On the iterative plug-in algorithm for estimating diurnal patterns of financial trade durations

ABSTRACT

This paper discusses the detailed performance of an iterative plug-in (IPI) bandwidth selector for estimating the diurnal duration pattern in a Semi-ACD (semiparametric autoregressive conditional duration) model. For this purpose a large simulation study was carried out. The effects of different factors, which affect the selected bandwidth are discussed in detail. The simulated results and data examples show that the proposed IPI algorithm works very well in practice and that the Semi-ACD model in general is clearly superior to the parametric ACD model, if there is a deterministic trend in the duration data. It is also shown that the bandwidth selection, and the estimation of the diurnal pattern and the model parameters will all be clearly improved, if the sample size is enlarged. According to the goodness-of-fit of the estimated diurnal pattern, a best combination of the above-mentioned factors is found. Moreover, a comparative study shows that our proposal usually outperforms the commonly used cubic spline.     

The split sample permutation t-tests

Without the exchangeability assumption, permutation tests for comparing two population means do not provide exact control of the probability of making a Type I error. Another drawback of permutation tests is that it cannot be used to test hypothesis about one population. In this paper, we propose a new type of permutation tests for testing the difference between two population means: the split sample permutation t-tests. We show that the split sample permutation t-tests do not require the exchangeability assumption, are asymptotically exact and can be easily extended to testing hypothesis about one population. Extensive simulations were carried out to evaluate the performance of two specific split sample permutation t-tests: the split in the middle permutation t-test and the split in the end permutation t-test. The simulation results show that the split in the middle permutation t-test has comparable performance to the permutation test if the population distributions are symmetric and satisfy the exchangeability assumption. Otherwise, the split in the end permutation t-test has significantly more accurate control of level of significance than the split in the middle permutation t-test and other existing permutation tests.     

Betting the future online

The Internet has brought, among other things, the online betting exchange. From desks in the City or from laptops on sunny beaches, those in the know or out of it can place bets with each other on anything under the sun, whether likely—Australia beating England at cricket—or the reverse. If you have a hunch that a genetically engineered Tyrannosaur will wander down Oxford Street next year, and if you want to put money on it, Internet betting exchanges will find someone to accommodate you. Leighton Vaughan Williams explains how a simple change in tax regime has brought something other than just a more efficient gambling system. It has created a strangely accurate way of foretelling the future.     

Truncation effect in closed and open birth interval data

A numerical investigation using a flexible simulation model to establish interval analysis as an index for changing natality patterns. Such an index should reflect parity distribution, the age at which women start reproduction, and the spacing of their births. The simulated statistical results illustrate the truncation effect that reflects a negative correlation between parity and the length of closed and open intervals in a birth or marriage cohort. Truncation is related to the duration of marriage at survey, but this duration interacts with other assumptions. Holding duration constant does not ensure that the data on intervals will reflect postulated changes in the distributions. For complete birth orders, this analysis does reflect patterns of child spacing. However, it ignores changes in the parity distribution, whether produced by deliberate limitation of family size or by the onset of secondary sterility. This difficulty is not overcome by life table analysis except under highly restrictive assumptions. It is doubtful whether the current emphasis on securing such data is justified. Further investigation is needed to provide a better basis for the definition and analysis of interval data if they are to be used.     

EWMA and industrial applications to feedback adjustment and control

In his book 'Out of the Crisis' the late Dr Edwards Deming asserted that 'if anyone adjusts a stable process to try to compensate for a result that is undesirable, or for a result that is extra good, the output will be worse than if he had left the process alone'. His famous funnel experiments supported this assertion. The development of the control chart by Dr Walter Shewhart stemmed from an approach made to him by the management of a Western Electric Company plant because of their awareness that adjustments made to processes often made matters worse. However, many industrial processes are such that the mean values of product quality characteristics shift and drift over time so that, instead of sequences of independent observations to which Deming's assertion applies, process owners are faced with autocorrelated data. The truth of Dr Deming's assertion is demonstrated, both theoretically and via computer simulation. The use of the Exponentially Weighted Moving Average (EWMA) for process monitoring is demonstrated and, for situations where process data exhibit autocorrelation, its use for feedback adjustment is discussed and demonstrated. Finally, successful applications of process improvements using EWMA-based control algorithms is discussed.     

