The purpose of this study is to develop sex-specific mortality estimation models using historical mortality data for Sri Lanka, based on the statistical time series techniques attributed to Lee and Carter (1992 2006-2008.
A b s t r a c t : The demand for electricity in Sri Lanlca depends mainly on the activities ol'clomestic, industrial and comnlercial sectors ancl t h e t h r e e conlponents a r e highly correlatecl. Although such correlation does n o t affect u n i v a r i a t e e s t i m a t i o n procedures, it may lead to incorrect inferences of influerltial factors on the demand for electricity. As a r e s u l t , s e p a r a t e u n i v a r i a t e a p p r o a c l~e s for each sector m a y not bc a n a c c u r ;~t e niethocl of' identifying s u c h factors. Therefore, this stucly alms to identify sucll facto1.s using multivariate regression whicb consjders the correlation among different sectors (or dependent variables) ancl estimates il multivariate demand model for the purpose offorecasting. The overall sign iricance of t h e fitted demand model a n d t h e significant influential factors a r e assessed by m u l t i v a r i a t e t e s t s s u c h a s B a r t l e t t ' s u s i n g t h e s t a t i s t i c a l package SAS. Theoretically, d e m a n d i s a function of i t s own price, t h e income level ,or consumers, and the price of substitutes. Gross Domestic Product (GDP) a t constant (1960) factor prices is used as a proxy for income level of consumers a n d kerosene i s tillten a s a close substitute Ibl. elcctriclty. The analysis uses quarterly data Tot two periocls 1970-1977 ant1 1978-1994 to a s s e s s t h e effect of t h e liberalized economy int~~ocluccd in late 1977. During the period aftcr 1977, the effect ol'thc income levcl h a s i~lcreasecl s u b s t a~l t i a l l y clue to t h e liberalized economy. T h e substitution between electricity and lierosene i s marginal ill the post-lil)ol~alizecl periocl, a s electricity is more efficient antl-convenient t l~a n Iterosene. Jlue to , such dit'ferenccs between t h e two pcriohs. t h e de~nancl for electricity lni>y be explained better by tnto models rather than a single lnoclel estimated for t l~e entire period. The multivariate demand model based on the post-li.beralized period is fbuncl to aclequately forecast the clemand for electricity.
Many empirical studies have been carried out in developed and emerging markets using methods to account for extreme market risk. A frontrunner in the methods used is Extreme Value Theory (EVT). In this study, these methods are applied to the All Share Price Index (ASPI) of the Colombo Stock Exchange (CSE) to obtain risk forecasts for VaR (Value at Risk) and ES (Expected Shortfall) at 99% confidence level. Recognizing the merits of both conditional and unconditional models for risk measures the most suitable unconditional model and conditional model are found for the ASPI. Contrary to other studies the Historical Simulation method was found to be more appropriate for obtaining static estimates than the static EVT model. The Two-Step Approach of McNeil and Frey which combines EVT and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) methodologies was found to be the most appropriate conditional model. Backtesting of models is done using a binomial test, Christoferssen's conditional coverage test and McNeil and Frey's ES test.
Three-mode data pertain to measurements related on the most general approach called Tucker3 to three entities or "modes". For example, measurements of a analysis that helps one to simultaneously study number of objects on a number of variables a t several different t h e interaction between all t h r e e modes occasions, would make up a three-mode data set. Such data frequently occur in fields such a s Agriculture, Biology,
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