997 resultados para empirical likelihood


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This paper explores the place of aims in the early years foundation stage outdoor environment in England. Through examining the writing of academics, various themes are identified, and constructed into possible aims. These themes/aims are compared to an empirical study of early years teachers’ attitudes. Data was collected by questionnaire from schools within the University of Reading partnership. There was general agreement between experts and teachers as to the aims. While some respondents were able to explain what the aims of outdoor activity were, a significant number were unable to identify aims; further, a significant number did not distinguish between approach/practice and aims. A lack of understanding and agreement as to what the aims are may indicate teachers are unsure about the purpose of outdoor education for young children. A result of this study is to agree and make explicit the aims for outdoor education in the early years.

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This paper examines the determinants of cross-platform arbitrage profits. We develop a structural model that enables us to decompose the likelihood of an arbitrage opportunity into three distinct factors: the fixed cost to trade the opportunity, the level at which one of the platforms delays a price update and the impact of the order flow on the quoted prices (inventory and asymmetric information effects). We then investigate the predictions from the theoretical model for the European Bond market with the estimation of a probit model. Our main finding is that the results found in the empirical part corroborate strongly the predictions from the structural model. The event of a cross market arbitrage opportunity has a certain degree of predictability where an optimal ex ante scenario is represented by a low level of spreads on both platforms, a time of the day close to the end of trading hours and a high volume of trade.

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This paper investigates the behavior of residential property and examines the linkages between house price dynamics and bank herding behavior. The analysis presents evidence that irrational behaviour may have played a significant role in several countries, including; United Kingdom, Spain, Denmark, Sweden and Ireland. In addition, we also provide evidence indicative of herding behaviour in the European residential mortgage loan market. Granger Causality tests indicate that non-fundamentally justified prices dynamics contributed to herding by lenders and that this behaviour was a response by the banks as a group to common information on residential property assets. In contrast, in Germany, Portugal and Austria, residential property prices were largely explained by fundamentals. Furthermore, these countries show no evidence of either irrational price bubbles or herd behaviour in the mortgage market. Granger Causality tests indicate that both variables are independent.

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Empirical Mode Decomposition is presented as an alternative to traditional analysis methods to decompose geomagnetic time series into spectral components. Important comments on the algorithm and its variations will be given. Using this technique, planetary wave modes of 5-, 10-, and 16-day mean periods can be extracted from magnetic field components of three different stations in Germany. In a second step, the amplitude modulation functions of these wave modes can be shown to contain significant contribution from solar cycle variation through correlation with smoothed sunspot numbers. Additionally, the data indicate connections with geomagnetic jerk occurrences, supported by a second set of data providing reconstructed near-Earth magnetic field for 150 years. Usually attributed to internal dynamo processes within the Earth's outer core, the question of who is impacting whom will be briefly discussed here.

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This paper investigates the price effect of EPC ratings on the residential dwelling prices in Wales. It examines the capitalisation of energy efficiency ratings into house prices using two approaches. The first adopts a cross-sectional framework to investigate the effect of EPC band (and EPC rating) on a large sample of dwelling transactions. The second approach is based on a repeat-sales methodology to examine the impact of EPC band and rating on house price appreciation. The results show that, controlling for other price influencing dwelling characteristics, EPC band does affect house prices. This observed influence of EPC on price may not be a result of energy performance alone; the effect may be due to non-energy related benefits associated with certain types, specifications and ages of dwellings or there may be unobserved quality differences unrelated to energy performance such as better quality fittings and materials. An analysis of the private rental segment reveals that, in contrast to the general market, low-EPC rated properties were not traded at a significant discount, suggesting different implicit prices of potential energy savings for landlords and owner-occupiers.

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Empirical Mode Decomposition (EMD) is a data driven technique for extraction of oscillatory components from data. Although it has been introduced over 15 years ago, its mathematical foundations are still missing which also implies lack of objective metrics for decomposed set evaluation. Most common technique for assessing results of EMD is their visual inspection, which is very subjective. This article provides objective measures for assessing EMD results based on the original definition of oscillatory components.

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Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

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In this paper, we investigate the pricing of crack spread options. Particular emphasis is placed on the question of whether univariate modeling of the crack spread or explicit modeling of the two underlyings is preferable. Therefore, we contrast a bivariate GARCH volatility model for cointegrated underlyings with the alternative of modeling the crack spread directly. Conducting an empirical analysis of crude oil/heating oil and crude oil/gasoline crack spread options traded on the New York Mercantile Exchange, the more simplistic univariate approach is found to be superior with respect to option pricing performance.

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This paper investigates the effect of Energy Performance Certificate (EPC) ratings on residential prices in Wales. Drawing on a sample of approximately 192,000 transactions, the capitalisation of energy efficiency ratings into house prices is investigated using two approaches. The first adopts a cross-sectional framework to investigate the effect of EPC rating on price. The second approach applies a repeat-sales methodology to investigate the impact of EPC rating on house price appreciation. Statistically significant positive price premiums are estimated for dwellings in EPC bands A/B (12.8%) and C (3.5%) compared to houses in band D. For dwellings in band E (−3.6%) and F (−6.5%) there are statistically significant discounts. Such effects may not be the result of energy performance alone. In addition to energy cost differences, the price effect may be due to additional benefits of energy efficient features. An analysis of the private rental segment reveals that, in contrast to the general market, low-EPC rated dwellings were not traded at a significant discount. This suggests different implicit prices of potential energy savings for landlords and owner-occupiers.