Detecting multiple change points in piecewise constant hazard functions

The National Cancer Institute (NCI) suggests a sudden reduction in prostate cancer mortality rates, likely due to highly successful treatments and screening methods for early diagnosis. We are interested in understanding the impact of medical breakthroughs, treatments, or interventions, on the survival experience for a population. For this purpose, estimating the underlying hazard function, with possible time change points, would be of substantial interest, as it will provide a general picture of the survival trend and when this trend is disrupted. Increasing attention has been given to testing the assumption of a constant failure rate against a failure rate that changes at a single point in time. We expand the set of alternatives to allow for the consideration of multiple change-points, and propose a model selection algorithm using sequential testing for the piecewise constant hazard model. These methods are data driven and allow us to estimate not only the number of change points in the hazard function but where those changes occur. Such an analysis allows for better understanding of how changing medical practice affects the survival experience for a patient population. We test for change points in prostate cancer mortality rates using the NCI Surveillance, Epidemiology, and End Results dataset.     

Multivariate option price models and extremes

We consider the Cox-Ross-Rubinstein model of option prices which is a simple binomial model and deal with its multivariate extensions. The model consists of n independent up or down movements of the (multivariate) price. We discuss the model in the view of the limiting distributions for the price as well for the extreme changes of the prices during a period T which is split up into n small price changes, which depend on n (with nh = T). Interesting is also whether the components of the prices and of the extremes are asymptotically dependent.     

Coordinate-free analysis of trends in British social mobility

This paper is intended to make a contribution to the ongoing debate about declining social mobility in Great Britain by analyzing mobility tables based on data from the 1991 British Household Panel Survey and the 2005 General Household Survey. The models proposed here generalize Hauser's levels models and allow for a semi-parametric analysis of change in social mobility. The cell frequencies are assumed to be equal to the product of three effects: the effect of the father's position for the given year, the effect of the son's position for the given year, and the mobility effect related to the difference between the father's and the son's positions. A generalization of the iterative proportional fitting procedure is proposed and applied to computing the maximum likelihood estimates of the cell frequencies. The standard errors of the estimated parameters are computed under the product-multinomial sampling assumption. The results indicate opposing trends of mobility between the two timepoints. Fewer steps up or down in the society became less likely, while more steps became somewhat more likely.     

Avalanche! Coming! Now! Close to the borders of unpredictability

Earthquakes, evolution, avalanches—even something as simple as grains of sand tumbling down a pile: so many phenomena of nature seem to resist prediction. We still cannot tell when geological stresses will release themselves in a major quake or when the last extra grain of sand will make half the heap avalanche down. But have statisticians been looking in the wrong place? Or are these critical phenomena really inherently unpredictable? They are close to the borders, but still in predictable land, says Osvanny Ramos , one of the authors of an experiment to prove it.     

Exotic Properties of Non Homogeneous Markov and Semi-Markov Systems

In this article we study what we chose to call exotic properties of NHMS and NHSMS. The interplay between stochastic theory of NHMS and NHSMS and other branches of probability, stochastic processes and mathematics, we believe is a fascinating one apart from being important. In many cases the information needed for the evolution of a NHMS is a larger set than the history of the multidimensional process NHMS. In our world where an overflow of information exists almost in all problems, it is almost surely that this will be available. Here, we extend the definition of the NHMS in order to accomodate this case. In this respect we arrive at the defnition of the 𝒢-non homogeneous Markov system. We study the problem of change of measure in a 𝒢-non homogeneousMarkov system. It is proved that under certain conditions the NHMS retains the Markov property, while as expected the basic sequences of transition probabilities change and it is established how they do so. We also find the expected population structure of the NHMS under the new measure in close analytic form. We also define the 𝒢-non homogeneous semi-Markov system and we study the problem of change of measure in a 𝒢-non homogeneous semi-Markov system. It is proved that under certain conditions the NHSMS retains the semi-Markov property while as expected the basic sequences of transition probabilities change and it is established how they do so. We prove that if the input process of memberships is a non homogeneous Poisson process, then asymtotically and under certain easily met in practice conditions, the compensated population structure of the 𝒢-NHMS is a martingale. Finally we prove that the space of all random population structures, under easily met in practice conditions, is a Hilbert space.     

