scholarly journals Bootstrapping the Autocovariance of PC Time Series - A Simulation Study

2019 ◽  
pp. 41-55
Author(s):  
Anna E. Dudek ◽  
Paweł Potorski
Keyword(s):  
Time Series ◽  
Simulation Study

scholarly journals The CSS and The Two-Staged Methods for Parameter Estimation in SARFIMA Models

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Erol Egrioglu ◽  
Cagdas Hakan Aladag ◽  
Cem Kadilar
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Simulation Study ◽  
Long Memory ◽  
Moving Average ◽  
Mean Square ◽  
Sample Sizes ◽  
Advantages And Disadvantages

Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) models are used in the analysis of seasonal long memory-dependent time series. Two methods, which are conditional sum of squares (CSS) and two-staged methods introduced by Hosking (1984), are proposed to estimate the parameters of SARFIMA models. However, no simulation study has been conducted in the literature. Therefore, it is not known how these methods behave under different parameter settings and sample sizes in SARFIMA models. The aim of this study is to show the behavior of these methods by a simulation study. According to results of the simulation, advantages and disadvantages of both methods under different parameter settings and sample sizes are discussed by comparing the root mean square error (RMSE) obtained by the CSS and two-staged methods. As a result of the comparison, it is seen that CSS method produces better results than those obtained from the two-staged method.

scholarly journals Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shan Jiang ◽  
Joshua L. Warren ◽  
Noah Scovronick ◽  
Shannon E. Moss ◽  
Lyndsey A. Darrow ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Health Outcomes ◽  
Simulation Study ◽  
Heat Exposure ◽  
Emergency Department Visits ◽  
Extreme Heat ◽  
Logic Regression ◽  
Adverse Health ◽  
Adverse Health Outcomes

Abstract Background Short-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations. Methods Logic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency department visits for 12 outcomes in the Atlanta metropolitan area. Results For the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient. Conclusion Logic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.

scholarly journals Measurement error in time-series analysis: a simulation study comparing modelled and monitored data

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Barbara K Butland ◽  
Ben Armstrong ◽  
Richard W Atkinson ◽  
Paul Wilkinson ◽  
Mathew R Heal ◽  
...  
Keyword(s):  
Time Series ◽  
Measurement Error ◽  
Time Series Analysis ◽  
Simulation Study ◽  
Series Analysis

scholarly journals Sample size and power considerations for ordinary least squares interrupted time series analysis: a simulation study

2019 ◽  
Vol Volume 11 ◽  
pp. 197-205 ◽  
Author(s):  
Samuel Hawley ◽  
M Sanni Ali ◽  
Klara Berencsi ◽  
Andrew Judge ◽  
Daniel Prieto-Alhambra
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Sample Size ◽  
Least Squares ◽  
Simulation Study ◽  
Interrupted Time Series ◽  
Ordinary Least Squares ◽  
Interrupted Time Series Analysis ◽  
Series Analysis

scholarly journals A SIMPLE EFFICIENT INSTRUMENTAL VARIABLE ESTIMATOR FOR PANEL AR(p) MODELS WHEN BOTHNANDTARE LARGE

2009 ◽  
Vol 25 (3) ◽  
pp. 873-890 ◽  
Author(s):  
Kazuhiko Hayakawa
Keyword(s):  
Time Series ◽  
Lower Bound ◽  
Asymptotic Distribution ◽  
Cross Section ◽  
Simulation Study ◽  
Instrumental Variable ◽  
Asymptotic Variance ◽  
The Cross ◽  
Instrumental Variable Estimator ◽  
Normally Distributed

In this paper, we show that for panel AR(p) models, an instrumental variable (IV) estimator with instruments deviated from past means has the same asymptotic distribution as the infeasible optimal IV estimator when bothNandT, the dimensions of the cross section and time series, are large. If we assume that the errors are normally distributed, the asymptotic variance of the proposed IV estimator is shown to attain the lower bound when bothNandTare large. A simulation study is conducted to assess the estimator.

A Comparative Simulation Study of ARIMA and Fuzzy Time Series Model for Forecasting Time Series Data

2018 ◽  
pp. 49-56
Author(s):  
Haji A. Haji ◽  
Kusman Sadik ◽  
Agus Mohamad Soleh
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Data Generation ◽  
Data Set

Simulation study is used when real world data is hard to find or time consuming to gather and it involves generating data set by specific statistical model or using random sampling. A simulation of the process is useful to test theories and understand behavior of the statistical methods. This study aimed to compare ARIMA and Fuzzy Time Series (FTS) model in order to identify the best model for forecasting time series data based on 100 replicates on 100 generated data of the ARIMA (1,0,1) model.There are 16 scenarios used in this study as a combination between 4 data generation variance error values (0.5, 1, 3,5) with 4 ARMA(1,1) parameter values. Furthermore, The performances were evaluated based on three metric mean absolute percentage error (MAPE),Root mean squared error (RMSE) and Bias statistics criterion to determine the more appropriate method and performance of model. The results of the study show a lowest bias for the chen fuzzy time series model and the performance of all measurements is small then other models. The results also proved that chen method is compatible with the advanced forecasting techniques in all of the consided situation in providing better forecasting accuracy.

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