scholarly journals STRUCTURAL CHANGE IN NONSTATIONARY AR(1) MODELS

2017 ◽  
Vol 34 (5) ◽  
pp. 985-1017 ◽  
Author(s):  
Tianxiao Pang ◽  
Terence Tai-Leung Chong ◽  
Danna Zhang ◽  
Yanling Liang

This article revisits the asymptotic inference for nonstationary AR(1) models of Phillips and Magdalinos (2007a) by incorporating a structural change in the AR parameter at an unknown time k0. Consider the model ${y_t} = {\beta _1}{y_{t - 1}}I\{ t \le {k_0}\} + {\beta _2}{y_{t - 1}}I\{ t > {k_0}\} + {\varepsilon _t},t = 1,2, \ldots ,T$, where I{·} denotes the indicator function, one of ${\beta _1}$ and ${\beta _2}$ depends on the sample size T, and the other is equal to one. We examine four cases: Case (I): ${\beta _1} = {\beta _{1T}} = 1 - c/{k_T}$, ${\beta _2} = 1$; (II): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 - c/{k_T}$; (III): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 + c/{k_T}$; and case (IV): ${\beta _1} = {\beta _{1T}} = 1 + c/{k_T}$, ${\beta _2} = 1$, where c is a fixed positive constant, and kT is a sequence of positive constants increasing to ∞ such that kT = o(T). We derive the limiting distributions of the t-ratios of ${\beta _1}$ and ${\beta _2}$ and the least squares estimator of the change point for the cases above under some mild conditions. Monte Carlo simulations are conducted to examine the finite-sample properties of the estimators. Our theoretical findings are supported by the Monte Carlo simulations.

2019 ◽  
Vol 12 (2) ◽  
pp. 64 ◽  
Author(s):  
Sadat Reza ◽  
Paul Rilstone

This paper extends Horowitz’s smoothed maximum score estimator to discrete-time duration models. The estimator’s consistency and asymptotic distribution are derived. Monte Carlo simulations using various data generating processes with varying error distributions and shapes of the hazard rate are conducted to examine the finite sample properties of the estimator. The bias-corrected estimator performs reasonably well for the models considered with moderately-sized samples.


2018 ◽  
Vol 615 ◽  
pp. A62 ◽  
Author(s):  
G. Valle ◽  
M. Dell’Omodarme ◽  
P. G. Prada Moroni ◽  
S. Degl’Innocenti

Aims. The capability of grid-based techniques to estimate the age together with the convective core overshooting efficiency of stars in detached eclipsing binary systems for main sequence stars has previously been investigated. We have extended this investigation to later evolutionary stages and have evaluated the bias and variability on the recovered age and convective core overshooting parameter accounting for both observational and internal uncertainties. Methods. We considered synthetic binary systems, whose age and overshooting efficiency should be recovered by applying the SCEPtER pipeline to the same grid of models used to build the mock stars. We focus our attention on a binary system composed of a 2.50 M⊙ primary star coupled with a 2.38 M⊙ secondary. To explore different evolutionary scenarios, we performed the estimation at three different times: when the primary is at the end of the central helium burning, when it is at the bottom of the RGB, and when it is in the helium core burning phase. The Monte Carlo simulations have been carried out for two typical values of accuracy on the mass determination, that is, 1% and 0.1%. Results. Adopting typical observational uncertainties, we found that the recovered age and overshooting efficiency are biased towards low values in all three scenarios. For an uncertainty on the masses of 1%, the underestimation is particularly relevant for a primary in the central helium burning stage, reaching − 8.5% in age and − 0.04 (− 25% relative error) in the overshooting parameter β. In the other scenarios, an undervaluation of the age by about 4% occurs. A large variability in the fitted values between Monte Carlo simulations was found: for an individual system calibration, the value of the overshooting parameter can vary from β = 0.0 to β = 0.26. When adopting a 0.1% error on the masses, the biases remain nearly unchanged but the global variability is suppressed by a factor of about two. We also explored the effect of a systematic discrepancy between the artificial systems and the model grid by accounting for an offset in the effective temperature of the stars by ± 150 K. For a mass error of 1% the overshooting parameter is largely biased towards the edges of the explored range, while for the lower mass uncertainty it is basically unconstrained from 0.0 to 0.2. We also evaluate the possibility of individually recovering the β value for both binary stars. We found that this is impossible for a primary near to central hydrogen exhaustion owing to huge biases for the primary star of + 0.14 (90% relative error), while in the other cases the fitted β are consistent, but always biased by about − 0.04 (− 25% relative error). Finally, the possibility to distinguish between models computed with mild overshooting from models with no overshooting was evaluated, resulting in a reassuring power of distinction greater than 80%. However, the scenario with a primary in the central helium burning was a notable exception, showing a power of distinction lower than 5%.


