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Quasi-maximum likelihood and the kernel block bootstrap for nonlinear dynamic models

Parente, Paulo M. D. C.; Smith, Richard J. - 2019 - This Draft: October 2019

This paper applies a novel bootstrap method, the kernel block bootstrap, to quasi-maximum likelihood estimation of dynamic models with stationary strong mixing data. The method first kernel weights the components comprising the quasi-log likelihood function in an appropriate way and then samples...

Persistent link: https://www.econbiz.de/10012115888

Nested pseudo-likelihood estimation and bootstrap-based inference for structural discrete Markov decision models

Kasahara, Hiroyuki (contributor); … - 2006

This paper analyzes the higher-order properties of nested pseudo-likelihood (NPL) estimators and their practical implementation for parametric discrete Markov decision models in which the probability distribution is defined as a fixed point. We propose a new NPL estimator that can achieve...

Persistent link: https://www.econbiz.de/10003274966

Asymptotic distribution and finite sample bias correction of QML estimators for spatial error dependence model

Liu, Shew Fan; Yang, Zhenlin - In: Econometrics : open access journal 3 (2015) 2, pp. 376-411

In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model....

Persistent link: https://www.econbiz.de/10011297624

Analysis of Panel Vector Error Correction Models Using Maximum Likelihood, the Bootstrap, and Canonical-Correlation Estimators

Anderson, Richard G. - 2006

In this paper, we examine the use of Box-Tiao's (1977) canonical correlation method as an alternative to likelihood-based inferences for vector error-correction models. It is now well-known that testing of cointegration ranks based on Johansen's (1995) ML-based method suffers from severe small...

Persistent link: https://www.econbiz.de/10012732978

Expansions for Approximate Maximum Likelihood Estimators of the Fractional Difference Parameter

Lieberman, Offer; Phillips, Peter C. B. - 2004

This paper derives second-order expansions for the distributions of the Whittle and profile plug-in maximum likelihood estimators of the fractional difference parameter in the ARFIMA(0,d,0) with unknown mean and variance. Both estimators are shown to be second-order pivotal. This extends earlier...

Persistent link: https://www.econbiz.de/10014070489

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

Grothe, Oliver - 2017

We consider Particle Gibbs (PG) as a tool for Bayesian analysis of non-linear non-Gaussian state-space models. PG is a Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the Gibbs procedure to update the latent and potentially...

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

Finite sample comparison of parametric, semiparametric, and wavelet estimators of fractional integration

Nielsen, Morten Ørregaard (contributor); … - 2005

In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches and we consider both parametric and semiparametric estimation methods. The...

Persistent link: https://www.econbiz.de/10003780898

Maximum likelihood estimation for the fractional Vasicek model

Tanaka, Katsuto; Xiao, Weilin; Yu, Jun - In: Econometrics : open access journal 8 (2020) 3/32, pp. 1-28

maximum likelihood (ML). The asymptotic theory for the ML estimates (MLE) is established in the stationary case, the explosive … analysis. It is shown that changing the sign of the persistence parameter changes the asymptotic theory for the MLE, including … the rate of convergence and the limiting distribution. It is also found that the asymptotic theory depends on the value of …

Persistent link: https://www.econbiz.de/10012265682

Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood

Kristensen, Dennis - 2006

We propose a simulated maximum likelihood estimator (SMLE) for general stochastic dynamic models based on nonparametric kernel methods. The method requires that, while the actual likelihood function cannot be written down, we can still simulate observations from the model. From the simulated...

Persistent link: https://www.econbiz.de/10012734210

Simulated Maximum Likelihood Estimation in Transition Models

Kamionka, Thierry - 1999

In this paper we analyse the problem of modelling individual transitions in the presence of an incomplete sampling scheme. This problem is particularly cumbersome when a continuous time-scale is used for the modelling and when the model incorporates unobserved heterogeneity. This problem arises,...

Persistent link: https://www.econbiz.de/10014197183