Non-stationarity Tests Research Articles

A spatio-temporal model for temporal evolution of spatial extremal dependence

Few spatio-temporal models allow temporal non-stationarity. When modeling environmental data recorded over the last decades of the 20th century until now, it seems not reasonable to assume temporal stationarity, since it would not capture climate change effects. In this paper, we propose a space–time max-stable model for modeling some temporal non-stationarity of the spatial extremal dependence. Our model consists of a mixture of max-stable spatial processes, with a rate of mixing depending on time. We use maximum composite likelihood for estimation, model selection, and a non-stationarity test. The assessment of its performance is done through wide simulation experiments. The proposed model is used to investigate how the rainfall in the south of France evolves with time. The results demonstrate that the spatial extremal dependence is significantly non-stationary over time, with a decrease in the strength of dependence.

Spatial non-stationarity test of regression relationships in the multiscale geographically weighted regression model

By allowing covariate-specific bandwidths for estimating spatially varying coefficients, the multiscale geographically weighted regression (MGWR) model can simultaneously explore spatial non-stationarity and multiple operational scales of the corresponding geographical processes. Treating the constant coefficients as an extreme situation which corresponds to the global scale and infinite covariate bandwidth, the traditional linear regression, GWR and mixed GWR models are special cases of the MGWR model. An appropriately-specified GWR-based model would be beneficial to the understanding of the general underlying processes, especially for their operational scales. To specify an appropriate model, the key issue is to determine how many MGWR coefficient(s) should be constant. Along the traditional statistical line of thought, we propose a residual-based bootstrap method to test spatial non-stationarity of the MGWR coefficients, which can underpin our understanding of the characteristics of regression relationships in statistics. The simulation experiment validates the proposed test, and demonstrates that it is of valid Type I error and satisfactory power, and is robust to different types of model error distributions. The applicability of the proposed test is demonstrated in a real-world case study on the Shanghai housing prices.

Nonstationary Changes in Annual Rainfall over Indonesia’s Maritime Continent

The investigation into changing rainfall patterns in the Indonesian Maritime Continent (IMC) involved testing for trends, step changes, and variance nonstationarity using the Mann–Kendall, Pettitt, and White tests, respectively. The analysis covered data from 106 meteorological stations over the period from 1981 to 2021, with all tests conducted at annual time scales to understand the evolving precipitation dynamics in the region. According to the findings of this study, the average annual rainfall in IMC is nonstationary. Rainfall has increased dramatically by 12.72 mm with a significant shift point in 1994. However, this characteristic is likely to vary if the analysis period is extended, reduced, or conducted in different time frames. The spatial analysis indicates that 35.8% of meteorological stations observed a notable increase in rainfall, while 28.3% experienced a significant shift, and 16% displayed considerable variation. Consequently, the study only partially identified the nonstationary nature of rainfall in the IMC. Moreover, the research highlights a substantial rise in rainfall in the central to eastern IMC region, whereas the central to western region predominantly shows a decrease. This nonstationary test helps avoid errors in climatic conditions and analytical methods, presenting recommendations for hydrological projects. Consequently, employing this nonstationary test helps prevent inaccuracies in understanding climatic conditions and analytical techniques, thereby offering valuable suggestions for hydrological projects.

On the response of daily precipitation extremes to local mean temperature in the Yangtze River basin

This study explores the response of daily extreme precipitation (PEX) to local mean temperatures (T) of the previous 0-day (T0), 1-day (T1), and 1 to 9-days (T1–9) in the Yangtze River basin (YRB) using the non-stationarity test, binning method and copula theory. Results show that the annual scaling rate of PEX with T during 1961–2020 exhibits positive, negative and positive changing trends from southwest to northeast with the maximum magnitudes in the west, and experiences significant abrupt change only at 14.1% grids. The PEX-T scaling relation in the entire study period is predominantly characterized by a peak pattern over the YRB, with precipitation peaking at about 8 °C in the western highlands and at 25 °C in the east-central lowlands. However, the use of T1–9 yields a positive PEX-T pattern at more grids compared to T1 and T0, which highlights the important role of atmospheric moisture residence time in minimizing cooling artefacts in the scaling estimation. Generally, the full scaling rate of PEX with T in the study period is larger in the west-central part but smaller in the eastern YRB, and more sensitive to antecedent temperatures, with the ranges of −8–20%/°C, −8–30%/°C and −10–30%/°C against T0, T1 and T1–9, respectively. As precipitation intensifies, the full scaling decreases in plateau-mountain areas but increases in low-lying regions, gradually approaching the Clausius-Clapeyron (CC) scaling and exhibiting a “super-CC, CC-like and sub-CC scaling” pattern from west to east. Additionally, heavier precipitation events are most likely to occur at higher local temperatures in most areas even with peak structure. These findings provide new insights into the PEX-T relationships, which is conducive to understanding future changes in precipitation extremes and assessing precipitation-related risks under climate change.

