Time series regression with persistent level shifts

Woody, Jonathan

doi:10.1016/j.spl.2015.03.011

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…The related test statistic can be given by [48,49]: Therefore, the hypothesis that the data are from all rainfall series with the null hypothesis is rejected, if Reject : H 0 if |z τ | > Z α/2 is the critical test-statistic value with d f = n − 1 and a significance level of α) and least squares regression test. The least squares test statistic can be given by [23]:…”

Section: Checking the Normality And Homogeneity Of Rainfalls Seriesmentioning

confidence: 99%

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Trend and Homogeneity Analysis of Precipitation in Iran

Javari

2016

Climate

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Abstract:The main objective of this study is to examine trend and homogeneity through the analysis of rainfall variability patterns in Iran. The study presents a review on the application of homogeneity and seasonal time series analysis methods for forecasting rainfall variations. Trend and homogeneity methods are applied in the time series analysis from collecting rainfall data to evaluating results in climate studies. For the homogeneity analysis of monthly, seasonal and annual rainfall, homogeneity tests were used in 140 stations in the 1975-2014 period. The homogeneity of the monthly and annual rainfall at each station was studied using the autocorrelation (ACF), and the von Neumann (VN) tests at a significance level of 0.05. In addition, the nature of the monthly and seasonal rainfall series in Iran was studied using the Kruskal-Wallis (KW) test, the Thumb test (TT), and the least squares regression (LSR) test at a significance level of 0.05. The present results indicate that the seasonal patterns of rainfall exhibit considerable diversity across Iran. Rainfall seasonality is generally less spatially coherent than temporal patterns in Iran. The seasonal variations of rainfall decreased significantly throughout eastern and central Iran, but they increased in the west and north of Iran during the studied interval. The present study comparisons among variations of patterns with the seasonal rainfall series reveal that the variability of rainfall can be predicted by the non-trended and trended patterns.

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Section: Checking the Normality And Homogeneity Of Rainfalls Seriesmentioning

confidence: 99%

“…Forecasting involves basic definitions and assumptions. In this study, we have used least squares method or regression method [49] to predict rainfall patterns. However, we used the following steps: (1) Estimating the seasonal index.…”

Section: Forecast Models For Rainfalls Seriesmentioning

confidence: 99%

Trend and Homogeneity Analysis of Precipitation in Iran

Javari

2016

Climate

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“…For the selected stations and rainfall data in the study area, the least square seasonality test was used to distinguish the temporal trends and seasonality of the seasonal rainfall series. The nonparametric Kruskal-Wallis test was utilized on the seasonal rainfall, which is a serial version of one-factor analysis rank statistic [40,43]. Therefore, the null hypothesis that the data are from all rainfall series is rejected if is the critical test-statistic value with DF=n-1 and a significance level of α.…”

Section: Measurement Methodsmentioning

confidence: 99%

Spatial-temporal Variability of Seasonal Precipitation in Iran

Javari¹

2016

TOASCJ

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Spatial-seasonal variability and temporal trends has essential importance to climatic prediction and analysis. The aim of this research is the seasonal variations and temporal trends in the Iran were predicted by using rainfall series. The exploratoryconfirmatory method, and seasonal time series procedure (STSP), temporal trend (TT), seasonal least squares (SLS) and spatial (GIS) methods (STSP¬-SLS-GIS) were employed to bring to light rainfall spatial-seasonal variability and temporal trends (SSVTT). To explore the spatial-seasonal variability and temporal trends during the period over 1975 to 2014 at 140 stations. To investigate the spatial-seasonal variability and temporal trends amount of each series was studied using ArcGIS 10.3 on different time scale. New climatic findings for the region: the investigates and predictions revealed that: (a) range of monthly and seasonal changes of rainfall tends to be highest (increasing trend) during winter (Winter Seasonal Index or WUSI=137.83 mm); (b) lowest (decreasing trend) during summer (Summer Seasonal Index or SUSI=20.8l mm) and (c) the coefficient of rainfall seasonal pattern variations in winter to 5.94 mm, in spring to 11.13 mm, in summer to 4.44 mm and in autumn to 8.05 mm with seasonality being the most effective of all. Mean annual rainfall changed from 51.45 mm (at Bafgh) to 1834.9 mm (at Bandar Anzali). Maximum decrease in annual rainfall was obtained at Miandeh Jiroft (-143.83%) and minimum at Abali (-0.013%) station. The most apparent year of variation was 2007 in annual rainfall.

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“…Regression analysis was applied to explore the spatial relationships and observed spatial patterns. According to Woody (2015), the spatial regression model can be calculated using the following equation [35]:…”

Section: Regression Analysismentioning

confidence: 99%

“…In addition, effectiveness models in SEM include the measurement model and the structural model. A measurement model measures the latent variables, whereas the structural model investigates all the path pattern relations [74]. However, the analyzed patterns of temperature elevation and DEM effectiveness to annual rainfall (SEM models) are displayed as the effectiveness final model in Figure 21.…”

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confidence: 99%

Assessment of Temperature and Elevation Controls on Spatial Variability of Rainfall in Iran

Javari

2017

Atmosphere

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Abstract:With rainfall changes, hydrological process variability increases. This study predicts the potential effects of temperature and topography characteristics on rainfall spatial variability. Temperature and topography were considered as two effective factors that may influence monthly rainfall. This study uses rainfall and temperature data from 174 synoptic and climatic stations and 39,055 rain, elevation and temperature points extracted by ArcGIS10.3 over the 40 years . In this study, in order to predict the relationship between temperature, topography and rainfall, a combination of statistics including spatial statistics and Geographical information System (GIS) methods were employed. It was found that the distribution and rainfall variability in some parts of Iran was regarded to be based on topography and temperature. The spatial patterns showed that the variability based on spatial autocorrelation in rainfall severity gradually increased from west to east and north to south in Iran. Temperature and topography influence rainfall spatial variability; moreover, these factors have direct, indirect and total effects on rainfall variability in temporal and spatial patterns. These research results will be useful for the regionalization of climate and rainfall formation factors, management of water sources, environmental planning and measuring environmental controls on the climate system.

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Time series regression with persistent level shifts

Cited by 5 publications

References 13 publications

Trend and Homogeneity Analysis of Precipitation in Iran

Trend and Homogeneity Analysis of Precipitation in Iran

Spatial-temporal Variability of Seasonal Precipitation in Iran

Assessment of Temperature and Elevation Controls on Spatial Variability of Rainfall in Iran

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