On-Line Monitoring the Stationarity for Time Seires

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.444-445.687 ◽

2013 ◽

Vol 444-445 ◽

pp. 687-691

Author(s):

Zhan Shou Chen

Keyword(s):

Time Series ◽

Monte Carlo ◽

Statistical Tests ◽

Variance Ratio ◽

Test Power ◽

Normal Sequence ◽

Fixed Sample ◽

Monte Carlo Studies ◽

Monitoring Procedure ◽

On Line

When analyzing time series an important issue is to decide whether the time series is stationary or nonstationary. Fixed sample statistical tests for that problem are well studies in the literature. In this paper we propose a moving variance ratio statistic to monitor the stationarity for normal sequence. Our Monte Carlo studies show that the proposed monitoring procedure has satisfactory test power and that the decision can often be made very early.

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A TIME SERIES APPLICATION OF THE USE OF MONTE CARLO METHODS TO COMPARE STATISTICAL TESTS

Journal of Time Series Analysis ◽

10.1111/j.1467-9892.1981.tb00325.x ◽

1981 ◽

Vol 2 (4) ◽

pp. 263-277 ◽

Cited By ~ 5

Author(s):

D. S. Poskitt ◽

A. R. Tremayne

Keyword(s):

Time Series ◽

Monte Carlo ◽

Monte Carlo Methods ◽

Statistical Tests

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An On-Line Change Detection Scheme for Nonlinear Time Series Data from Unknown Dynamical Systems: A Bayesian Appraoch Using Sequential Monte Carlo

2005 IEEE Workshop on Machine Learning for Signal Processing ◽

10.1109/mlsp.2005.1532922 ◽

2006 ◽

Cited By ~ 1

Author(s):

Y. Nakada ◽

T. Matsumoto

Keyword(s):

Time Series ◽

Monte Carlo ◽

Dynamical Systems ◽

Change Detection ◽

Sequential Monte Carlo ◽

Time Series Data ◽

Nonlinear Time Series ◽

Series Data ◽

Detection Scheme ◽

On Line

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Varying Responses to Common Shocks and Complex Cross-Sectional Dependence: Dynamic Multilevel Modeling with Multifactor Error Structures for Time-Series Cross-Sectional Data

Political Analysis ◽

10.1093/pan/mpu008 ◽

2014 ◽

Vol 22 (4) ◽

pp. 464-496 ◽

Cited By ~ 4

Author(s):

Xun Pang

Keyword(s):

Time Series ◽

Monte Carlo ◽

Multilevel Modeling ◽

Fixed Effects ◽

Autoregressive Process ◽

Cross Sectional ◽

Error Structure ◽

Complex Cross ◽

Monte Carlo Studies ◽

Cross Sectional Dependence

Multifactor error structures utilize factor analysis to deal with complex cross-sectional dependence in Time-Series Cross-Sectional data caused by cross-level interactions. The multifactor error structure specification is a generalization of the fixed-effects model. This article extends the existing multifactor error models from panel econometrics to multilevel modeling, from linear setups to generalized linear models with the probit and logistic links, and from assuming serial independence to modeling the error dynamics with an autoregressive process. I develop Markov Chain Monte Carlo algorithms mixed with a rejection sampling scheme to estimate the multilevel multifactor error structure model with apth-order autoregressive process in linear, probit, and logistic specifications. I conduct several Monte Carlo studies to compare the performance of alternative specifications and approaches with varying degrees of data complication and different sample sizes. The Monte Carlo studies provide guidance on when and how to apply the proposed model. An empirical application sovereign default demonstrates how the proposed approach can accommodate a complex pattern of cross-sectional dependence and helps answer research questions related to units' sensitivity or vulnerability to systemic shocks.

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Monte Carlo studies of pion distributions from heavy ion collisions

Physical Review C ◽

10.1103/physrevc.27.606 ◽

1983 ◽

Vol 27 (2) ◽

pp. 606-627 ◽

Cited By ~ 4

Author(s):

Hafez M. A. Radi ◽

John O. Rasmussen ◽

Kenneth A. Frankel ◽

John P. Sullivan ◽

H. C. Song

Keyword(s):

Monte Carlo ◽

Heavy Ion Collisions ◽

Heavy Ion ◽

Ion Collisions ◽

Monte Carlo Studies

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Statistical Tests of Symbolic Dynamics

Mathematics ◽

10.3390/math9080817 ◽

2021 ◽

Vol 9 (8) ◽

pp. 817

Author(s):

Fernando López ◽

Mariano Matilla-García ◽

Jesús Mur ◽

Manuel Ruiz Marín

Keyword(s):

Monte Carlo ◽

Symbolic Dynamics ◽

Null Hypothesis ◽

Statistical Tests ◽

General Applicability ◽

Monte Carlo Experiments ◽

The Mean ◽

Theoretical Application ◽

New Statistics ◽

General Method

A novel general method for constructing nonparametric hypotheses tests based on the field of symbolic analysis is introduced in this paper. Several existing tests based on symbolic entropy that have been used for testing central hypotheses in several branches of science (particularly in economics and statistics) are particular cases of this general approach. This family of symbolic tests uses few assumptions, which increases the general applicability of any symbolic-based test. Additionally, as a theoretical application of this method, we construct and put forward four new statistics to test for the null hypothesis of spatiotemporal independence. There are very few tests in the specialized literature in this regard. The new tests were evaluated with the mean of several Monte Carlo experiments. The results highlight the outstanding performance of the proposed test.

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