Search results

The public’s awareness of noise and vibration forms a significant barrier to further development of railways. This chapter begins with a short introduction to the main fundamental aspects of acoustics, including decibels, frequency analysis, the propagation of sound with distance and common measurement quantities. The main sources of railway noise are discussed, including rolling noise, impact noise, curve squeal and aerodynamic noise. Simple calculation procedures are described that can be used to assess the impact of railway noise and to compare it with legal limits. The final section is devoted to ground vibration, which is a related form of environmental disturbance.

This research studies private information from extreme price movements or jumps. The authors calculate the private information using a reduced form model from the stochastic volatility jump process and use several statistical robustness tests as well as several frequencies to improve our consistency. This study reveals that private information is significant in explain the existence of jumps in capital markets in Southeast Asia, whereas macroeconomic events cannot explain them. The authors determine empirically that private information in Malaysia, Singapore, Thailand, and Indonesia are not persistent and its value gradually decreases when we use the lower frequency. Based on the Fama–Macbeth regression, this study shows that private information in the capital market has a strong positive relationship with individual returns in Indonesia’s capital market and Thailand’s capital market for all frequencies.

Purpose – The purpose of this chapter is to explore whether and how social tagging can be useful in an information web site for cancer patients and their relatives.

Methodology/approach – Three studies have been carried out in order to investigate the research questions. First, we reviewed and analyzed literature about cancer patients’ information needs and seeking behavior, and about social tagging and patient terminology. Second, we analyzed tags applied to blog postings at Blogomkraeft.dk, a blog site at the Danish information web site Cancer.dk. The tags were compared with the formal browsing structure of Cancer.dk. Results from the two studies were used to develop a prototype for social tagging at Cancer.dk. Thus third, we evaluated the prototype in a usability study.

Findings – We found that tags have the potential to describe and provide access to web site content from the users’ perspective and language use. Social tags may be a means to bridge between scientific viewpoints and terminology and everyday problems and vocabulary. Tags at Blogomkraeft.dk are mainly factual, often detailed, and do not cover as many functions as tags in more general bookmarking systems. An important finding is that some tags seemed to add to and supplement the content instead of factually describing the content of a blog posting. The usability test showed that our test persons liked the tagging feature.

Social implications – Tagging features give the public an opportunity to apply their own terms to documents, reflecting their own model of the current topic. Tags may furthermore function as colloquial lead-in terms from users’ search formulations at search engines such as Google to the domain-specific, tailored cancer web site.

Originality/value – Unlike most research on social tagging so far, we investigate tagging in a domain-specific setting, how tags can improve the interaction and communication between layman users and domain experts in an information web site within health care.

It is widely documented that while contemporaneous spot and forward financial prices trace each other extremely closely, their difference is often highly persistent and the conventional cointegration tests may suggest lack of cointegration. This chapter studies the possibility of having cointegrated errors that are characterized simultaneously by high persistence (near-unit root behavior) and very small (near zero) variance. The proposed dual parameterization induces the cointegration error process to be stochastically bounded which prevents the variables in the cointegrating system from drifting apart over a reasonably long horizon. More specifically, this chapter develops the appropriate asymptotic theory (rate of convergence and asymptotic distribution) for the estimators in unconditional and conditional vector error correction models (VECM) when the error correction term is parameterized as a dampened near-unit root process (local-to-unity process with local-to-zero variance). The important differences in the limiting behavior of the estimators and their implications for empirical analysis are discussed. Simulation results and an empirical analysis of the forward premium regressions are also provided.

This article proposes a new approach to detecting the presence of common cyclical features when several time series are sampled at different frequencies. We generalize the common-frequency approach introduced by Engle and Kozicki (1993) and Vahid and Engle (1993). We start with the mixed-frequency VAR representation investigated in Ghysels (2012) for stationary time series. For non-stationary time series in levels, we show that one has to account for the presence of two sets of long-run relationships. The first set is implied by identities stemming from the fact that the differences of the high-frequency I (1) regressors are stationary. The second set comes from possible additional long-run relationships between one of the high-frequency series and the low-frequency variables. Our transformed vector error-correction model (VECM) representations extend the results of Ghysels (2012) and are important for determining the correct set of variables to be used in a subsequent common cycle investigation. This fact has implications for the distribution of test statistics and for forecasting. Empirical analyses with quarterly real gross national product (GNP) and monthly industrial production indices for, respectively, the United States and Germany illustrate our new approach. We also conduct a Monte Carlo study which compares our proposed mixed-frequency models with models stemming from classical temporal aggregation methods.

In this chapter, we aim to measure the actual volatility within a model-based framework using high-frequency data. In the empirical finance literature, it is widely discussed that tick-by-tick prices are subject to market micro-structure effects such as bid-ask bounces and trade information. These market micro-structure effects become more and more apparent as prices or returns are sampled at smaller and smaller time intervals. An increasingly popular measure for the variability of spot prices on a particular day is realised volatility that is typically defined as the sum of squared intra-daily log-returns. Recent theoretical results have shown that realised volatility is a consistent estimator of actual volatility, but when it is subject to micro-structure noise and the sampling frequency increases, the estimator diverges. Parametric and nonparametric methods can be adopted to account for the micro-structure bias. Here, we measure actual volatility using a model that takes account of micro-structure noise together with intra-daily volatility patterns and stochastic volatility. The coefficients of this model are estimated by maximum likelihood methods that are based on importance sampling techniques. It is shown that such Monte Carlo techniques can be employed successfully for our purposes in a feasible way. As far as we know, this is a first attempt to model the basic components of the mean and variance of high-frequency prices simultaneously. An illustration is given for three months of tick-by-tick transaction prices of the IBM stock traded at the New York Stock Exchange.

Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

This paper is concerned with estimation of the parameters of a high-frequency VAR model using mixed-frequency data, both for the stock and for the flow case. Extended Yule–Walker estimators and (Gaussian) maximum likelihood type estimators based on the EM algorithm are considered. Properties of these estimators are derived, partly analytically and by simulations. Finally, the loss of information due to mixed-frequency data when compared to the high-frequency situation as well as the gain of information when using mixed-frequency data relative to low-frequency data is discussed.

Human performance, particularly that of the warfighter, has been the subject of a large amount of research during the past few decades. For example, in the Medline database of medical and psychological research, 1,061 papers had been published on the topic of “military performance” as of October 2003. Because warfighters are often pushed to physiological and mental extremes, a study of their performance provides a unique glimpse of the interplay of a wide variety of intrinsic and extrinsic factors on the functioning of the human brain and body. Unfortunately, it has proven very difficult to build performance models that can adequately incorporate the myriad of physiological, medical, social, and cognitive factors that influence behavior in extreme conditions. The chief purpose of this chapter is to provide a neurobiological (neurochemical) framework for building and integrating warfighter performance models in the physiological, medical, social, and cognitive areas. This framework should be relevant to all other professionals who routinely operate in extreme environments. The secondary purpose of this chapter is to recommend various performance metrics that can be linked to specific neurochemical states and can accordingly strengthen and extend the scope of the neurochemical model.