; Palmer, … - 2015

This paper considers the finite sample distribution of the 2SLS estimator and derives bounds on its exact bias in the presence of weak and/or many instruments. We then contrast the behavior of the exact bias expressions and the asymptotic expansions currently popular in the literature, including...

Persistent link: https://www.econbiz.de/10011300710

Finite sample performance of principal components estimators for dynamic factor models : asymptotic vs. bootstrap approximations

Shintani, Mototsugu; Guo, Zi-Yi - 2011 - This version: January 2011

This paper investigates the finite sample properties of the two-step estimators of dynamic factor models when unobservable common factors are estimated by the principal components methods in the first step. Effects of the number of individual series on the estimation of an auto-regressive model...

Persistent link: https://www.econbiz.de/10011723905

On the practice of Bayesian inference in basic economic time series models using Gibbs sampling

Pooter, Michiel de; Segers, Rene; Dijk, Herman K. van - 2006

Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling …

Persistent link: https://www.econbiz.de/10011349180

On importance sampling for state space models

Jungbacker, Borus; Koopman, Siem Jan - 2005

We consider likelihood inference and state estimation by means of importance sampling for state space models with a … are presented that lead to a more effective implementation of importance sampling for state space models. An illustration …

Persistent link: https://www.econbiz.de/10011348357