2021 ◽  
Author(s):  
Masahide Sato

Abstract Performing isothermal-isochoric Monte Carlo simulations, I examine the types of clusters that dumbbell-like one–patch particles form in thin space between two parallel walls, assuming that each particle is synthesized through the merging of two particles, one non-attracting and the other attracting for which, for example, the inter-particle interaction is approximated by the DLVO model. The shape of these dumbbell-like particles is controlled by the ratio of the diameters q of the two spherical particles and by the dimensionless distance l between them. Using a modified Kern–Frenkel potential, I examine the dependence of the cluster shape on l and q. Large island-like clusters are created when q < 1. With increasing q, the clusters become chain-like. When q increases further, elongated clusters and regular polygonal clusters are created. In hte simulations, the cluster shape becomes three-dimensional with increasing l because the thickness of the thin system increases proportionally to l.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Masahide Sato

AbstractPerforming isothermal-isochoric Monte Carlo simulations, I examine the types of clusters that dumbbell-like one–patch particles form in thin space between two parallel walls, assuming that each particle is synthesized through the merging of two particles, one non-attracting and the other attracting for which, for example, the inter-particle interaction is approximated by the DLVO model . The shape of these dumbbell-like particles is controlled by the ratio of the diameters q of the two spherical particles and by the dimensionless distance l between these centers. Using a modified Kern–Frenkel potential, I examine the dependence of the cluster shape on l and q. Large island-like clusters are created when $$q<1$$ q < 1 . With increasing q, the clusters become chain-like . When q increases further, elongated clusters and regular polygonal clusters are created. In the simulations, the cluster shape becomes three-dimensional with increasing l because the thickness of the thin system increases proportionally to l.


2011 ◽  
Vol 172-174 ◽  
pp. 658-663 ◽  
Author(s):  
Mohamed Briki ◽  
Jérôme Creuze ◽  
Fabienne Berthier ◽  
Bernard Legrand

In order to build the phase diagram of Cu-Ag nanoalloys, we study a 405-atom nanoparticle by means of Monte Carlo simulations with relaxations usingN-body interatomic potentials. We focus on a range of nominal concentrations for which the cluster core remains Cu-pure and the (001) facets of the outer shell exhibit two original phenomena. Within the (N,mAg-mCu,P,T) ensemble, a structural and chemical bistability is observed, which affects all the (001) facets together. For a nanoparticle assembly, this will result in a bimodal distribution of clusters, some of them having their (001) facets Cu-rich with the usual square shape, the other ones having their (001) facets Ag-rich with a diamond shape. This bistability is replaced in the (NAg,NCu,P,T) ensemble by a continuous evolution of both the structure and the concentration of the (001) facets from Cu-rich square-shaped to Ag-rich diamond-shaped facets as the number of Ag atoms increases. For a nanoparticle assembly, this will result in an unimodal distribution of the cluster population concerning the properties of the (001) facets. This comparison between pseudo grand canonical and isothermal-isobaric results shows that the distribution of a population of bimetallic nanoparticles depends strongly on the conditions under it is elaborated.


2012 ◽  
Vol 263-266 ◽  
pp. 506-509
Author(s):  
Xiao Gang Wang

Detection and estimation of structural change at unknown dates in panel model was investigated. The structural change can be consistently estimated and asymptotic distribution was derived. Monte Carlo simulations were presented to access the theoretical result numerically.


1998 ◽  
Vol 14 (2) ◽  
pp. 200-221 ◽  
Author(s):  
Jörg Breitung ◽  
Philip Hans Franses

In this paper we consider a semiparametric version of the test for seasonal unit roots suggested by Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238). The asymptotic theory is based on the analysis of a simple regression problem, and the results apply to tests at any given frequency in the range (0,π]. Monte Carlo simulations suggest that the test may have more power than the parametric test of Hylleberg et al. (1990). On the other hand, the semiparametric version suffers from severe size distortions in some situations.


1987 ◽  
Vol 3 (3) ◽  
pp. 359-370 ◽  
Author(s):  
Koichi Maekawa

We compare the distributional properties of the four predictors commonly used in practice. They are based on the maximum likelihood, two types of the least squared, and the Yule-Walker estimators. The asymptotic expansions of the distribution, bias, and mean-squared error for the four predictors are derived up to O(T−1), where T is the sample size. Examining the formulas of the asymptotic expansions, we find that except for the Yule-Walker type predictor, the other three predictors have the same distributional properties up to O(T−1).


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