Alternative mean-squared error estimators for synthetic estimators of domain means

In forest management surveys, the mean of a variable of interest (Y) in a population composed of N equal area spatial compact elements is increasingly estimated from a model linking Y to an auxiliary vector X known for all elements in the population. It is also desired to have synthetic estimates of the mean of Y in spatially compact domains (forest stands) with no or at most one sample-based observation of Y. We develop three alternative estimators of mean-squared errors (MSE) that reduce the risk of a serious underestimation of the uncertainty in a synthetic estimate of a domain mean in cases where the employed model does not accounts for domain effects nor spatial autocorrelation in unobserved residual errors. Expansions of the estimators including anticipated effects of a spatial autocorrelation in residual errors are also provided. Simulation results indicate that the conventional model-dependent (MD) population-level estimator of variance in a synthetic estimate of a domain mean underestimates uncertainty by a wide margin. Our alternative estimators mitigated, in settings with weak to moderate domain effects and relatively small sample sizes, to a large extent, the problem of underestimating uncertainty. We demonstrate applications with examples from two actual forest inventories.     

MCMC Bayesian Estimation in FIEGARCH Models

Bayesian inference for fractionally integrated exponential generalized autoregressive conditional heteroscedastic (FIEGARCH) models using Markov chain Monte Carlo (MCMC) methods is described. A simulation study is presented to assess the performance of the procedure, under the presence of long-memory in the volatility. Samples from FIEGARCH processes are obtained upon considering the generalized error distribution (GED) for the innovation process. Different values for the tail-thickness parameter ν are considered covering both scenarios, innovation processes with lighter (ν > 2) and heavier (ν < 2) tails than the Gaussian distribution (ν = 2). A comparison between the performance of quasi-maximum likelihood (QML) and MCMC procedures is also discussed. An application of the MCMC procedure to estimate the parameters of a FIEGARCH model for the daily log-returns of the S&P500 U.S. stock market index is provided.     

Alternative operations strategy processes: do they matter?

Abstract

With the increasing significance of business model innovation, and due to the ongoing difficulties encountered in executing formally developed strategies, there is a strong case for studying the functional-level strategy processes from fresh perspectives. This paper examines the significance of alternative operations strategy (OS) processes and organisational context and explores how they relate to operations performance. The empirical evidence was drawn based on the statistical analysis of data collected through 242 survey responses from the senior management staff of the Canadian oil and gas industry. The evidence drawn from the empirical study confirmed that alternative forms of OS development do indeed exist in practice and that certain organisational factors have a direct influence on OS processes. The analysis discerned four strategy process configurations representing singular and multiple combinations of the three process modes identified through the literature review. The results explain the differences between these process configurations in terms of certain contextual factors and operations performance. The findings of this study help advance the understanding of OS processes and may contribute to theory building, particularly what is known as ‘mid-range’ theory, as the evidence was built through the analysis of a relatively large sample of data drawn from an industry sector that has not been reported on in previous studies.     

Team creativity versus team learning: transcending conceptual boundaries to inspire future framework building

The purpose of this study was to compare team creativity and team learning – approached as interpersonal processes – conceptually and empirically and thereby inspire future framework building on team creativity. First, both concepts were compared based on present literature. In the next – empirical – step, a questionnaire consisting of a range of team creative and learning processes was developed and validated among 112 design teams, encompassing 540 employees. By means of a questionnaire validation protocol, this study assessed the empirical similarities and interrelation between team creative and learning processes. Several conceptual similarities in both frameworks were identified. The theoretically expected difference between team creativity and team learning (i.e. the aspect newness) was not supported by our empirical results. A five-factor model – consisting of team creative efficacy, facilitating team processes, basic team processes, error communication, and co-construction was validated. This study contributes to the present literature by showing that future theorising on team creative processes can certainly draw inspiration from the team learning literature in several ways.     

我国破产制度的域外借鉴与完善进路——基于《全球营商环境报告》的分析

破产制度是当前我国市场主体合法、高效退出市场的制度保障。为提升我国破产司法能力,优化营商环境,通过对世界银行《全球营商环境报告》中关于司法破产能力各项指标进行数据分析和法律分析,发现我国破产制度存在破产时间长、破产成本高、破产回收率低和破产架构不全等问题。为了改进和完善我国的破产制度,可以借鉴英国、我国香港地区个人破产制度中的自由财产保留制度和破产依法免责制度以及德国的余债免除制度,建立健全我国个人破产制度;学习美国、韩国专业的破产法院制度,设立我国破产法院;分析日本的司法破产规定,设置依职权启动破产程序,进而提高司法破产效率、降低司法破产成本,解决“执行不能”问题。     

农村潜在劳动力剩余标准及其就业出路

通过分析比较各种衡量农村劳动力剩余规模评估标准的优缺点,根据经济学中行业利润趋于平均化的原理,推导出了一个衡量农村潜在劳动力剩余规模的数学模型,并根据模型的计算公式对中国农村潜在的劳动力剩余规模进行了估算.同时分析了影响农村潜在剩余劳动力的因素,并进一步探讨了解决农村劳动力充分就业问题的理念和发展战略.     

Computing the Distributions of Economic Models via Simulation

We study a Monte Carlo algorithm for computing marginal and stationary densities of stochastic models with the Markov property, establishing global asymptotic normality and O_P(n^–1/2) convergence. Asymptotic normality is used to derive error bounds in terms of the distribution of the norm deviation.     

Variance estimation in nonparametric regression with jump discontinuities

Variance estimation is an important topic in nonparametric regression. In this paper, we propose a pairwise regression method for estimating the residual variance. Specifically, we regress the squared difference between observations on the squared distance between design points, and then estimate the residual variance as the intercept. Unlike most existing difference-based estimators that require a smooth regression function, our method applies to regression models with jump discontinuities. Our method also applies to the situations where the design points are unequally spaced. Finally, we conduct extensive simulation studies to evaluate the finite-sample performance of the proposed method and compare it with some existing competitors.     

Integer‐valued Trawl Processes: A Class of Stationary Infinitely Divisible Processes

This paper introduces a new continuous‐time framework for modelling serially correlated count and integer‐valued data. The key component in our new model is the class of integer‐valued trawl processes, which are serially correlated, stationary, infinitely divisible processes. We analyse the probabilistic properties of such processes in detail and, in addition, study volatility modulation and multivariate extensions within the new modelling framework. Moreover, we describe how the parameters of a trawl process can be estimated and obtain promising estimation results in our simulation study. Finally, we apply our new modelling framework to high‐frequency financial data.     

Sample Path Asymmetries in Non‐Gaussian Random Processes

We tackle an important although rarely addressed question of accounting for a variety of asymmetries frequently observed in stochastic temporal/spatial records. First, we review some measures intending to capture such asymmetries that have been introduced on various occasions in the past and then propose a family of measures that is motivated by Rice's formula for crossing level distributions of the slope. We utilize those asymmetry measures to demonstrate how a class of second‐order models built on the skewed Laplace distributions can account for sample path asymmetries. It is shown that these models are capable of mimicking not only distributional skewness but also more complex geometrical asymmetries in the sample path such as tilting, front‐back slope asymmetry and time irreversibility. Simple moment‐based estimation techniques are briefly discussed to allow direct application to modelling and fitting actual records.