Injury severity of police officers involved in traffic crashes: A spatial analysis of Alabama

Traffic crashes are the leading cause of injuries and deaths for police officers in the US. There has been limited research to investigate the characteristics of traffic crashes that involve police officers. This study aims to investigate the relationships between different independent variables and police officer injury severity in traffic crashes. Considering the inherent spatial correlation between traffic crashes and the geographical environment, this study employs the ordered logit regression and the Geographically Weighted Ordered Logistic Regression (GWOLR) approach to identify the contributing factors of police officers’ injuries in traffic crashes and to quantify the correlates of police injury severity in crashes. This study explored traffic crashes of five years (Number of observations = 4,231) that involved police officers in the State of Alabama. The results of the global ordered logit model illustrated that some independent variables, such as pre-crash behaviors, holiday, and road type, are statistically significantly related to police officer crash injury severity. Also, some variables, such as pre-crash behaviors and seatbelt usage, passed the non-stationarity tests. This suggests significant spatial variations in their relationships with the severity of injuries sustained by police officers in traffic crashes. The local correlates of police injury severity are valuable information for law enforcement agencies to localize strategies for improving officer safety on the road. For example, Move-over laws may need to be enhanced in the southeast Alabama area, where failing to yield (to police officers) seems to lead to increased traffic injury severities of police officers compared to other areas in Alabama.

Comparative Analysis of Usage Patterns and Underlying Determinants for Ride-hailing and Traditional Taxi Services: A Chicago Case Study

As app-based ride-hailing (or e-hailing) services have achieved great success worldwide, the traditional taxi industry faces an unprecedented crisis. To gain a deeper understanding of these two distinct modes that both offer door-to-door mobility services, in this study, we use Chicago as a case to comparatively analyze the travel patterns of the app-based ride-hailing and traditional taxi services and understand how socio-demographic and urban land use attributes affect the usage of two services in temporal and spatial dimensions. We employ the local regression models named geographically and temporally weighted regression (GTWR) models that can capture the spatiotemporal nonstationarity between variables as the primary analysis tool. Four GTWR models that predict the average hourly taxi or ride-hailing trip volume on weekdays or weekends for each community area are carefully constructed with R^2 all above 0.98. The nonstationarity test shows all variables exhibit extra local variations, indicating the necessity of using the GTWR models to explore the attributes’ spatiotemporal impacts. Spatial error models (SEM) and ordinary least square (OLS) models, global regression models, are also built as comparisons and benchmark references. The differences between global and local regression models are illustrated through clustering analysis. To compare and contrast the usage patterns of ride-hailing and taxi services, we analyze the temporal-spatially varying coefficients of GTWR models. Interpretations are provided for our interesting findings, including the strong trip generation power of the population in the South Side area, the attitude of white people towards taxi services is positive but ambiguous towards ride-hailing services, young people's opposing behaviors towards taxi services on weekdays and weekends, and the opposite relationship between transit and taxi or ride-hailing in certain areas. Based on these key findings, we offer some planning and operational suggestions, such as developing mobile apps, implementing price-related strategies, strengthening partnerships with local businesses, building multi-modal transportation, clarifying customer segmentation, providing auxiliary services, etc.

Determining the spatial non-stationarity underlying social and natural environment in thyroid cancer in China

BackgroundLight at night (LAN) is a physiological environmental factor related to thyroid cancer (TC). The spatial relationship between the number of TC incident cases, LAN, air pollution and other macro social factors and stationarity needs to be further examined to provide evidence for regional control of TC. MethodsSpatial econometrics methods for spatial nonstationarity were used to explore the impacts of LAN, air pollutants, economic factors, and population size on the number of TC incident cases in 182 Chinese prefecture-level cities and the local coefficients were further tested for nonstationarity. Temporally weighted regression (TWR), geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR) were compared in this study for model selection. ResultsBased on the ordinary least squares (OLS), LAN, air pollutants, and urbanization all significantly affected the number of TC incident cases. GWR had the best goodness of fit, and the coefficients of all the variables passed the nonstationarity test. The strong positive impact of LAN was mainly concentrated in North China, air pollutants in Central China and neighboring regions, and urbanization in the eastern coast of China. ConclusionsThe locational factors of the prefecture-level city influence the spatial pattern of the number of TC incident cases. Governments should pay attention to this influence, adhere to the Health in All Policies principle, and formulate region-specific policies based on regional characteristics, which this study provides updated evidence for.