流动人口女性个体的生育间隔影响因素研究——基于2016年全国流动人口动态监测调查数据

本文采用2016年全国流动人口动态监测调查数据,运用半参数Cox回归对我国流动人口的生育间隔进行分析。结果发现：第一,不仅人口流动会延迟女性的生育时间,而且受教育水平的提高对女性的婚育间隔、第一次生育间隔均有显著的延迟效应。第二,结婚年龄越大,婚后越有可能选择尽早生育,且不同初婚时间对生育间隔的影响差异明显。同时,参加医疗保险对婚育间隔存在显著的缩短效应,而参加生育保险对生育间隔存在显著的延迟效应。越有经济实力以及在工作中担任重要职位的女性越有可能扩大生育间隔;而随着婚育间隔的扩大,第一次生育间隔反而会缩短。第三,初育子女的性别对第一次生育间隔的影响存在显著差异,即若初次生育为女性,则第一次生育间隔会缩短。第四,根据生育政策效果分析发现,放开生育政策虽无法促使女性缩短婚育间隔,但会明显缩短第一次生育间隔。     

Occam's shadow: levels of analysis in evolutionary ecology--where to next?

Evolutionary ecology is the study of evolutionary processes, and the ecological conditions that influence them. A fundamental paradigm underlying the study of evolution is natural selection. Although there are a variety of operational definitions for natural selection in the literature, perhaps the most general one is that which characterizes selection as the process whereby heritable variation in fitness associated with variation in one or more phenotypic traits leads to intergenerational change in the frequency distribution of those traits. The past 20 years have witnessed a marked increase in the precision and reliability of our ability to estimate one or more components of fitness and characterize natural selection in wild populations, owing particularly to significant advances in methods for analysis of data from marked individuals. In this paper, we focus on several issues that we believe are important considerations for the application and development of these methods in the context of addressing questions in evolutionary ecology. First, our traditional approach to estimation often rests upon analysis of aggregates of individuals, which in the wild may reflect increasingly non-random (selected) samples with respect to the trait(s) of interest. In some cases, analysis at the aggregate level, rather than the individual level, may obscure important patterns. While there are a growing number of analytical tools available to estimate parameters at the individual level, and which can cope (to varying degrees) with progressive selection of the sample, the advent of new methods does not reduce the need to consider carefully the appropriate level of analysis in the first place. Estimation should be motivated a priori by strong theoretical analysis. Doing so provides clear guidance, in terms of both (i) assisting in the identification of realistic and meaningful models to include in the candidate model set, and (ii) providing the appropriate context under which the results are interpreted. Second, while it is true that selection (as defined) operates at the level of the individual, the selection gradient is often (if not generally) conditional on the abundance of the population. As such, it may be important to consider estimating transition rates conditional on both the parameter values of the other individuals in the population (or at least their distribution), and population abundance. This will undoubtedly pose a considerable challenge, for both single- and multi-strata applications. It will also require renewed consideration of the estimation of abundance, especially for open populations. Thirdly, selection typically operates on dynamic, individually varying traits. Such estimation may require characterizing fitness in terms of individual plasticity in one or more state variables, constituting analysis of the norms of reaction of individuals to variable environments. This can be quite complex, especially for traits that are under facultative control. Recent work has indicated that the pattern of selection on such traits is conditional on the relative rates of movement among and frequency of spatially heterogeneous habitats, suggesting analyses of evolution of life histories in open populations can be misleading in some cases.     

