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Showing 8 results for Autoregressive Model
Hamidreza Mostafaei, Maryam Safaei,
Volume 3, Issue 2 (3-2010)
Abstract
In 2002 the enforcement on policy unification of exchange rate caused dramatic decrease in the nominal price of Iran's Rial against U.S.dollar per on unit.For this reason due to the existence of unexpected and large change we cannot use the linear time series models for surveying the fluctuations of the rate of Iran's Rial change against U.S. dollar per on unit. In this paper we compare Self-Exciting threshold autoregressive and Markov switching autoregressive model. then it will be show that only the Markov switching autoregressive model being able to show the behaviors of Iran's exchange rate.
Maryam Safaei,
Volume 5, Issue 1 (9-2011)
Abstract
This paper offers a method of the estimation of the transition probability for the behaviors of financial time series by Markov Switching Autoregressive model. Using this model, the behaviors of fluctuations of exchange rate form two regimes low and high changes rate are considered. Results of prediction show that the persistence probability of regimes will be decreased. Thus, the probability of transition to other regime will be increased if process were in a specific regime.
Sedighe Zamani Mehryan, Ali Reza Nematollahi,
Volume 7, Issue 2 (3-2014)
Abstract
In this paper, the pseudo-likelihood estimators and the limiting distribution of the score test statistic associated with several hypothesis tests such as unit root test for the linear regression models with stationary and nonstationary residuals are calculated. The limiting behavior of theses test statistics by using a simpler approach of the original presentation is derived. Also by using a Mont Carlo method, it is shown that the derived pseudo-likelihood estimators are appropriate. The quantiles of the limiting distribution of the test statistic for a unit root are also calculated and a new table is provided which can be used by researchers for the unit root test.
Azadeh Mojiri, Yadolla Waghei, Hamid Reza Nili Sani, Gholam Reza Mohtashami Borzadaran,
Volume 12, Issue 1 (9-2018)
Abstract
Prediction of spatial variability is one of the most important issues in the analysis of spatial data. So predictions are usually made by assuming that the data follow a spatial model. In General, the spatial models are the spatial autoregressive (SAR), the conditional autoregressive and the moving average models. In this paper, we estimated parameter of SAR(2,1) model by using maximum likelihood and obtained formulas for predicting in SAR models, including the prediction within the data (interpolation) and outside the data (extrapolation). Finally, we evaluate the prediction methods by using image processing data.
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.
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.
Elham Ranjbar, Mohamad Ghasem Akbari, Reza Zarei,
Volume 19, Issue 1 (9-2025)
Abstract
In the time series analysis, we may encounter situations where some elements of the model are imprecise quantities. One of the most common situations is the inaccuracy of the underlying observations, usually due to measurement or human errors. In this paper, a new fuzzy autoregressive time series model based on the support vector machine approach is proposed. For this purpose, the kernel function has been used for the stability and flexibility of the model, and the constraints included in the model have been used to control the points. In order to examine the performance and effectiveness of the proposed fuzzy autoregressive time series model, some goodness of fit criteria are used. The results were based on one example of simulated fuzzy time series data and two real examples, which showed that the proposed method performed better than other existing methods.
Arezu Rahmanpour, Yadollah Waghei, Gholam Reza Mohtashami Borzadaran,
Volume 19, Issue 1 (9-2025)
Abstract
Change point detection is one of the most challenging statistical problems because the number and position of these points are unknown. In this article, we will first introduce the concept of change point and then obtain the parameter estimation of the first-order autoregressive model AR(1); in order to investigate the precision of estimated parameters, we have done a simulation study. The precision and consistency of parameters were evaluated using MSE. The simulation study shows that parameter estimation is consistent. In the sense that as the sample size increases, the MSE of different parameters converges to zero. Next, the AR(1) model with the change point was fitted to Iran's annual inflation rate data (from 1944 to 2022), and the inflation rate in 2023 and 2024 was predicted using it.
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