Time Series Analysis of Monte Carlo Fission Sources—I: Dominance Ratio Computation

2004 ◽  
Vol 148 (3) ◽  
pp. 374-390 ◽  
Author(s):  
Taro Ueki ◽  
Forrest B. Brown ◽  
D. Kent Parsons ◽  
James S. Warsa
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Time Series Analysis ◽  
Dominance Ratio ◽  
Series Analysis ◽  
Fission Sources

scholarly journals An Independence Test Based on Symbolic Time Series

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Wiston Adrián Risso
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Time Series Analysis ◽  
Monte Carlo Simulations ◽  
Degrees Of Freedom ◽  
Chaotic Systems ◽  
Small Samples ◽  
Independence Test ◽  
Series Analysis ◽  
Symbolic Time Series Analysis

An independence test based on symbolic time series analysis (STSA) is developed. Considering an independent symbolic time series there is a statistic asymptotically distributed as a CHI-2 with n-1 degrees of freedom. Size and power experiments for small samples were conducted applying Monte Carlo simulations and comparing the results with BDS and runs test. The introduced test shows a good performance detecting independence in nonlinear and chaotic systems.

scholarly journals A note on robust and non-robust variogram estimators

2008 ◽  
Vol 61 (1) ◽  
pp. 87-95 ◽  
Author(s):  
Sueli Aparecida Mingoti ◽  
Gilmar Rosa
Keyword(s):  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Series Analysis ◽  
Simulation Data ◽  
Contaminated Data ◽  
Series Analysis ◽  
Variogram Model ◽  
Wave Models

In 1998 Genton proposed a variogram estimator claimed to be robust against outliers and compared it to Matheron's and Cressie-Hawkins' variogram estimators. Lark (2000) extended the comparison evaluating the effects of nonnormality. However, the comparison was limited to the spherical variogram model. In this paper 4 variogram estimators are compared including Genton's by using Monte Carlo simulation. Data with and without outliers were simulated using the spherical, exponential and wave models. The results showed that Genton's and the Median estimators were the best choices for contaminated data, while those of Matheron and Haslett presented better results for non-contaminated date; this latter being appropriate only for time series analysis.

scholarly journals THE ECONOMIC GROWTH FORECAST AND A REVIEW OF THE IMPACTS OF COVID-19 ON THE GHANAIAN ECONOMY: APPLICATION OF TIME SERIES ANALYSIS AND MONTE-CARLO SIMULATION

2020 ◽  
pp. 83-95
Author(s):  
Professor Li Fang Lin ◽  
Blessed Kwasi Adjei ◽  
Felix Kwame Nyarko
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Time Series Analysis ◽  
Arima Model ◽  
Information Criterion ◽  
Growth Trend ◽  
Series Analysis ◽  
The Government

This manuscript explores the effects of Covid-19 pandemic on the economic activities of Ghana by first modelling the Economic growth figures of Ghana; discuss the current covid-19 situation and its economic impact on the nation and to wrap things up by suggesting remedial measures necessary to salvage the situation at hand. To model and forecast the Economic growth trend, the times series analysis and the Monte Carlo simulation (Laplace distribution) techniques were employed. The success of the ARIMA model was monitored through Akaike information Criterion (AIC) where irrefutably the absolute number shows the success of the model - the lower the number, the better the model. The research results showed that in spite of promising economic forecasts, with the force of the pandemic soaring universally, there is no doubt that the economic prosperity of Ghana will be disrupted and major revenue margins shrinked this year. However, due to some solid and harsh measures set out by the government we are optimistic that situations will be well contained and managed. The scientific contribution of the research lies in the fact that it will offer a new way of perceiving risks and uncertainties when policy makers are drafting budgets and economic policies going forward. In that capacity, they will not only adapt to practical and analytical methods to forecast but additionally consider some unforeseen circumstances beyond the control of humanity that may have tormenting impact on economic outputs. KEYWORDS: Time series analysis, Covid-19, Monte-Carlo simulation, GDP per Capita, Modelling, Economic Growth.

Sign in / Sign up

Export Citation Format

Share Document