جستجوی مقالات مرتبط با کلیدواژه "quantile regression" در نشریات گروه "جغرافیا"

The objective of this research is to examine the bidirectional relationship between house prices and house rental prices in Iran from the first quarter of 2002 to the fourth quarter of 2019. Granger causality tests and the bootstrap approach of quantile regression, specifically employing the MCMB-A Method, are utilized for the investigation. The results of the Granger causality test indicate a two-way causal relationship between house prices and house rental prices. The bootstrap method of quantile regression reveals that, across all deciles, house rental prices exert a significant positive impact on house prices. Notably, the coefficient of house rental prices exhibits an increasing trend from the first to the sixth decile, reaching its maximum value in the sixth decile, and subsequently decreasing from the seventh to the ninth decile. Additionally, across all deciles, house prices demonstrate a significant positive impact on house rental prices. Throughout the deciles, the coefficient of house prices experiences a decreasing trend from the first to the fourth decile, reaching its lowest value in the fourth decile, followed by an increasing trend from the fifth decile to the ninth decile, reaching its maximum value in the ninth decile.

Temperature is one of the most important meteorological variables and any change in temperature variable causes changes in the occurrence of extreme phenomena such as drought, heavy rainfall, and storms that will cause irreparable damage in various social, economic, and agricultural sectors. Therefore, it is important to study the trend of these climatic variables in order to achieve methods for controlling and managing damages. Methods based on the mean or median of the data are generally used in studies related to trend investigation, since mean is a measure of central tendency, if studied alone may not provide information about trend variation in different parts of meteorological and hydrological data distribution, especially distribution tails. While extreme weather events often result from extreme values of climatic parameters. For this purpose, to study trend variation in the different data ranges, the quantile regression method was proposed, which has no limitations of previous parametric and nonparametric methods and has the ability to study trend variation and Show changes in different quantiles or different values of a climatic parameter. Therefore, the purpose of this study is to investigate the trend of temporal and spatial changes of minimum and maximum temperature on an annual scale using the quantile regression method in the geographical area of Iran.

The study area in the present study is the geographical area of Iran, which due to its location in the middle latitudes of 30 degrees, most of its area is covered by arid and semi-arid climates. In order to analyze a trend, maximum and minimum daily temperature data of 102 meteorological stations with a statistical period of 30 years (1988-2017) were obtained from the Meteorological Organization. After preparing the data, the annual time series was formed from the minimum and maximum temperature for this period of 30 years. Then the quantile regression method was used to analyze the trend variation in different quantiles of minimum and maximum temperature and the estimated slopes for the whole country were zoned using different interpolation methods in the GIS environment after that the Bayesian kriging interpolation method was selected for interpolation and the results were analyzed.

The results showed that the quantile regression method showed different trends for the minimum and maximum temperature variables in different quantiles and for different parts of Iran during the year. In general, both temperature variables had an increasing trend in all studied quantiles for all parts of Iran; Lower quantiles of the minimum temperature have an increasing trend in most parts of Iran and the most increasing trend slopes have been observed in the western half of the country, and about 63% of the area of Iran had a positive slope of 5-10%. While in the median quantile, the trend variation is more severe and all regions of Iran have a significant increasing trend that has been significant in most regions. in general, about 73% of the regions have a slope of 5-10%, which is visible in the western half, northeast, and southeastern parts and about 24% of the areas have a slope of 10-15% which is seen in eastern Iran. However, upper quantiles of minimum temperature that indicate high-temperature values also have a positive and significant trend in most parts of Iran, which in general 69% of the regions have a trend slope of 2-5%, which is located in the eastern half, north and south of the country, while 29% of Iran's area has a slope of 5-10%, which is mainly located in the western half and parts of the east and center of the country. However, in the study of the lower quantiles of the maximum temperature, the trend variation was more than the minimum temperature and there were significant increasing trends in most parts of Iran that 47% of the area had a slope of 2-5% which is located in the eastern half of Iran, and also 43% and 10% of the area of Iran had a slope of 5-10 and 10-15 %, respectively, which were observed in the western half of the country, but the number of increasing slopes was higher in the west. The median quantiles of the maximum temperature have a slope of 5-10% in 73% of the area, and 24% of the areas have a slope of 10-15%, which was significant in all cases. However, for the upper quantiles of the maximum temperature, trend variation was not significant, so that 64% of the area had a slope of 2-5% in the southern half and 36% of the areas had a slope of 5-10% in the northern half of Iran.

The most increasing trends for low values of minimum temperature were in the western half, median values in the eastern half, and high values in the western half, east and central part of Iran. In contrast, the highest upward trends for low values of maximum temperature are obtained in the northwest, median values in the eastern, western, and central half, and high values in the northern half of Iran. trend slopes for both minimum and maximum temperature have been higher in the median quantile and in general, it can be inferred that the temperature in Iran has increased due to climate change and the quantile regression method is useful to study and control very high and very low temperatures that are more important than the average temperature in climate risk studies.