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Using a newly developed integrated indicator system with entropy weighting, we analyzed the panel data of 577 recorded disasters in 30 provinces of China from 1985–2011 to identify their links with the subsequent economic growth. Meteorological disasters promote economic growth through human capital instead of physical capital. Geological disasters did not trigger local economic growth from 1999–2011. Generally, natural disasters overall had no significant impact on economic growth from 1985–1998. Thus, human capital reinvestment should be the aim in managing recoveries, and it should be used to regenerate the local economy based on long-term sustainable development.

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Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalizing constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network analysis. However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to the intractability of their likelihood functions. Several methods that permit exact, or close to exact, simulation from the posterior distribution have recently been developed. However, estimating the evidence and Bayes’ factors for these models remains challenging in general. This paper describes new random weight importance sampling and sequential Monte Carlo methods for estimating BFs that use simulation to circumvent the evaluation of the intractable likelihood, and compares them to existing methods. In some cases we observe an advantage in the use of biased weight estimates. An initial investigation into the theoretical and empirical properties of this class of methods is presented. Some support for the use of biased estimates is presented, but we advocate caution in the use of such estimates.

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Liquidity is a fundamentally important facet of investments, but there is no single measure that quantifies it perfectly. Instead, a range of measures are necessary to capture different dimensions of liquidity such as the breadth and depth of markets, the costs of transacting, the speed with which transactions can occur and the resilience of prices to trading activity. This article considers how different dimensions have been measured in financial markets and for various forms of real estate investment. The purpose of this exercise is to establish the range of liquidity measures that could be used for real estate investments before considering which measures and questions have been investigated so far. Most measures reviewed here are applicable to public real estate, but not all can be applied to private real estate assets or funds. Use of a broader range of liquidity measures could help real estate researchers tackle issues such as quantification of illiquidity premiums for the real estate asset class or different types of real estate, and how liquidity differences might be incorporated into portfolio allocation models.

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Purpose – The purpose of this paper is to seek to shed light on the practice of incomplete corporate disclosure of quantitative Greenhouse gas (GHG) emissions and investigates whether external stakeholder pressure influences the existence, and separately, the completeness of voluntary GHG emissions disclosures by 431 European companies. Design/methodology/approach – A classification of reporting completeness is developed with respect to the scope, type and reporting boundary of GHG emissions based on the guidelines of the GHG Protocol, Global Reporting Initiative and the Carbon Disclosure Project. Logistic regression analysis is applied to examine whether proxies for exposure to climate change concerns from different stakeholder groups influence the existence and/or completeness of quantitative GHG emissions disclosure. Findings – From 2005 to 2009, on average only 15 percent of companies that disclose GHG emissions report them in a manner that the authors consider complete. Results of regression analyses suggest that external stakeholder pressure is a determinant of the existence but not the completeness of emissions disclosure. Findings are consistent with stakeholder theory arguments that companies respond to external stakeholder pressure to report GHG emissions, but also with legitimacy theory claims that firms can use carbon disclosure, in this case the incomplete reporting of emissions, as a symbolic act to address legitimacy exposures. Practical implications – Bringing corporate GHG emissions disclosure in line with recommended guidelines will require either more direct stakeholder pressure or, perhaps, a mandated disclosure regime. In the meantime, users of the data will need to carefully consider the relevance of the reported data and develop the necessary competencies to detect and control for its incompleteness. A more troubling concern is that stakeholders may instead grow to accept less than complete disclosure. Originality/value – The paper represents the first large-scale empirical study into the completeness of companies’ disclosure of quantitative GHG emissions and is the first to analyze these disclosures in the context of stakeholder pressure and its relation to legitimation.

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Small and medium sized enterprises (SMEs) play an important role in the European economy. A critical challenge faced by SME leaders, as a consequence of the continuing digital technology revolution, is how to optimally align business strategy with digital technology to fully leverage the potential offered by these technologies in pursuit of longevity and growth. There is a paucity of empirical research examining how e-leadership in SMEs drives successful alignment between business strategy and digital technology fostering longevity and growth. To address this gap, in this paper we develop an empirically derived e-leadership model. Initially we develop a theoretical model of e-leadership drawing on strategic alignment theory. This provides a theoretical foundation on how SMEs can harness digital technology in support of their business strategy enabling sustainable growth. An in-depth empirical study was undertaken interviewing 42 successful European SME leaders to validate, advance and substantiate our theoretically driven model. The outcome of the two stage process – inductive development of a theoretically driven e-leadership model and deductive advancement to develop a complete model through in-depth interviews with successful European SME leaders – is an e-leadership model with specific constructs fostering effective strategic alignment. The resulting diagnostic model enables SME decision makers to exercise effective e-leadership by creating productive alignment between business strategy and digital technology improving longevity and growth prospects.