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Copyright of Journal of Economics & Finance (2437-0630) is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to explain the role of the financial policy in achieving economic stability, through this paper the method of Auto Regressive Distributed Lags models ARDL(p,q1,q2) was applied to build an econometric model for the impact of public expenditure on economic growth in Algeria. The results of the empirical study on the case of Algeria showed that public expenditure contribute to accelerate the rate of economic growth, and that it has a negative impact in the short and long term. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Financial, Accounting & Managerial Studies is the property of Journal of Financial, Accounting & Managerial Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Economics & Finance (2437-0630) is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revue Valaque d'Etudes Economiques is the property of Valahia University of Targoviste and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In scientific vocabulary, the term “process” is used to denote change in time. Even a stationary process describes a system changing in time, rather than a static one that keeps a constant state all the time. However, this is often missed, which has led to misuse of the term “nonstationarity” as a synonym of “change”. A simple rule to avoid such misuse is to answer the question: can the change be predicted in deterministic terms? Only if the answer is positive is it legitimate to invoke nonstationarity. In addition, we should have in mind that models are made to simulate the future rather than to describe the past; the past is characterized by observations (data). Usually future changes are not deterministically predictable and thus the models should, on the one hand, be stationary and, on the other hand, describe in stochastic terms the full variability, originating from all agents of change. Even if the past evolution of the process of interest contains changes explainable in deterministic terms (e.g. urbanization), it is better to describe the future conditions in stationary terms, after “stationarizing” the past observations, i.e. adapting them to represent the future conditions. [ABSTRACT FROM AUTHOR]

Heavy rainfall events often occur in southern French Mediterranean regions during the autumn, leading to catastrophic flood events. A non-stationary peaks-over-threshold (POT) model with climatic covariates for these heavy rainfall events is developed herein. A regional sample of events exceeding the threshold of 100 mm/d is built using daily precipitation data recorded at 44 stations over the period 1958–2008. The POT model combines a Poisson distribution for the occurrence and a generalized Pareto distribution for the magnitude of the heavy rainfall events. The selected covariates are the seasonal occurrence of southern circulation patterns for the Poisson distribution parameter, and monthly air temperature for the generalized Pareto distribution scale parameter. According to the deviance test, the non-stationary model provides a better fit to the data than a classical stationary model. Such a model incorporating climatic covariates instead of time allows one to re-evaluate the risk of extreme precipitation on a monthly and seasonal basis, and can also be used with climate model outputs to produce future scenarios. Existing scenarios of the future changes projected for the covariates included in the model are tested to evaluate the possible future changes on extreme precipitation quantiles in the study area. Editor Z.W. Kundzewicz; Associate editor K. Hamed Citation Tramblay, Y., Neppel, L., Carreau, J., and Najib, K., 2013. Non-stationary frequency analysis of heavy rainfall events in southern France. Hydrological Sciences Journal, 58 (2), 280–294. [ABSTRACT FROM AUTHOR]

During recent decades, intensive research has focused on techniques capable of generating rainfall time series at a fine time scale that are (fully or partially) consistent with a given series at a coarser time scale. Here we theoretically investigate the consequences on the ensemble statistical behaviour caused by the structure of a simple and widely-used approach of stochastic downscaling for rainfall time series, the discrete Multiplicative Random Cascade. We show that synthetic rainfall time series generated by these cascade models correspond to a stochastic process which is non-stationary, because its temporal autocorrelation structure depends on the position in time in an undesirable manner. Then, we propose and theoretically analyse an alternative downscaling approach based on the Hurst-Kolmogorov process, which is equally simple but is stationary. Finally, we provide Monte Carlo experiments which validate our theoretical results. Editor Z.W. Kundzewicz Citation Lombardo, F., Volpi, E., and Koutsoyiannis, D., 2012. Rainfall downscaling in time: theoretical and empirical comparison between multifractal and Hurst-Kolmogorov discrete random cascades. Hydrological Sciences Journal, 57 (6), 1052–1066. [ABSTRACT FROM AUTHOR]

Rome has been plagued by flooding since its foundation, and, in December 2008, the largest flood event over the past 20 years caused a fatality and more than €150 million in economic damage. Meteorological conditions associated with the December 2008 flooding are shown to be typical of flooding in the Tiber. The long record of discharge measurements of the Tiber River at the Ripetta station in downtown Rome was used to examine flood frequency for the Tiber, including assessment of the return interval of the December 2008 flood. Particular attention is given to examination of the stationarity assumption for flood peaks through change-point and trend analyses, quantile regression, and statistical modelling of the flood-peak distribution. Once anthropogenic changes linked to reservoir regulation of the Tiber River have been accounted for, the stationarity assumption holds and can be used for flood frequency analysis. We highlight the difficulties in detecting departures from the stationarity assumption due to climate change. In the current regime, the December 2008 flood event has a return period of the order of 10–20 years. Citation Villarini, G., Smith, J.A., Napolitano, F. & Baeck, M.L. (2011) Hydrometeorological analyses of the December 2008 flood in Rome. Hydrol. Sci. J. 56(7), 1150–1165. [ABSTRACT FROM AUTHOR]

Abstract: The present article reports studies to develop a univariate model to forecast the summer monsoon (June–August) rainfall over India. Based on the data pertaining to the period 1871–1999, the trend and stationarity within the time series have been investigated. After revealing the randomness and non-stationarity within the time series, the autoregressive integrated moving average (ARIMA) models have been attempted and the ARIMA(0,1,1) has been identified as a suitable representative model. Consequently, an autoregressive neural network (ARNN) model has been attempted and the neural network has been trained as a multilayer perceptron with the extensive variable selection procedure. Sigmoid non-linearity has been used while training the network. Finally, a three-three-one architecture of the ARNN model has been obtained and after thorough statistical analysis the supremacy of ARNN has been established over ARIMA(0,1,1). The usefulness of ARIMA(0,1,1) has also been described. [Copyright &y& Elsevier]