The most significant factor that results in uncertainty about future water resource scarcity is climate change. Due to global warming, concerns about increasing or decreasing precipitation exist, which complicates water resource planning and management. Therefore, studying the trend of precipitation is of great importance. The linear trend reported in climate assessments reflects changes in average annual precipitation. However, the average trend cannot capture variations in other distribution aspects, including extreme precipitation events (high or low precipitation amounts). In this study, the Quantile Regression (QR) method was used to determine the trend of heavy precipitation (rainfall exceeding the 98th percentile) for seasonal and annual periods at 44 synoptic stations in Iran for two recent climatological standard normal periods: 1981-2010 and 1991-2020. For this purpose, after data quality control and homogenization, the 98th percentiles of seasonal and annual precipitation were calculated for both periods and compared. Then, the trends of these percentiles were estimated using the Quantile Regression method and tested. The results showed that compared to the two climatological standard normal periods, spring heavy precipitation had changed significantly in the southern of Alborz Mountain, summer heavy precipitation on the shores of the Caspian Sea, autumn heavy precipitation in the northwest and northeast regions of Iran, and winter heavy rainfall in the Zagros range. Similarly, spring-heavy precipitation in the southern of Alborz Mountain is decreasing, while summer-heavy precipitation on the shores of the Caspian Sea and autumn-heavy precipitation in the northwest and northeast regions of Iran are increasing.

Dew point temperature is one of the most important atmospheric variables and its long-term changes can lead to changes in the climatic characteristics of an area. In order to spatially analyze the trend of dew point temperature changes in Iran, the annual and monthly average data of this variable in 109 synoptic meteorological stations during the statistical period of 1990-2022 have been used. In order to investigate the effect of changes in other atmospheric variables on dew point temperature, the data of average annual relative humidity, average minimum and average maximum temperature, and average total annual precipitation have been used. In order to investigate the change process using the Mann-Kendall method and to identify the patterns governing the spatial changes of the dew point temperature in Iran, the spatial autocorrelation models of Global Moran and Getis-Ord General G statistic have been used. The results of investigating the trend of dew point temperature changes show that this variable has an increasing trend in the areas around the north and South Seas and the northwest region, and a decreasing trend in the central, eastern and northeastern regions of Iran. Spatial analysis of areas with high dew point temperature shows a severe cluster pattern, which means that areas with high dew point temperature are mainly spread in parts of the coasts of the Caspian Sea and many parts of the coasts of the Persian Gulf and Oman Sea, parts of northern Azerbaijan and Ardabil. This shows that the temperature of the dew point increases as it approaches moisture sources. Also, the spatial analysis of the areas with low dew point temperature also shows that these types of temperatures are generally concentrated in a large part of the central areas of the south of Khorasan province and the east and southeast of Iran.

Housing is one of the fundamental human needs. Accordingly to Maslov’s hierarchy of needs, housing as a physiological and basic need, set the stage for other needs for safety and survival, social relation, self-esteem and self-actualizution. Fulfilling a wide range of needs with housing, means that housing market can influence the behavior of individuals as well as the entire society. Given that, the housing market is linked to the entire economy of a country, it is therefore considered a very important market. On the other hand, various studies show that the exchange rate is a key variable that failure to pay attention to proper management can create problems for the economy of any country in various dimensions. So that Iran’s economy is no exception. Accordingly, the objective of this research is studing the effect of exchange rate volatility on house price in Iran from first quarter of 2006 to fourth quarter of 2019. The method of collecting research data is the library research method and to study the research topic, first the exchange rate volatility is estimated using the exponential GARCH Model and then the effect of exchange rate volatility on house price is investigated using the quantile regression method. The result indicate that, exchange rate volatility has a significant negative impact on house price and interaction effect of exchange rate volatility and inflation on house price is significant positive.

Climate change is a condition that refers to any change in climate that occurs over time. These changes may not be seen in the average of the data series but occur in quantiles of the series with different intensities that can be examined by quantile regression. In this research, to investigate Spatio-temporal changes of maximum and minimum temperature in different seasons in Iran, after fitting quantile regression on 102 meteorological stations with the statistical period of 2016-1986, the slope of the trend was calculated in the different quantiles, and by comparing its results with the results of the ordinary linear regression, they were spatially zoned. The results showed that in the spring season, the highest slope of the increasing trend is in the lower quantiles and the eastern half of Iran, but in the winter season, it was in the middle and upper quantiles and the northwest and west of Iran. In the summer season, the lower quantiles had an increasing trend, but in the autumn season, they had a decreasing trend. However, in spring and summer, the lower quantiles of daily temperature and in autumn and winter, the upper quantiles increased more strongly. Also, the comparison of the two regression methods shows that most of the quantiles had a slope different from the slope of the least square regression, and it’s not correct to assign the slope of the ordinary square regression line for the trend of temperature changes in the whole series. Finally, it can be stated that climate change has occurred in the maximum and minimum daily temperatures in Iran, but the intensity of these changes varies according to different seasons, quantiles, and locations.

