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Showing 11 results for Subject: Time Series
Rahim Chinipardaz, Hoda Kamranfar,
Volume 3, Issue 1 (9-2009)
Abstract
This paper is concerned with the study of the effect of outliers in GARCH models. Four common outliers are considered: additive outliers, innovation outliers, level change and temporary change. Each of the outlier is embedded to a GARCH model and then the effectness of outliers in this model is studied. The residuals of the models have been investigated for both cases, the usual GARCH model and the GARCH model in the present of outliers.
Gholam Ali Parham, Parisa Masjedi,
Volume 7, Issue 2 (3-2014)
Abstract
One of the issues in reviewing the performance of a financial market is existence of long-term memory. Since for a financial time series, we may find this feature in the volatility. So reviewing in volatility has been considered by many economists. A common method for identification and modeling of long-term memory in the volatility is to use FIGARCH models. In this paper, we identify and model long-term memory in the data exchange rates volatility (EUR/IRR). According to the statistical properties of skewness, heavy tail and excess kurtosis of data, assuming normal residuals being rejected and therefore cannot identify model by using common methods. The data structure looks NIG distribution is a good choice for the distribution of residuals. Hence with this assumption, we again identify model. The results show a good selection for data is FIGARCH-NIG model.
Reza Zabihi Moghadam, Rahim Chinipardaz, Gholamali Parham,
Volume 12, Issue 1 (9-2018)
Abstract
In this paper a method has been given to detect the shocks in structural time series using Kalman filter algorithm. As the Kalman filter algorithm is used for state space forms which include ARMA models as an especial case, the suggested method can be used for more general time series than linear models. Five shocks; additive outlier, level change, seasonal change, periodic change and slope change have been reviewed with this method. The performance of suggested method has been shown via a simulation study. The marriage data set from England has been considered as a real data set to study.
Behzad Mansouri, Rahim Chinipardaz,
Volume 12, Issue 2 (3-2019)
Abstract
In this paper, using Band matrix, a method has been proposed to estimating the covariance matrix of the ARMA model and the likelihood function of the ARMA model with diagonal covariance matrix has been obtained and approximations for Kullback-Leibler and Chernoff criteria were presented. In addition, two rules for discriminating the ARMA models has been proposed. A simulation and real data sets are used to illustrate the performance of the proposed rules. Significant reduction of the calculations for large time series and low discrimination error rate are two characteristics of the proposed rules. In addition no need to normal assumption is showed in a theorem.
Mohammad Reza Yeganegi, Rahim Chinipardaz,
Volume 13, Issue 1 (9-2019)
Abstract
This paper is investigating the mixture autoregressive model with constant mixing weights in state space form and generalization to ARMA mixture model. Using a sequential Monte Carlo method, the forecasting, filtering and smoothing distributions are approximated and parameters f the model is estimated via the EM algorithm. The results show the dimension of parameter vector in state space representation reduces. The results of the simulation study show that the proposed filtering algorithm has a steady state close to the real values of the state vector. Moreover, according to simulation results, the mean vectors of filtering and smoothing distribution converges to state vector quickly.
Emad Ashtari Nezhad, Yadollah Waghei, Gholam Reza Mohtashami Borzadaran, Hamid Reza Nili Sani, Hadi Alizadeh Noughabi,
Volume 13, Issue 1 (9-2019)
Abstract
Before analyzing a time series data, it is better to verify the dependency of the data, because if the data be independent, the fitting of the time series model is not efficient. In recent years, the power divergence statistics used for the goodness of fit test. In this paper, we introduce an independence test of time series via power divergence which depends on the parameter λ. We obtain asymptotic distribution of the test statistic. Also using a simulation study, we estimate the error type I and test power for some λ and n. Our simulation study shows that for extremely large sample sizes, the estimated error type I converges to the nominal α, for any λ. Furthermore, the modified chi-square, modified likelihood ratio, and Freeman-Tukey test have the most power.