Weak EMH and Canadian stock markets: evidence from linear and nonlinear unit root tests

PurposeThe purpose of this paper is to test the weak form of the efficient market hypothesis (EMH) using monthly data from 2004M08 to 2018M04 for two Canadian stock indices: the Islamic (DJICPI) and the conventional (CCSI). This paper investigates whether Islamic and/or conventional stock market would be efficient through the non-stationarity test of the stock indices.Design/methodology/approachThe authors conduct the linearity test of Harvey et al. (2008) to identify whether the considered series has linear or nonlinear behavior. If the time series exhibits nonlinear evolution, then the authors apply nonlinear unit root tests (three KSS type tests and Sollis tests).FindingsLinearity test results say that LCCSI has nonlinear behavior, while Dow Jones Islamic Canadian Price Index, LDJICPI, is a linear process. Then, the findings of this paper show that only Canadian Islamic Price Index (DJICPI) has the characteristics of random walk indicating that only conventional stock markets are inefficient. The major implication is that in Canada, fund managers and investors can (cannot) enjoy excess returns to their investment in conventional (Islamic) stock market.Originality/valueNumerous empirical studies of the weak EMH are carried out within a linear framework. However, stock indices can show nonlinear behavior as a result of 2008 global financial crisis. To contribute to the existing literature on the Islamic and conventional stock market efficiency, the authors take into account both structural breaks and nonlinearity. Thus, as a testing strategy for weak EMH, the authors perform (Harvey et al., 2008) linearity test to examine the presence of nonlinear behavior and correct for outliers effect when it is needed.

Characteristics of 2020 stock market crash: The COVID-19 induced extreme event.

A sudden fall of stock prices happens during a pandemic due to the panic sell-off by the investors. Such a sell-off may continue for more than a day, leading to a significant crash in the stock price or, more specifically, an extreme event (EE). In this paper, Hilbert-Huang transformation and a structural break analysis (SBA) have been applied to identify and characterize an EE in the stock market due to the COVID-19 pandemic. The Hilbert spectrum shows a maximum energy concentration at the time of an EE, and hence, it is useful to identify such an event. The EE's significant energy concentration is more than four times the standard deviation above the mean energy of the normal fluctuation of stock prices. A statistical significance test for the intrinsic mode functions is applied, and the test found that the signal is not noisy. The degree of nonstationarity test shows that the indices and stock prices are nonstationary. We identify the time of influence of the EE on the stock price by using SBA. Furthermore, we have identified the time scale ( τ) of the shock and recovery of the stock price during the EE using the intrinsic mode function obtained from the empirical mode decomposition technique. The quality stocks with V-shape recovery during the COVID-19 pandemic have definite τ of shock and recovery, whereas the stressed stocks with L-shape recovery have no definite τ. The identification of τ of shock and recovery during an EE will help investors to differentiate between quality and stressed stocks. These studies will help investors to make appropriate investment decisions.

FDI Driven Growth in Emerging Economies: Impact of Levels of Central Bank Independence

This paper investigates the impact of levels of central bank independence (cbi) on the relationship between foreign direct investment (fdi) and economic growth. Economic theory confirms that a higher level of central bank independence is associated with a relatively lower and stable rate of inflation. To the extent that inflation moderation contributes to macroeconomic stability, and the latter provides a favorable environment for fdi to impact growth significantly, we considered it important to examine the possible effect of cbi on the fdi-economic growth relationship. Our attempt in this direction fills an important gap in the literature by adding a new dimension to the fdi-growth nexus. We proceed by subjecting data on fdi flows and gross domestic product (gdp) to Granger causality tests within a panel cointegration framework. Our panel consists of 22 emerging countries subdivided into groups of high and low cbi countries. The empirical investigation in our framework proceeds in three different steps. We begin by testing for non-stationarity in the two variables of fdi flows and gdp in our panel of 22 countries. Prompted by the existence of unit roots in the time series of the referred variables, we use the panel cointegration technique to test for a long-run cointegrated relationship between the two variables of interest in the second step of our estimation. With evidence of cointegration in the long-run fdi-growth relationship for the full and the subdivided panels, we finally use an error correction model to uncover Granger causality in the long run relationship along with its short-run dynamics. The results of our tests indicate a strong cointegration between fdi and economic growth for both the subdivided country groups referred above; causal links between the two variables, however, vary substantially between the two groups. While the high cbi countries show a significant long run bidirectional causality between fdi and economic growth, the low cbi countries demonstrate only a unidirectional link running from economic growth to fdi; there is no evidence of a link running from fdi to economic growth for the low cbi countries. Additionally, we find that for both the country groups, fdi induced growth is a long run phenomenon only. These results seem to suggest that with associated price stability, high cbi countries serve not only as an attraction for fdi flows but, through productivity spillovers, triggers economic growth that induces additional fdi flows.