The major aim of this research is to analyze the relationship between regional sustainable development and environmental, demographic-human and socio-economic structures. To do this, the composite development index is initially created based upon Principal Component Analysis (PCA) and GIS methods. According to these methods, the significant effective factors on the rural development are identified including their weights. It is worth saying that the sample size of data ,  equals  , in which N stands the number of villages (289) and K indicates the number of variables (25) based on latest census in 2016. This phase of assessment leaded to rural groups ranking.At the second stage, using econometric techniques (such as quantile regression), we have investigated the most important factors affecting development index as the dependent variable. According to the results, the regional development index has been affected negatively and meaningfully by natural variables such as slop and natural structures, and positively been affected by socio-economic and demographic determinants such as improving non-governmental organizations and local management. These results indicate the necessity to apply a specific development pattern for other similar areas in Iran.JEL Classification: R1, R2,R14.

Climate change and global warming are some of the issues and concerns of human beings today that have important effects on rain, evaporation, runoff, and finally water supply and causes the severity and weakness of these parameters, increasing the occurrence of severe weather events and lack of available water, which causes irreparable damage. Evapotranspiration is an important component in the hydrological cycle that is affected by various factors such as air temperature, relative humidity, wind speed and number of sunshine hours, and these factors are also affected by climate change. Due to the occurrence of climate change in the country in recent decades, it is important to study the changes in the trend of climatic parameters and their role in evapotranspiration in order to apply management methods to reduce evaporation in water resources. The Mann-Kendall trend test is an often-used method to examine changes in the data time series, but this test only expresses changes in the center of the data series, so Quantile and Bayesian multiple regression methods are used to study the trend of changes in different parts of the data time series and also to investigate the role of various parameters on a specific parameter. Therefore, the purpose of the present research is to study and compare the trend of changes in evaporation and climatic parameters affecting it and determining the role of these factors on evaporation using Quantile and Bayesian multiple regression methods at Hashemabad Gorgan station located in Golestan province.

In the first step, the meteorological data time series of evaporation, and the factors affecting it including average temperature, relative humidity, sunshine hour and wind speed were prepared for Hashemabad Gorgan synoptic station with a statistical period of more than 30 years (1984-2018) and the seasonal data series of these data were formed. The non-parametric Mann-Kendall test was performed to investigate the trend of changes in evapotranspiration and the factors affecting it and then Quantile and Bayesian regression was used to investigate the changes in various quantiles of data series and also to determine the role of changes in different values of each of these meteorological factors on evaporation.

investigation of the trend in the evaporation data series and the factors affecting it based on the Man-Kendall test shows that the aforementioned trend test shows a decreasing trend in evaporation only for winter, while the lowest changes in trend in different quantiles are in this season, and in Summer the increasing trend in evaporation has the highest slope in the range of 0.66-1.14% for lower and upper quantiles, respectively. In spring, the increasing slope of 0.75 in the upper quantiles gradually changes to a decreasing slope of -0.72. In autumn, the lower quantiles have a higher slope up to -0.66 and in winter, the quantiles have the lowest slope between -0.14 to -0.08. The results of investigating the factors affecting evaporation also show that changes in temperature have a greater share in the changes of daily evaporation and the bigger the quantile, the increasing slope increases. The relationship between relative humidity and evaporation follows a decreasing slope and ranks second after temperature. Finally, it can be concluded that in Hashemabad station of Gorgan, the Mann-Kendall trend test could not detect significant changes that have occurred in the process of the evaporation trend, but these changes have been revealed the best by the Quantile regression method. In summer, which is the driest and hottest season of the year, the trend of increase in evaporation is more intense, and due to summer cultivation in this region, water consumption in agriculture has increased in recent years and with this trend will increase further in the future.

Given the fact that climate change may not have occurred only in the average value of a data series, but extreme events might have occurred in some parts of the series so it is necessary to study different parts of the data series using methods such as Quantile and Bayesian multiple regression and the results of this research emphasizes this matter. Also, due to the necessity of studying evapotranspiration in the application of management methods for water resources, useful and practical results can be brought out by studying the changes in different ranges of evapotranspiration, especially it’s extreme and high values as well as the effects of changes in different ranges of climatic parameters on evaporation.