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The main purpose of the research work is to assess quantitatively the impact of the components of human capital vector upon the level of unemployment in the labor market. So that the econometric investigation has been conducted in order to consider the impact of those components of the human capital vector from the state budget to education and health on the level of unemployment. The various methods of econometric modeling have been used during the study. Econometric testing of built-in regression models has shown that these kinds of models are far more adequate in real-time and can be used to foresee and define unemployment levels within the labor market. [ABSTRACT FROM AUTHOR]

Global economic has been decreasing in the recent years, manifested by the greater exchange rates volatility on international commodity market. This study attempts to analyze some prominent exchange rate forecasting models on Malaysian commodity trading: univariate ARIMA, ARCH and GARCH models in conjunction with stationarity test on residual diagnosis direct testing of heteroskedasticity. All forecasting models utilized the monthly data from 1990 to 2015. Given a total of 312 observations, the data used to forecast both short-term and long-term exchange rate. The forecasting power statistics suggested that the forecasting performance of ARIMA (1, 1, 1) model is more efficient than the ARCH (1) and GARCH (1, 1) models. For ex-post forecast, exchange rate was increased from RM 3.50 per USD in January 2015 to RM 4.47 per USD in December 2015 based on the baseline data. For short-term ex-ante forecast, the analysis results indicate a decrease in exchange rate on 2016 June (RM 4.27 per USD) as compared with 2015 December. A more appropriate forecasting method of exchange rate is vital to aid the decision-making process and planning on the sustainable commodities' production in the world economy. [ABSTRACT FROM AUTHOR]

The spatial and time dependencies among observations are demanded for space time modeling. In order to estimate unobserved sites in the near future, we may apply Kriging methods to the forecast results of observed sites. Here we considered two ways of forecasting, first is by using AR model and second is by using GSTAR model. We compare both ways and observe the goodness of GSTAR model, relative to the AR model, in spatial dependence observations through some numerical studies and case study. It is obtained that GSTAR model gives the better forecasting result than AR model, which always perform overestimate values. Since the Kriging interpolation is better for linear approximation then AR model will give better interpolated observations if the site has higher real observations than other sites. This result also confirms the stationarity of GSTAR model. [ABSTRACT FROM AUTHOR]

Continuous time series modelling has been an active research in the past few decades. However, time series data in terms of correlated counts appear in many situations such as the counts of rainy days and access downloading. Therefore, the study on count data has become popular in time series modelling recently. This article introduces a new mixture model, which is an univariate non-negative stationary time series model with binomial marginal distribution, arising from the combination of the well-known binomial thinning and Pegram's operators. A brief review of important properties will be carried out and the EM algorithm is applied in parameter estimation. A numerical study is presented to show the performance of the model. Finally, a potential real application will be presented to illustrate the advantage of the new mixture model. [ABSTRACT FROM AUTHOR]

Snow loads for structural design are calculated using the product of the ground snow load and several coefficients that transform the ground load to a roof load. The basis for the ground snow load is a statistical analysis of the annual extreme snow-water equivalents. This statistical analysis traditionally uses the lognormal distribution and is based on an initial analysis of 76 weather stations in the northeastern United States between 1952 and 1980. Since the original study by Ellingwood and Redfield, additional data have been collected, thus enabling a reanalysis of ground snow load data for 113 weather stations with at least 40 years of data. In reevaluating the data, the authors first conducted a statistical analysis for trends or nonstationarity at all stations. The ground snow load data were then evaluated using multiple extreme-value distributions at each station. An Anderson-Darling statistical goodness-of-fit test was applied to each analysis at each station, and the results were compiled to determine which extreme-value distribution is most appropriate for analyzing snow loads. Conclusions showed that 90 percent of the stations analyzed display no significant temporal trend in ground snow load over the four-decade period of record and confirm the findings of Ellingwood and Redfield that the lognormal distribution is a reasonable model for estimating ground snow load for structural design. [ABSTRACT FROM AUTHOR]

In this work the core objective is to apply discrete wavelet transform (DWT) functions namely Haar, Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets in non-stationary financial time series data from US stock market (DJIA30). The data consists of 2048 daily data of closing index starting from December 17, 2004 until October 23, 2012. From the unit root test the results show that the data is non stationary in the level. In order to study the stationarity of a time series, the autocorrelation function (ACF) is used. Results indicate that, Haar function is the lowest function to obtain noisy series as compared to Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets. In addition, the original data after decomposition by DWT is less noisy series than decomposition by DWT for return time series. [ABSTRACT FROM AUTHOR]

In this paper, we apply the Invers of Autocovariance Matrix (IAcM) to investigate the process stationarity of the Generalized STAR model. In particular, we consider the second order of Generalized STAR (GSTAR(2;λ1,λ2)) processes. For that purpose, we derive the use of IAcM for the first order of GSTAR (GSTAR(1;λ1)) processes briefly. We move forward to the second order by following two important steps i.e. investigate the relevance between the sum square of errors and IAcM, and formulate the elements of IAcM as the function of autoregressive parameters and spatial weights. We obtain a definite positive IAcM whose size is twice the IAcM's size of GSTAR(1;λ1). We acquire the GSTAR(2;λ1,λ2) process is stationary if all determinants of the IAcM's leading principal submatrices are positive. Furthermore we find that the stationary condition obtained from the IAcM is stronger than from the characteristic polynomial of parameter matrix. [ABSTRACT FROM AUTHOR]

This paper investigates the Eurozone current account sustainability by using unit root tests and cointegration techniques. The results indicate there is no cointegration between the credit and the debit transactions of the current account. In these circumstances the current account of the Euro Area could be considered as unsustainable, this being an aspect that has to be approached in projecting the future policies. [ABSTRACT FROM AUTHOR]

A nonlinear asymmetric GARCH(p, q) model with stochastic delay is considered. Sufficient conditions for stationarity are provided. [ABSTRACT FROM PUBLISHER]

Summary form only given. Nest building by termites is considered as an example to acquire insight into the nature of self-organizing information systems. The behavior of the insects as atomistic units is organized by the time evolution of relatively stationary attractors in the pheromone diffusion field arising from the insects' waste deposits. A change in the attractor layout induces an instability in the pheromone flow pattern, driving the system to a state of greater order, as instability begets self-organization. As a result, the nest building evolves through stages of random depositing, pillar construction, arch construction and dome construction. Nonstationarity is present in the form of time-dependent variations in the perceptual threshold of the individual insect. When the waste is deposited, the insect loses its pheromone affinity and ceases to be oriented by the diffusion field: the affinity returns with the accumulation of new waste. Consequently, sensitivity to the qualities of the field vary dynamically as a function of simple internal states. This overall insects-environment system of evolving global stationarities and local nonstationarities has been analyzed through the methods of nonlinear dynamics and simulated. Analysis and simulation suggest a number of potentially very general design principles for self-organizing information systems, among them the understanding that the attractors of such systems are emergent cross-scale symmetries [ABSTRACT FROM PUBLISHER]