Non Stationarity

This paper examined the demand for broad money and its long run stability in Ghana. Multivariate time series approach was used. Since all the variables are integrated of order one, Johansen's cointegration approach is used to establish that the variables are cointegrated. Hence, vector error correction model was used to find the determinants of broad money. Also, CUMSUM and CUMSUMSQ plots are used to check the long run stability of the demand function. It was established that nominal foreign interest rate and expected inflation were long run determinants of demand for money while real income and nominal exchange rate were short run determinants. Also, it was found that the long run broad money demand function was stable over the period under consideration. It was recommended that monetary policy authorities should continue to implement policies that will enhance macroeconomic stability (price stability) and facilitate economic growth. Introduction This study aims at investigatin...

An implicit assumption often made in research on the environmental determinants of health is that the relationships between environmental factors and their health effects are stable over space and time. This is the assumption of stationarity. The health impacts of environmental factors, however, may vary not only over space and time but also over the value ranges of the environmental factors under investigation. Few studies to date have examined how often the stationarity assumption is violated and when violated, to what extent findings might be misleading. Using selected studies as examples, this paper explores how the stationarity assumption can lead to misleading conclusions about health-environment relationships that may in turn have serious health consequences or policy implications. It encourages researchers to embrace nonstationarity and recognize its meaning because it helps direct our attention to the ignored factors or processes that may enhance our understanding of the phenomena under investigation.

Studies pertaining to the effects of economic growth on the environment generally focused on diverse relationships between carbon dioxide, economic growth and energy consumption. This paper contributes to the literature by determining the effects of the USA and China's emissions on several economies carbon dioxide discharges from 1960 to 2010. The analysis uses a cointegration procedure proposed by Saikkonen and Lütkepohl. The study further applies the Granger causality test to test for causal links. The results of the study demonstrate that the US Granger causes emissions of ten economies under investigation. Additionally, China Granger causes 14 economies carbon dioxide discharges. In essence, the USA and China are tasked with the duty of accelerating programs attempting to reduce global carbon dioxide emissions due to their influential standpoint.

Empirical Mode Decomposition is an adaptive and local tool that extracts underlying analytical components of a non-linear and non-stationary process, in turn, is the basis of Hilbert Huang transform, however, there are problems such as interfering modes or ensuring the orthogonality of decomposition. Three variants of the algorithm are evaluated, with different experimental parameters and on a set of 10 time series obtained from surface electromyography. Experimental results show that obtaining low error in reconstruction with the analytical signals obtained from a process is not a valid characteristic to ensure that the purpose of decomposition has been fulfilled (physical significance and no interference between modes), in addition, freedom must be generated in the iterative processes of decomposition so that it has consistency and does not generate biased information. This project was developed within the framework of the research group DIGITI of the Universidad Distrital Francisco José de Caldas. KEYWORDS EMD, EEMD, CEEMDAN, mix of modes, non-linearity and non-stationarity.

Detecting trends in flow and precipitation series has great importance for the management of water resources and can be accomplished using statistic non-parametric tests.
This work evaluated the significance of trends in flow and temporal series of precipitation as well as monthly and annual precipitation trends using Pettitt’s test. The study used historical 
series of flow and precipitation corresponding to the period of 1939 to 2005, recorded at stations 02041003 and 57450000 of the hydro-meteorological network of Water National Agency (ANA), located in the municipality of Alegre-ES.

! This paper proposes, describes and diagrams a methodology for planning, designing, constructing and operating civil infrastructure projects for an operating environment that is changing substantially and in ways that are not readily predictable. Its purpose is to offer a timely answer to the engineer's follow-on question, " How do I deliver a safe, reliable and cost-effective infrastructure project in the face of these significant changes? " An extension of existing civil infrastructure project delivery practices, this methodology addresses these changing conditions by incorporating appropriate levels of robustness, resilience, redundancy and adaptability into the project design. Global climate change, the result of the burning of fossil fuels, is altering significantly the statistical properties of the environmental design parameters engineers use in infrastructure design. Consequently, long-held design assumptions such as ambient temperatures, sea levels, storm intensity, and the likely extent of droughts and heat waves are no longer reliable. Unknowingly, today's engineers are planning, designing and constructing infrastructure projects that will not be able to cope with future operating conditions. Thus, it is critical that the engineering profession devise a way of delivering projects that accounts for these new and significantly changing environmental conditions. Adopting this or some modification to this methodology is essential if civil infrastructure projects are to function as specified, and be protective of public health, safety and well-being.

Precipitation is intrinsically associated with high uncertainty, which is exacerbated exponentially over time—especially concerning climate change. However, the current design practice in urban drainage infrastructure remains firmly bound to deterministic assumptions regarding the design load. This approach is too simplified—focusing only on the return period of the design event—and ignores the complexity of drainage systems and the potential changes in catchment hydrology and the at-risk valuable assets within. Therefore, the current design approach is inherently an unsustainable practice that cannot deal with extreme uncertainties associated with urban drainage and flood resilience in changing climate and society. This paper examines the current deterministic design practice and encourages a collective discussion on the need for a paradigm shift in the engineering of pluvial floods toward a risk-based design. We believe that adopting a risk-based design will partially address the ...

Climate change is expected to significantly affect flooding regimes of river systems in the future. For Western Europe, flood risk assessments generally assume an increase in extreme events and flood risk, and as a result major investments are planned to reduce their impacts. However, flood risk assessments for the present day and the near future suffer from uncertainty, coming from short measurements series, limited precision of input data, arbitrary choices for particular statistical and modelling approaches, and climatic non-stationarities. This study demonstrates how historical and sedimentary information can extend data records, adds important information on extremes, and generally improves flood risk assessments. The collection of specific data on the occurrence and magnitude of extremes and the natural variability of the floods is shown to be of paramount importance to reduce uncertainty in our understanding of flooding regime changes in a changing climate. For the Lower Rhin...

The infrastructure design is primarily based on rainfall intensity–duration–frequency (IDF) curves, and the existing IDF curves are based on the concept of stationary extreme value theory (EVT) (i.e. the occurrence probability of extreme precipitation is not expected to change significantly over time). But, the extreme precipitation events are increasing due to global climate change and questioning the reliability of our current infrastructure design. Based on recent developments in the EVT, recent studies proposed a method for developing non-stationary rainfall IDF curve by incorporating linear trend in the location parameter of the generalized extreme value (GEV) distribution. Upon detecting a significant trend in the extreme rainfall series, directly applying the linear trend to develop non-stationary IDF curves may increase the bias of the non-stationary model. Hence, it is important to develop non-stationary GEV model which has less bias than the stationary model by modelling nonlinear trend in the series. In this study, we try to develop non-stationary GEV models with less bias by modelling nonlinear trend in the series using multi-objective genetic algorithm (MOGA). In addition, the proposed GEV model is compared with the stationary GEV model and the linear trend-based non-stationary GEV model. Furthermore, the Wilmington city and the Hyderabad city non-stationary IDF curves are developed and compared with stationary IDF curves. From the study results, it is observed that the proposed MOGA-based method is able to build the good quality and less bias non-stationary GEV models by modelling nonlinear trend in the series. In addition, it is also observed that the usage of linear trend for modelling non-stationarity in the time series sometimes increase the bias of non-stationary model.