Asymmetry of the Oil Price Pass –Through to Inflation in Iran

Due to the structure of Iran’s economy, oil revenues do not have a multi-dimensional role rather than a one-dimensional role in inflation. To put it differently, oil revenues impact inflation through exchange rate, government budget, importation, and imported inflation, monetary base, GDP growth, and government investment. These factors sometimes have contradictory effects on inflation. Therefore, investigating and analyzing the pass-through of oil shocks into inflation and providing appropriate policies is quite essential. Hence, the present research is primarily aimed at modeling the pass-through of oil price and investigating its effect on inflation by means of hidden co-integration approach, analysis, and presenting political implications to control the effect of oil shocks on inflation. In order to do so, monthly data of crude oil and consumer price index from March 2003 to March 2013 have been utilized. The findings demonstrated the pass-through of oil price to the CPI in Iran. Moreover, the coefficient calculated in this study revealed that the magnitude of this pass-through is quite large in the long run in Iran’s economy. In addition, based on the CECM model which is a type of non-linear, asymmetric, and hidden co-integration method this research showed that the pass-through of oil price to inflation is asymmetrical. On the other hand, the dynamic short-term relationship, in the framework of CECM model, also confirmed the asymmetrical pass-through of positive and negative oil shocks into inflation. Keywords: oil price; inflation; Asymmetric Pass-Through; CECM model. JEL Classifications: C13; C22; E31; Q4

Investigating the relationship between physical and mental health among operating room personnel

BACKGROUND: Communities have been involved in adverse health factors at any point in time and have sought to improve community health and quality of life (QOL). The purpose of this study is to investigate the condition of physical and mental health and the relation between the two variables with each other in the operating room of hospitals in Shiraz, Iran.METHODS: This was a cross-sectional study performed on 192 staff of operating rooms of Shiraz University of Medical Sciences. The data for the study were collected through the General Health Questionnaire (GHQ) and the General Lifestyle Questionnaire (GLQ).RESULTS: Analyzing the results, the mean total scores of lifestyle and mental health were 333.93 ± 42.91 and 39.24 ± 12.59, respectively. In addition, the correlation coefficient between the total lifestyle and mental health scores was -0.411.CONCLUSION: Since the operating room is the most sensitive part of any hospital and the so-called heart of the hospital, special attention should be paid to staff working in this department, as any disruption to the operating room staff is not only harmful to them. Rather, it has many detrimental effects on patients and the health system. Therefore, given the stressful environment of the operating room, managers should promote operating room programs focused on reducing stress and by conducting classes and training sessions, improve the mental and physical health of the operating room personnel

Investigation of the Lambda Parameter for Language Modeling Based Persian Retrieval

Language modeling is one of the most powerful methods in information retrieval. Many language modeling based retrieval systems have been developed and tested on English collections. Hence, the evaluation of language modeling on collections of other languages is an interesting research issue. In this study, four different language modeling methods proposed by Hiemstra [1] have been evaluated on a large Persian collection of a news archive. Furthermore, we study two different approaches that are proposed for tuning the Lambda parameter in the method. Experimental results show that the performance of language models on Persian text improves after Lambda Tuning. More specifically Witten Bell method provides the best results

Oil Price Pass-Through into Domestic Inflation: The Case of Iran

Review of economic developments in Iran over the past four decades shows that oil revenues have deep and wide impact on economic indicators. The Two channels which oil price changes directly or indirectly affect inflation as the most important Economic variables are: increase in demand  (mainly by government public budget and Influencing the components of monetary base and money supply) and increase in production costs (via the price of factors of production). In this regard, the present paper attempts to investigate the nature and causes of oil price pass-through into inflation in the short-and-long term; analysis of the pass-through and in addition design the necessary policies to control its destructive consequences. For this purpose, the Dynamic Error Correction Model was used and the data were collected monthly from 2003/3 to 2013/3. The findings showed that the oil price pass-through into inflation in both short-and-long term were Positive and incomplete. Therefore, it would be useful in policymaking. Keywords: Oil Price; Inflation; Pass-Through; Error Correction Model. JEL Classifications: C13; C22; E31; Q43

Drug Interactions in Iranian Veterans With Chronic Spinal Cord Injury - A Descriptive Study

Background: Veterans with chronic spinal cord injury usually have various comorbidities. They are, therefore, visited by different doctors and use different medications. It is necessary to monitor the health of these veterans. One of the important issues in this regard is the attention to drug interactions. The purpose of this study was to investigate the drugs used and their interactions.Methods: This descriptive study of the cross-sectional studies was carried out retrospectively in 2015 under the Shefa Neuroscience Research Center’s supervision, examining the medical records of veterans with spinal cord injury participating in the health screening program at Khatam Alanbiya hospital in Tehran. Demographic data, comorbidities, used drugs, and the level of involvement collected. According to the FDA, drug interactions among the drugs used for each patient has evaluated and classified into three severe, moderate, and weak groups. SPSS v. 21 analyzed data.Results: The study population consisted of 404 men, ranging in age from 41 to 74, with a mean of 51.6 ± 6.4 years. One hundred forty-two of them (35.1%) had a complete injury, and 262 veterans (64.8%) had an incomplete injury. Only 17 veterans (4.2%) had no drug interactions. The number of drug interactions varied from 1 to 38, with an average of 5.9 ± 12.8 interactions per patient. The total number of interactions was 2856, of which 32.5% were weak, 55.3% moderate, and 12.2% severe, with a 95% confidence interval. Among the severe drug interactions in the study, the highest number belonged to the antidepressant drugs.Conclusion: This study highlights the necessity of developing a strategy for investigating and preventing drug interactions in veterans with chronic spinal cord injury. It has recommended that physicians pay more attention to other medications used by the patient and prescribe as little as possible of the drug and the drug with the least number of interactions

