Income ineqaulity from 1960-2012: a brief time-series history of capital and labour

This commentary examines two principal forms of inequality and their evolution since the 1960s: the division of national income between capital and labour, and the share of total income held by the top 1 per cent of earners. Trends are linked to current discussions of inequality drivers such as financialisation, and a brief time-series analysis of the effects of trade and financial sector growth on top incomes is presented.

Bandwidth selection by cross-validation for forecasting long memory financial time series

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.

Optimal Time Series Momentum

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.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Validation of non‑stationary precipitation series for site‑specific impact assessment: comparison of two statistical downscaling techniques

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

Synthesis, thermal characterization and rheological properties of a homologous series of polymethacrylate-based side-chain liquid crystal polymers

A new homologous series of side-chain liquid crystal polymers, the poly[omega-(4-cyanoazobenzene-4'-oxy)alkyl methacrylate]s, have been prepared in which the length of the flexible alkyl spacer is varied from 3 to 12 methylene units. All the polymers exhibit liquid crystalline behaviour; specifically, crystal E, smectic A and nematic phases are observed. The glass transition temperatures decrease on increasing spacer length before reaching a limiting value at ca. 30 degrees C. The clearing temperatures exhibit an odd-even effect on varying the length and parity of the spacer. This is attributed to the change in the average shape of the side chain as the parity of the spacer is varied. This rationalization also accounts for the observed alternation in the entropy change associated with the clearing transition. A weak relaxation is observed theologically for several members of this polymer series at temperatures above their respective glass transition temperatures. This is attributed either to specific motions of the smectic layers or to 180 degrees reorientational jumps of the long axis of the mesogenic unit about the polymer backbone. (C) 1997 Elsevier Science Ltd. All rights reserved.

“If I Were Nick”: Men’s Responses to an Interactive Video Drama Series to Support Smoking Cessation

Background: Men continue to smoke in greater numbers than women; however, few interventions have been developed and tested to support men’s cessation. Men also tend to rely on quitting strategies associated with stereotypical manliness, such as willpower, stoicism and independence, but may lack the self‐efficacy skills required to sustain a quit. In this article we describe the development of and reception to an interactive video drama (IVD) series, composed of 7 brief scenarios, to support and strengthen men’s smoking cessation efforts. The value of IVD in health promotion is predicated on the evidence that viewers engage with the material when they are presented characters with whom they can personally identify. The video dramatizes the challenges unfolding in the life of the main character, Nick, on the first day of his quit and models the skills necessary to embark upon a sustainable quit. 
Objective: The objective was to describe men’s responses to the If I were Nick IVD series as part of a pilot study of QuitNow MenTM, an innovative smoking cessation website designed for men. Specific objectives were to explore the resonance of the main character of the IVD series with end‐users, and men’s perceptions of the effectiveness of the IVD series for supporting their quit self‐management. 
Methods: Seven brief IVD scenarios were developed, filmed with a professional actor and uploaded to a new online smoking cessation website, QuitNow MenTM.  A sample of 117 men who smoked were recruited into the study and provided baseline data prior to access to the QuitNow MenTM website for a 6 month period. During this time, 47 men chose to view the IVDs. Their responses to questions about the IVDs were collected in 3‐month and 6‐month online follow‐up surveys and analyzed using descriptive statistics. 
Findings: The majority of participants indicated they related to the main character, Nick. Participants who “strongly agreed” they could relate to Nick perceived significantly higher levels of support from the IVDs than the “neutral” and “disagree” groups (P <.001, d =2.0, P <.001 d =3.1). The “agree” and “neutral” groups were significantly higher on rated support from the videos than the “disagree” (P <.001 d =2.2, P =.01 d = 1.5). Participants’ perception of the main character was independent of participant age, education attainment or previous quit attempts. 
Conclusions: The findings suggest that IVD interventions may be an important addition to men’s smoking cessation programs. Given that the use of IVD scenarios in health promotion is in its infancy, the positive outcomes from this pilot study signal the potential for IVD and warrant ongoing evaluation in smoking cessation and, more generally, men’s health promotion.