Backache is a common health problem associated with non ergonomic working environment. A cross sectional study amongst the rubber tappers was conducted to determine the prevalence of low back pain and ergonomic factors related to it. The respondents were 116 male rubber tappers from Felda scheme in the state of Pahang who were selected by random sampling. The result showed prevalence of low back pain was 60.3% and has significant association with history of backache (p<0.05). Rubber tappers were 6 times more higher to develop low back pain if they had history of backache (Odd Ratio =6.24, 95% CI). There were no significant association between socio-demographic factors and low back pain. Ergonomic factors such as bending during tapping, respondents’ height and duration of the tapping showed no significant association with low back pain. Further study should be done in ergonomic aspect to explore the problem.
Key words: Low back pain, rubber tapper, Felda, ergonomic.
The main aim of this paper was to validate the relative price monetary model (RPMM) of exchange rate determination for the Malaysian exchange rate (RM/USD) using monthly data set from 1986-2010. The Johansen multivariate cointegration test and vector error correction model were employed. Because the time period under consideration includes the South
East Asian financial crisis, the analysis is done using two time periods; the full time period as well as the period after the crisis. Two interesting results were observed from this empirical exercise. First, there is a long-run relationship between exchange rate and the selected macro variables only for the period after the crisis. Second, the forecasting performance of monetary approach based on the error correction model outperformed the Random Walk model.
The main purpose of this article is to introduce the technique of panel data analysis in econometrics modeling. The elasticity of labour, capital and economic of scale for twenty two food manufacturing firms covering from 1989 to 1993 is estimated using the Cobb-Douglas model. The three main techniques of panel data analysis discussed are least square dummy variables (LSDV), analysis of covariance (ANCOVA) and generalized least square (GLS). Ordinary Least Square (OLS) method is included as the basis of comparison.
The incidence of dengue in Malaysia has increased from 15.2 per 100,000 in 1973 to 361.0 per 100,000 population by the year 2014. The same trend was seen in case fatality rates that went up from 0.16% to 0.62% between 2000 to 2013.1,2 More than 60% of the cases were from the Klang Valley region. In Pahang the dengue incidence rates from 2004 to 2014 showed a similar increase from 40.9 to 193.8 per 100,000 population and an increase in case fatality rates from 0.07% to 0.3% too was observed between 2000-2014. Fortunately these figures were below the national average.3
This study proposes a simple methodology to estimate the power-law tail index of the Malaysian stock exchange by using the maximum likelihood Hill’s estimator. Recursive procedures base on empirical distribution tests are use to determine the threshold number of observations in the tail estimation. The threshold extreme values can be selected bases on the desired level of p-value in the goodness-of-fit tests. Finally, these procedures are apply to three indices in the Malaysian stock exchange.
In this research we introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. Use of KLIC is practically attractive as well as convenient, given its equivalent of the widely used LR test. We include an illustrative simulation to compare a set of distributions, including symmetric and asymmetric distribution, and a family of GARCH volatility models. Our results on simulated data show that the choice of the conditional distribution appears to be a more dominant factor in determining the adequacy and accuracy (quality) of density forecasts than the choice of volatility model.
This paper analyzes the asymmetric long memory volatility dependency of the interday prices of Composite Index (CI) at Bursa Malaysia by using GARCH family models. The GARCH type models are used with the assumption that the innovations series follow either one of the following distributions: Gaussian, Student -t and skewed Student -t. The stock returns' long memory dependency is determined using the Hurst parameter. The long memory and asymmetric volatility are modelled by fractionally integrated GARCH models. It is found that the asymmetric and long memory GARCH models with skewed student-t distribution give better predictive ability on the volatility of the Kuala Lumpur Composite Index (KLCI).
The accuracy of financial time series forecasts often rely on the model precision and the availability of actual observations for forecast evaluations. This study aimed to tackle these issues in order to obtain a suitable asymmetric time-varying volatility model that outperformed in the forecast evaluations based on interday and intraday data. The model precision was examined based on the most appropriate time-varying volatility representation under the autoregressive conditional heteroscedascity framework. For forecast precision, the evaluations were conducted under three loss functions using the volatility proxies and realized volatility. The empirical studies were implemented on two major financial markets and the estimated results are applied in quantifying their market risks. Empirical results indicated that Zakoian model provided the best in-sample forecasts whereas DGE on the other hand indicated better out-of-sample forecasts. For the type of volatility proxy selection, the implementation of intraday data in the latent volatility indicated significant improvement in all the time horizon forecasts.
