100 resultados para out-of-sample forecast


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By formally quitting during a ceasefire he himself masterminded – and on the back of significant military gains – Putin can leave with his head held high. Having proven his critics wrong and demonstrated the capability of Russia’s military (and its new weapons – a splendid advert to any buyers of Russian arms), Putin can now concentrate on the diplomatic aspect of the peace process, something at which Moscow usually excels.

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The precautionary principle has the potential to act as a valuable tool in food law. It operates in areas of scientific uncertainty, calling for protective measures where there are potential threats to human health (or the environment). However, the manner of the principle’s incorporation and implementation within legislation are key to its effectiveness and general legitimacy. Specific considerations include the role and nature of risk assessments, assessors, sources of evidence, divergent opinions, risk communication, other legitimate factors and the weighting of interests. However, more fundamentally, the crystallisation of approaches and removal of all flexibility would undermine the principle’s central tenets. Firstly, principles crucially play a guiding and interpretative role. Secondly, reflexive modernisation and continuing scientific uncertainty call for the precautionary principle’s continued application – precautionary measures do not end the precautionary principle’s relevance. This can be partially achieved through the legislation so as to facilitate later precautionary measures, e.g. through temporary authorisations, derogations and safeguard clauses. However, crucially, it requires that the legislation also be interpreted in light of the precautionary principle. This paper investigates the logic behind the Court of Justice of the EU’s judgments and the circumstances that enable or deter the Court in taking, or permitting, stronger precautionary approaches. Although apparently inconsistent, a number of contextual factors including the legislative provisions and actors involved influence the judgments substantially. The analysis provides insight into improving the principle’s incorporation to facilitate its continued application and maintenance of flexibility, whilst bearing in mind the general desirability of objectivity and legal certainty.

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This chapter provides an overview of some of the key findings from a large mixed methods study regarding disabled children in care in Northern Ireland

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Major food adulteration and contamination events occur with alarming regularity and are known to be episodic, with the question being not if but when another large-scale food safety/integrity incident will occur. Indeed, the challenges of maintaining food security are now internationally recognised. The ever increasing scale and complexity of food supply networks can lead to them becoming significantly more vulnerable to fraud and contamination, and potentially dysfunctional. This can make the task of deciding which analytical methods are more suitable to collect and analyse (bio)chemical data within complex food supply chains, at targeted points of vulnerability, that much more challenging. It is evident that those working within and associated with the food industry are seeking rapid, user-friendly methods to detect food fraud and contamination, and rapid/high-throughput screening methods for the analysis of food in general. In addition to being robust and reproducible, these methods should be portable and ideally handheld and/or remote sensor devices, that can be taken to or be positioned on/at-line at points of vulnerability along complex food supply networks and require a minimum amount of background training to acquire information rich data rapidly (ergo point-and-shoot). Here we briefly discuss a range of spectrometry and spectroscopy based approaches, many of which are commercially available, as well as other methods currently under development. We discuss a future perspective of how this range of detection methods in the growing sensor portfolio, along with developments in computational and information sciences such as predictive computing and the Internet of Things, will together form systems- and technology-based approaches that significantly reduce the areas of vulnerability to food crime within food supply chains. As food fraud is a problem of systems and therefore requires systems level solutions and thinking.

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The paper addresses the issue of choice of bandwidth in the application of semiparametric estimation of the long memory parameter in a univariate time series process. The focus is on the properties of forecasts from the long memory model. A variety of cross-validation methods based on out of sample forecasting properties are proposed. These procedures are used for the choice of bandwidth and subsequent model selection. Simulation evidence is presented that demonstrates the advantage of the proposed new methodology.

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Reducible diffusions (RDs) are nonlinear transformations of analytically solvable Basic Diffusions (BDs). Hence, by construction RDs are analytically tractable and flexible diffusion processes. Existing literature on RDs has mostly focused on time-homogeneous transformations, which to a significant extent fail to explore the full potential of RDs from both theoretical and practical points of view. In this paper, we propose flexible and economically justifiable time variations to the transformations of RDs. Concentrating on the Constant Elasticity Variance (CEV) RDs, we consider nonlinear dynamics for our time-varying transformations with both deterministic and stochastic designs. Such time variations can greatly enhance the flexibility of RDs while maintaining sufficient tractability of the resulting models. In the meantime, our modeling approach enjoys the benefits of classical inferential techniques such as the Maximum Likelihood (ML). Our application to the UK and the US short-term interest rates suggests that from an empirical point of view time-varying transformations are highly relevant and statistically significant. We expect that the proposed models can describe more truthfully the dynamic time-varying behavior of economic and financial variables and potentially improve out-of-sample forecasts significantly.

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We develop a continuous-time asset price model to capture the timeseries momentum documented recently. The underlying stochastic delay differentialsystem facilitates the analysis of effects of different time horizons used bymomentum trading. By studying an optimal asset allocation problem, we find thatthe performance of time series momentum strategy can be significantly improvedby combining with market fundamentals and timing opportunity with respect tomarket trend and volatility. Furthermore, the results also hold for different timehorizons, the out-of-sample tests and with short-sale constraints. The outperformanceof the optimal strategy is immune to market states, investor sentiment andmarket volatility.