Mehrnaz Mohammadpour, Masoumeh Shirozhan,
Volume 14, Issue 1 (8-2020)
Abstract
In this paper, we introduce a new integer-valued autoregressive model of first order based on the negative binomial thinning operator, where the noises are serially dependent. Some statistical properties of the model are discussed. The model parameters are estimated by maximum likelihood and Yule-Walker methods. By a simulation study, the performances of the two estimation methods are studied. This survey was carried out to study the efficiency of the new model by applying it on real data.
Eisa Mahmoudi, Soudabeh Sajjadipanah, Mohammad Sadegh Zamani,
Volume 16, Issue 1 (9-2022)
Abstract
In this paper, a modified two-stage procedure in the Autoregressive model AR(1) is considered, which investigates the point and the interval estimation of the mean based on the least-squares estimator. The modified two-stage procedure is as effective as the best fixed-sample size procedure. In this regard, the significant properties of the procedure, including asymptotic risk efficiency, first-order efficiency, consistent, and asymptotic distribution of the mean, are established. Then, a Monte Carlo simulation study is deduced to investigate the modified two-stage procedure. The performance of estimators and confidence intervals are evaluated utilizing a simulation study. Finally, real-time series data is considered to illustrate the applicability of the modified two-stage procedure.
Mr Reza Zabihi Moghadam, Dr Masoud Yarmohammadi, Dr Hossein Hassani, Dr Parviz Nasiri,
Volume 16, Issue 2 (3-2023)
Abstract
The Singular Spectrum Analysis (SSA) method is a powerful non-parametric method in the field of time series analysis and has been considered due to its features such as no need to stationarity assumptions or a limit on the number of collected observations. The main purpose of the SSA method is to decompose time series into interpretable components such as trend, oscillating component, and unstructured noise. In recent years, continuous efforts have been made by researchers in various fields of research to improve this method, especially in the field of time series prediction. In this paper, a new method for improving the prediction of singular spectrum analysis using Kalman filter algorithm in structural models is introduced. Then, the performance of this method and some generalized methods of SSA are compared with the basic SSA using the root mean square error criterion. For this comparison, simulated data from structural models and real data of gas consumption in the UK have been used. The results of this study show that the newly introduced method is more accurate than other methods.
Mr Einolah Deiri, Dr Einolah Deiri, Dr Ezzatallah Jamkhaneh,
Volume 16, Issue 2 (3-2023)
Abstract
In this paper, a new integer-valued autoregressive process is introduced based on the discrete exponential-Weibull distribution to model integer-value time series data. Regarding the importance of discrete distributions in counting data modeling, the discrete counterpart of the exponential-Weibull distribution is introduced, and some of its statistical properties, such as survival function, hazard rate, moment generating function, skewness and kurtosis, are investigated. The Fisher dispersion, skewness and kurtosis indices show the flexibility and efficiency of the discrete Exponential-Weibull distribution in fitting different types of counting data. The discrete Exponential-Weibull distribution covers data fits with different dispersion characteristics (overdispersion, underdispersion and equidispersion), long right tail (skewed to the right) and heavy-tailed. The model parameters are estimated using three approaches maximum conditional likelihood, minimum generalized conditional squares, and Yule-Walker. Finally, the efficiency and superiority of the process in fitting counts data of deaths due to COVID-19 disease are compared with other competing models.
Najmeh Rezaeerad, Mahnaz Khalafi, Mohsen Hoseinalizadeh, Majid Azimmohseni,
Volume 17, Issue 2 (2-2024)
Abstract
The analysis of spatio-temporal series is crucial but a challenge in different sciences. Accurate analyses of spatio-temporal series depend on how to measure their spatial and temporal relation simultaneously. In this article, one-sided dynamic principal components (ODPC) for spatio-temporal series are introduced and used to model the common structure of their relation. These principal components can be used in the data set, including many spatio-temporal series. In addition to spatial relations, trends, and seasonal trends, the dynamic principal components reflect other common temporal and spatial factors in spatio-temporal series. In order to evaluate the capability of one-sided dynamic principal components, they are used for clustering and forecasting in spatio-temporal series. Based on the precipitation time series in different stations of Golestan province, the efficiency of the principal components in the clustering of hydrometric stations is investigated. Moreover, forecasting for the SPI index, an essential indicator for detecting drought, is conducted based on the one-sided principal components.
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