This study investigates the value-at-risk (VaR) using nonlinear time-varying volatility (ARCH model) and extreme-value-theory (EVT) methodologies. Similar VaR estimation and prediction are observes under the EVT and heavy-tailed long-memory ARCH approaches. The empirical results evidence the EVT-based VaR are more accurate but only at higher quantiles. It is also found that EVT approach is able to provide a convenient framework for asymmetric properties in both the lower and upper tails which implies that the risk and reward are not equally likely for the short- and long-trading positions in Malaysian stock market.
Linear time series models are not able to capture the behaviour of many financial time series, as in the cases of exchange rates and stock market data. Some phenomena, such as volatility and structural breaks in time series data, cannot be modelled implicitly using linear time series models. Therefore, nonlinear time series models are typically designed to accommodate for such nonlinear features. In the present study, a nonlinearity test and a structural change test are used to detect the nonlinearity and the break date in three ASEAN currencies, namely the Indonesian Rupiah (IDR), the Malaysian Ringgit (MYR) and the Thai Baht (THB). The study finds that the null hypothesis of linearity is rejected and evidence of structural breaks exist in the exchange rates series. Therefore, the decision to use the self-exciting threshold autoregressive (SETAR) model in the present study is justified. The results showed that the SETAR model, as a regime switching model, can explain abrupt changes in a time series. To evaluate the prediction performance of SETAR model, an Autoregressive Integrated Moving Average (ARIMA) model used as a benchmark. In order to increase the accuracy of prediction, both models are combined with an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model. The prediction results showed that the construct model of SETAR-EGARCH performs better than that of the ARIMA model and the combined ARIMA and EGARCH model. The results indicated that nonlinear models give better fitting than linear models.
The main objective of this paper is to explore the varying volatility dynamic of inflation rate in Malaysia for the period from January 1980 to December 2004. The GARCH, GARCH-Mean, EGARCH and EGARCH-Mean models are used to capture the stochastic variation and asymmetries in the economic instruments. Results show that the EGARCH model gives better estimates of sub-periods volatility. Further analysis using Granger causality test show that there is sufficient empirical evidence that higher inflation rate level will result in higher future inflation uncertainty and higher level of inflation uncertainty will lead to lower future inflation rate.
This article study the influences of structural break to the fractionally integrated time-varying volatility model in Malaysian stock markets from year 1996 to 2006. A fractionally integrated autoregressive conditional heteroscedastic (FIGARCH) model combines with sudden changes of volatility is develops to study the possibility of structural change in Asian financial crisis and currency crisis. Our empirical results evidence substantially reduction in long memory clustering volatility after the inclusion of sudden changes in the volatility. Finally, the estimation, diagnostic and model selection evaluations indicate that the fractionally integrated model with structural change is out-performed compared to the standard model.
Expectation Maximization (EM) algorithm has experienced a significant increase in terms of usage in many fields of study. In this paper, the performance of the said algorithm in finding the Maximum Likelihood for the Gaussian Mixed Models (GMM), a probabilistic model normally used in fraud detection and recognizing a person’s voice in speech recognition field, is shown and discussed. At the end of the paper, some suggestions for future research works will also be given.
Peer-reviewed articles on the usage of mobile applications in the management of diabetes were reviewed. Studies using mobile device applications for diabetes interventions published between January 2007 and March 2013 were included in this review. Studies related to the developmental processes of the mobile applications were excluded. The characteristics of these studies and the outcomes of the methods used in the management of diabetes were gathered. We retrieved a total of 372 articles from ACM Digital library, PubMed and Proquest Medical Library. After applying the inclusion and exclusion criteria, 8 articles were eligible for further review. Most of the studies included in this review showed the effectiveness of mobile applications in diabetes management. Their major contribution comes from feedback functions of the systems that assist participants in the self-monitoring of their blood glucose and dietary intake. However, the majority of the studies showed the limited use of the mobile phone as a tool to input information into systems which would in turn convey a reminder message to the patient. In conclusion, with its features of portability and convenience, mobile application used in the management of diabetes has the potential to improve glycaemic control, resulting in significant clinical and financial benefits.
Poly(methyl methacrylate) (PMMA) is a lightweight insulating polymer that possesses good mechanical stability. On the other hand, polyaniline (PANi) is one of the most favorable conducting materials to be used, as it is easily synthesized, cost-effective, and has good conductivity. However, most organic solvents have restricted potential applications due to poor mechanical properties and dispersibility. Compared to PANi, PMMA has more outstanding physical and chemical properties, such as good dimensional stability and better molecular interactions between the monomers. To date, many research studies have focused on incorporating PANi into PMMA. In this review, the properties and suitability of PANi as a conducting material are briefly reviewed. The major parts of this paper reviewed different approaches to incorporating PANi into PMMA, as well as evaluating the modifications to improve its conductivity. Finally, the polymerization condition to prepare PMMA/PANi copolymer to improve its conductivity is also discussed.