Occam's shadow: Levels of analysis in evolutionary ecology--where to next?

Evolutionary ecology is the study of evolutionary processes, and the ecological conditions that influence them. A fundamental paradigm underlying the study of evolution is natural selection. Although there are a variety of operational definitions for natural selection in the literature, perhaps the most general one is that which characterizes selection as the process whereby heritable variation in fitness associated with variation in one or more phenotypic traits leads to intergenerational change in the frequency distribution of those traits. The past 20 years have witnessed a marked increase in the precision and reliability of our ability to estimate one or more components of fitness and characterize natural selection in wild populations, owing particularly to significant advances in methods for analysis of data from marked individuals. In this paper, we focus on several issues that we believe are important considerations for the application and development of these methods in the context of addressing questions in evolutionary ecology. First, our traditional approach to estimation often rests upon analysis of aggregates of individuals, which in the wild may reflect increasingly non-random (selected) samples with respect to the trait(s) of interest. In some cases, analysis at the aggregate level, rather than the individual level, may obscure important patterns. While there are a growing number of analytical tools available to estimate parameters at the individual level, and which can cope (to varying degrees) with progressive selection of the sample, the advent of new methods does not reduce the need to consider carefully the appropriate level of analysis in the first place. Estimation should be motivated a priori by strong theoretical analysis. Doing so provides clear guidance, in terms of both (i) assisting in the identification of realistic and meaningful models to include in the candidate model set, and (ii) providing the appropriate context under which the results are interpreted. Second, while it is true that selection (as defined) operates at the level of the individual, the selection gradient is often (if not generally) conditional on the abundance of the population. As such, it may be important to consider estimating transition rates conditional on both the parameter values of the other individuals in the population (or at least their distribution), and population abundance. This will undoubtedly pose a considerable challenge, for both single- and multi-strata applications. It will also require renewed consideration of the estimation of abundance, especially for open populations. Thirdly, selection typically operates on dynamic, individually varying traits. Such estimation may require characterizing fitness in terms of individual plasticity in one or more state variables, constituting analysis of the norms of reaction of individuals to variable environments. This can be quite complex, especially for traits that are under facultative control. Recent work has indicated that the pattern of selection on such traits is conditional on the relative rates of movement among and frequency of spatially heterogeneous habitats, suggesting analyses of evolution of life histories in open populations can be misleading in some cases.     

A semiparametric type II Tobit model for time-to-event data with application to a merger and acquisition study

Merger and acquisition is an important corporate strategy. We collect recent merger and acquisition data for companies on the China A-share stock market to explore the relationship between corporate ownership structure and speed of merger success. When studying merger success, selection bias occurs if only completed mergers are analyzed. There is also a censoring problem when duration time is used to measure the speed. In this article, for time-to-event outcomes, we propose a semiparametric version of the type II Tobit model that can simultaneously handle selection bias and right censoring. The proposed model can also easily incorporate time-dependent covariates. A nonparametric maximum likelihood estimator is proposed. The resulting estimators are shown to be consistent, asymptotically normal, and semiparametrically efficient. Some Monte Carlo studies are carried out to assess the finite-sample performance of the proposed approach. Using the proposed model, we find that higher power balance of a company is associated with faster merger success.     

Minimum Aberration Two-Level Fractional Factorial Split-Plot Designs with Hard to Change Factors

In this article we will consider industrial experiments in which some experimental factors have hard to change levels and others have levels which are easy to change. In such situations, fractional factorial split plot designs are often used where the hard to change factors are included as a subset of the whole plot factors and the easy to change factors make up the subplot factors. Here we consider the problem of finding two-level split plot designs which have minimum aberration among those designs which also minimize the number of level changes for the hard to change factors.     