Long Memory Analysis: An Empirical Investigation

This study is an attempt to review the theory and applications of autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models, mainly for the purpose of the description of the observed persistence in the mean and volatility of a time series. The long memory feature in FIGARCH models makes them a better candidate than other conditional heteroskedasticity models for modeling volatility in financial series. ARFIMA model also has a considerable capacity for modeling the return behavior of these time series. The daily data related to Tehran Stock Exchange (TSE) index was used for the purpose of this study. Considering the fact that the existence of conditional heteroskedasticity effects were confirmed in the stock return series, robust regression technique was used for estimation of different ARFIMA models. Furthermore, different GARCH-type models were also compared. The results of ARFIMA model are indicative of the absence of long memory in return series of the TSE index and the results from FIGARCH model show evidence of long memory in conditional variance of this series

Forecasting Stock Market Volatility: A Forecast Combination Approach

Recently, with the development of financial markets and due to the importance of these markets and their close relationship with other macroeconomic variables, using advanced mathematical models with complicated structures for forecasting these markets has become very popular. Besides, neural network models have gained a special position compared to other advanced models due to their high accuracy in forecasting different variables. Therefore, the main purpose of this study was to forecast the volatilities of TSE index by regressive models with long memory feature, feed forward neural network and hybrid models (based on forecast combination approach) using daily data. The results were indicative of the fact that based on the criteria for assessing forecasting error, i.e., MSE and RMSE, although forecasting errors of the feed forward neural network model were less than ARFIMA-FIGARCH model, the accuracy of the hybrid model of neural network and best GARCH was higher than each one of these models

Forecasting Stock Market Volatility: A Forecast Combination Approach

Recently, with the development of financial markets and due to the importance of these markets and their close relationship with other macroeconomic variables, using advanced mathematical models with complicated structures for forecasting these markets has become very popular. Besides, neural network models have gained a special position compared to other advanced models due to their high accuracy in forecasting different variables. Therefore, the main purpose of this study was to forecast the volatilities of TSE index by regressive models with long memory feature, feed forward neural network and hybrid models (based on forecast combination approach) using daily data. The results were indicative of the fact that based on the criteria for assessing forecasting error, i.e., MSE and RMSE, although forecasting errors of the feed forward neural network model were less than ARFIMA-FIGARCH model, the accuracy of the hybrid model of neural network and best GARCH was higher than each one of these models

Long Memory Analysis: An Empirical Investigation

This study is an attempt to review the theory and applications of autoregressive fractionally integrated moving average (ARFIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models, mainly for the purpose of the description of the observed persistence in the mean and volatility of a time series. The long memory feature in FIGARCH models makes them a better candidate than other conditional heteroskedasticity models for modeling volatility in financial series. ARFIMA model also has a considerable capacity for modeling the return behavior of these time series. The daily data related to Tehran Stock Exchange (TSE) index was used for the purpose of this study. Considering the fact that the existence of conditional heteroskedasticity effects were confirmed in the stock return series, robust regression technique was used for estimation of different ARFIMA models. Furthermore, different GARCH-type models were also compared. The results of ARFIMA model are indicative of the absence of long memory in return series of the TSE index and the results from FIGARCH model show evidence of long memory in conditional variance of this series

Financial Time Series Forecasting by Developing a Hybrid Intelligent System

The design of models for time series forecasting has found a solid foundation on statistics and mathematics. On this basis, in recent years, using intelligence-based techniques for forecasting has proved to be extremely successful and also is an appropriate choice as approximators to model and forecast time series, but designing a neural network model which provides a desirable forecasting is the main concern of researchers. For this purpose, the present study tries to examine the capabilities of two sets of models, i.e., those based on artificial intelligence and regressive models. In addition, fractal markets hypothesis investigates in daily data of the Tehran Stock Exchange (TSE) index. Finally, in order to introduce a complete design of a neural network for modeling and forecasting of stock return series, the long memory feature and dynamic neural network model were combined. Our results showed that fractal markets hypothesis was confirmed in TSE; therefore, it can be concluded that the fractal structure exists in the return of the TSE series. The results further indicate that although dynamic artificial neural network model have a stronger performance compared to ARFIMA model, taking into consideration the inherent features of a market and combining it with neural network models can yield much better results