In-Sample Forecasting
In-sample forecasting: A brief review and new algorithms
... distribution function. When adjusting for right censoring, the Efron’s algorithm starts with estimating a distribution function, ignoring the censoring. It distributes the mass at the first censored time to the right of ...
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In-Sample Forecasting with Local Linear Survival Densities
... casting structure in-sample forecasting, because information on the two relevant densities of the multiplicative structure is indeed in the sample. The independence assumption for the unfiltered data ...
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In-sample forecasting: structured models and reserving
... the forecasting exercise is to predict ...the forecasting exercise is to predict the densities on the triangle that added to the first completes a ...this forecasting structure in-sample ...
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In-Sample Forecasting Applied to Reserving and Mesothelioma Mortality
... sample size. We use a data set of this type provided by UK Health Service Executive that consists of annual aggregated counts of deaths caused by exposure to asbestos in Great Britain. The data are given by age ...
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In-Sample Forecasting Applied to Reserving and Mesothelioma Mortality
... Permanent repository link: http://openaccess.city.ac.uk/4962/ Link to published version: http://dx.doi.org/10.1016/j.insmatheco.2014.12.001 Copyright and reuse: City Research Online aims[r] ...
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Asymptotics for In-Sample Density Forecasting
... mally carried out manually by highly paid actuaries. The automatic adjustment of sea- sonal effects offered by this paper is therefore potentially cost saving. Insurance companies currently use the classical chain ladder ...
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Operational time and in-sample density forecasting
... happened and on a retrospective observation of the onset of these events. There- fore, there needs to be a lot less data to keep track of. For example, in-sample fore- casting requires only keeping track of actual ...
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Generalised additive dependency inflated models including aggregated covariates
... finite sample simulation studies showing good performance of our B-spline estimation ap- ...important forecasting result on future asbestos related deaths are presented in Section ...the forecasting ...
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Characterizing severe weather potential in synoptically weakly forced thunderstorm environments
... Abstract. Weakly forced thunderstorms (WFTs), short-lived convection forming in synoptically quiescent regimes, are a contemporary forecasting challenge. The convective en- vironments that support severe WFTs are ...
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Numerical Technique Based Model Approach to Electricity Growth Forecasting: Ilorin PHCN District as Sample
... of forecasting model for accurate prediction of electricity growth in ...power forecasting will lead to sound energy management, capital and operational ...
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Modeling prices of wholesale market of electric energy and power by the example of the UPS of the Ural
... of forecasting of the balancing market index deviations from the market rate on the day-ahead sample maximum likelihood using a variety of approximation equations for positive and negative values of the ...
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Learning Everywhere: Pervasive Machine Learning for Effective High Performance Computation: Application Background
... epidemic forecasting problem, we take an existing epidemiological model, derive a reasonable distribution of parameter values, sample many configurations of the model from the distribution and run HPC based ...
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Daily Network Traffic Prediction Based on Backpropagation Neural Network
... However, modeling using the artificial neural network (ANN) model can provide better analytical results, and it is effective for forecasting (Chen et al., 2014), in which, this method is able to work well on the ...
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DSGE Model Evaluation in a Bayesian Framework: an Assessment
... for instance obtained from a theoretical structural model, directly on the parameters. Litterman (1981) and Doan, Litterman and Sims (1984) introduced the so-called Minnesota Prior, where all equations are centered ...
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A Forecasting System of Carbon Price in the Carbon Trading Markets Using Artificial Neural Network
... forecasting the carbon price. RBFN is able to deal with varied and complicated relations between input and output data, and ACO helps with the appropriated regulation of learning rates. These tools help to improve ...
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A Vector Auto Regressıve (VAR) Model for the Turkish Financial Markets
... efficient forecasting models for these variables play important roles in the decision making ...and forecasting purposes, it is also important to analyse the interaction between variables in a multivariate ...
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A consumption based approach to exchange rate predictability
... The consumption-based model presented in Section 2 contains an arbitrage condition for international asset markets and its relation with consumers’ stochastic discount factors (Equation 11). Therefore, this approach does ...
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House Market in Chinese Cities: Dynamic Modeling, In Sampling Fitting and Out of Sample Forecasting
... This paper also carries important policy implications. For instance, if the housing market is believed to be “overheating,” our results suggest that increasing the interest rate for mortgage loans may not have a ...
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Beating the Odds - A State Space Model for predicting match results in the Australian Football League
... This thesis investigates whether state space models have the potential to pre- dict the outcome of Australian Rules Football matches and can produce significant positive return over the bookmaker’s odds. The point of ...
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Using Conditional Extreme Value Theory to Estimate Value at Risk for Daily Currency Exchange Rates
... EVT based approaches have in the recent past been considered in finance to address the shortcomings of the conventional techniques as well as improve the estimation of VaR. The EVT theory focuses on modeling the tail ...
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