A spatiotemporal analysis of motorcyclist injury severity: Findings from 20 years of crash data from Pennsylvania

Motorcyclists face higher risks of severe injuries in crashes compared to motor vehicle drivers who are often protected by seatbelts and airbags during collisions. A report by the National Highway Traffic Safety Administration reveals that motorcyclists have 27 times the risk of fatality in traffic crashes as much as motor vehicle drivers. Previous studies have identified a list of risk factors associated with motorcyclist injury severity and generated valuable insights for countermeasures to protect motorcyclists in crashes. These studies have shown that wearing helmets and/or motorcycle-specific reflective clothing and boots, driving alcohol/drug-free, and obeying traffic regulations are good practices for safe motorcycling. However, these practices and other risk factors are likely to interact with local geographic, socio-economic, and cultural contexts, leading to diversified correlations with motorcyclist injury severity, which remains under-explored. Such correlations may exhibit variations across space and time. The objective of this study is to revisit the correlates of motorcyclist injury severity with a focus on the spatial and temporal variations of correlations between risk factors and injury severity. This study employed an integrated spatiotemporal analytical approach to mine comprehensive statewide 20 years’ motorcycle-involved traffic crashes (N = 50,823) in Pennsylvania. Non-stationarity tests were performed to examine the significance of variations in spatially and temporally local correlations. The results show that most factors, such as helmet, engine size, vehicle age, pillion passenger, at-fault striking, and speeding, hold significant non-stationary relationships with motorcyclist injury severity. Furthermore, cluster analysis of estimations reveals the regional similarities of correlates, which may help practitioners develop regional motorcyclist safety countermeasures.

The Seventh International Symposium on Naturalistic Driving Research

Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).

Comments on Assessment of the Trade Integration Pattern Between the Russian Federation and East/Southeast Asian Economies Using the Panel Gravity Framework

Comments on Assessment of the Trade Integration Pattern Between the Russian Federation and East/Southeast Asian Economies Using the Panel Gravity Framework

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).

The Impact analysis on non-Stationary Extreme Wind Speed Evolution to Wind Turbine

It is highly preferred to evaluate evolution of wind extremes over long time-scale with nonstationarity extreme distribution model. The annual extreme value methods, which single out the maximum wind of each year as sample data, are utilized. Thereafter, the annual wind extremes are test for nonstationarity. Finally, the maximum possible wind extreme is calculated with generalized extreme value distribution model to evaluate the influence of nonstationarity of wind extreme on safety of wind turbines. It is uncovered that the wind extreme in Hailisu, Guaizihu and Chengshantou are non-stationary. However, there is no maximum possible wind extreme and it is a positive impact on the safety of wind turbines. The wind extreme of Hengshan is stationary. The wind extremes of Huangshan is non-stationary and the maximum possible wind extreme over there is 41.416 m/s, which is notable larger than the design wind speed of 37.5m/s of wind turbines. It is of negative impact on the safety of wind turbines.

Modeling the dynamic Nexus among coal consumption, pollutant emissions and real income: empirical evidence from South Africa.

This study explores the interaction among coal consumption, pollutant emissions, and real income for South Africa in a multivariate setting. To achieve this objective, annual frequency data spanning from 1965 to 2017 is used for analysis. A series of econometrics tests were conducted ranging from stationarity and non-stationarity tests for unit root properties of the variables under consideration. Empirical evidence finds support for the inverted U-shaped pattern between energy consumption and environmental degradation in South Africa. The Toda-Yamamoto Granger causality test shows a feedback causality between economic growth and carbon dioxide emissions, as well as between GDP and coal consumption. All these highlighted findings have inherent environmental implications. Based on these outcomes, policy directions such as diversification of the South Africa energy mix to renewables and cleaner energy sources and also the adoption of carbon capturing and storage techniques were suggested to engender a cleaner and friendlier environment.