The effectiveness of coach turnover and the effect on home team advantage,team quality and team ranking

The effectiveness of coach turnover on team performance is widely discussed in the literature due to the indirect impact of a team's performance on a club's revenues. This study examines the effect of coach turnover within a competition season by focusing on the change in team quality and the change in home team advantage under the new coach. The change in team quality or home team advantage can vary according to the team (team specific) or might be an independent quantity (non-team specific). We estimated nine possible regression models, given no change, team-specific change and non-team-specific change in quality or home team advantage. The data are the match results of Belgian male soccer teams playing in the highest national division during seven seasons. Results point to a team-specific effect of a new coach on a team's quality. This article further contributes by evaluating the new coach's success with regard to whether his ability to improve team quality also results in a better position of the team in the final ranking. A new coach will be able to improve the ranking of the team if the improved team quality under the new coach renders a positive team quality.     

土地确权、要素偏向性技术变革与产业结构转型

本文在构建土地确权、偏向性技术变革与产业结构转型关系理论框架的基础上,利用2014年和2016年中国劳动力动态调查(CLDS)数据集中的村庄和家庭层面数据,实证检验土地确权对产业结构转型的影响以及基于要素偏向性技术变革的传导机制。研究结果显示,土地确权有利于提升农业劳均产出、降低农业劳动密集度、增加农户非农劳动时间,从而促进产业结构转型。通过中介效应检验发现,体现为农业机械化的劳动增强型技术变革是土地确权影响产业结构转型的重要渠道。通过工具变量回归、PSM方法、替换被解释变量和增加控制变量识别验证,上述结论稳健可靠。此外,异质性分析表明,对于农业大省、非专业农户和未享受农业补贴农户,土地确权改革呈现出加速农业现代化和促进产业结构转型的双重政策红利。     

Performance of localized regression tree splitting criteria on data with discontinuities

Properties of the localized regression tree splitting criterion, described in Bremner & Taplin (2002) and referred to as the BT method, are explored in this paper and compared to those of Clark & Pregibon's (1992) criterion (the CP method). These properties indicate why the BT method can result in superior trees. This paper shows that the BT method exhibits a weak bias towards edge splits, and the CP method exhibits a strong bias towards central splits in the presence of main effects. A third criterion, called the SM method, that exhibits no bias towards a particular split position is introduced. The SM method is a modification of the BT method that uses more symmetric local means. The BT and SM methods are more likely to split at a discontinuity than the CP method because of their relatively low bias towards particular split positions. The paper shows that the BT and SM methods can be used to discover discontinuities in the data, and that they offer a way of producing a variety of different trees for examination or for tree averaging methods.     

Time‐change models for asymmetric processes

Many records in environmental sciences exhibit asymmetric trajectories. The physical mechanisms behind these records may lead for example to sample paths with different characteristics at high and low levels (up–down asymmetries) or in the ascending and descending phases leading to time irreversibility (front–back asymmetries). Such features are important for many applications, and there is a need for simple and tractable models that can reproduce them. In this paper, we explore original time‐change models where the clock is a stochastic process that depends on the observed trajectory. The ergodicity of the proposed model is established under general conditions, and this result is used to develop nonparametric estimation procedures based on the joint distribution of the process and its derivative. The methodology is illustrated on meteorological and oceanographic data sets. We show that, combined with a marginal transformation, the proposed methodology is able to reproduce important characteristics of the data set such as marginal distributions, up‐crossing intensity, and up–down and front–back asymmetries.     

ON UMP INVARIANT F-TEST PROCEDURES IN A GENERAL LINEAR MODEL

A simple method of setting linear hypotheses for a split mean vector testable by F-tests in a general linear model, when the covariance matrix has a general form and is completely unknown, is provided by extending the method discussed in Ukita et al. The critical functions in these F-tests are constructed as UMP invariants, when the covariance matrix has a known structure. Further critical functions in F-tests of linear hypotheses for the other split mean vector in the model are shown to be UMP invariant if the same known structure of the covariance matrix is assumed.