통화정책의 장기 유효성 분석: 실질이자율의 비정상 장기 기대 분해를 중심으로

본고는 Kim(2018)을 따라 우리나라의 통화정책이 실질이자율의 장기 기대 또는 예측(Long Run Expectation of Real Interest Rate: RELEX)에 영향을 미치고 이를 통해 다시 실물경제에 유효하게 영향을 미치는 메커니즘이 있는지를 검정․분석하고자 한다. 이를 위한 RELEX의 추정은 뉴케인지언 이론에 기초한 공적분 벡터 자기회귀(Co-integrated VAR) 모형 내에서 Beveridge-Nelson 추세 분해를 통해 수행된다. 한편, 실질이자율의 추세가 존재하기 위한 전제 조건인 비정상성 검정을 위해서는 Kim(2018)의 2단계 검정을 수행하며 비정상 실질이자율의 장기 기대를 인플레이션, 산출갭 및 이자율의 세 가지 추세로 분해한다. 외환위기 이후 월별 자료를 글로벌 금융위기(이하 위기)를 기점으로 구분하여 분석한 결과, 위기 전후 모두 실질이자율은 비정상이며, 통화정책 충격이 대부분인 이자율 추세는 위기 이후에만 존재하는 것으로 나타났다. 또한 RELEX 상승 충격에 산업생산지수는 초기 유의한 하락 반응을 보였으며, 특히 RELEX 중 이자율 추세 상승 충격이 산업생산지수 하락 반응을 주도하는 것으로 나타났다. 이러한 추정 결과는 우리나라의 통화정책이 실질이자율의 장기 기대에 영향을 미치고 이는 다시 실물경제에 영향을 미치는 메커니즘이 위기 이후 유효하게 작동하고 있음을 시사하는 것이다.

Exploring Spatially Non-stationary Relationships in the Determinants of Mineralization in 3D Geological Space

Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.

A road to enhancements in natural gas use in Iran: A multivariate modelling approach

The primary focus of this study is to empirically investigate the natural gas consumption-economic growth nexus in Iran, while incorporating real gross fixed capital formation (GFCF) and the role of oil revenue (OR) as additional variables to make it a multivariate framework in order to avoid possible omission variable bias in the estimations. To this end, quarterly frequency data from 1990Q1 to 2017Q4. Structural break point unit root test like Zivot and Andrews is employed and complemented with traditional non-stationarity tests such as Augmented Dickey Fuller and Phillips and Perron unit tests to investigate the interest variables stationarity characteristics. Recently developed Bayer and Hanck (2013) combined cointegration test is used alongside the Pesaran et al. (2001) bounds testing cointegration to test for long-run relationship among the variables. Finally, we test for causal relationships through Modified Wald test of Tada-Yamamoto (1995) Granger causality tests is employed. Empirical findings show cointegration relationship between the variables while accounting for structural break. Further piece of empirical results suggest that natural gas consumption exerts a significant positive impact on economic output in Iran, and also that there is a one-way causality from natural gas consumption to economic output. Thus, our study corroborates the natural gas-led growth hypothesis; being natural gas consumption a suitable alternative, as a complementary green energy source (IGU, 2015). One important conclusion reached in our study is that there is need for energy portfolio diversification in Iran in order to attain full gains from the energy sector, reducing other energies' emissions. Further insights are elucidated in the main text. Our findings provide policymakers useful insight into the state of the energy sector in Iran.

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes. The method leverages the space- and time-referenced crash data and powerful computational tools. This study performed non-stationarity tests to verify whether the local correlates of pedestrian injury severity have a significant spatio-temporal variation. Results showed that some variables passed the tests, indicating they have a significantly varying spatio-temporal relationship with the pedestrian injury severity. These factors include the pedestrian age, pedestrian position, crash location, motorist age and gender, driving under the influence (DUI), motor vehicle type and crash time in a day. The spatio-temporally varying correlates of pedestrian injury severity are valuable for researchers and practitioners to localize pedestrian safety improvement solutions in North Carolina. For example, in near future, special attention may be paid to DUI crashes in the city of Charlotte and Asheville, because in such areas DUI-involved crashes are even more likely to cause severe pedestrian injuries that in other areas. More implications are discussed in the paper.