Testing for unobserved heterogeneity in exponential and Weibull duration models

We examine the use of the likelihood ratio (LR) statistic to test for unobserved heterogeneity in duration models, based on mixtures of exponential or Weibull distributions. We consider both the uncensored and censored duration cases. The asymptotic null distribution of the LR test statistic is not the standard chi-square, as the standard regularity conditions do not hold. Instead, there is a nuisance parameter identified only under the alternative, and a null parameter value on the boundary of the parameter space, as in Cho and White (2007a). We accommodate these and provide methods delivering consistent asymptotic critical values. We conduct a number of Monte Carlo simulations, comparing the level and power of the LR test statistic to an information matrix (IM) test due to Chesher (1984) and Lagrange multiplier (LM) tests of Kiefer (1985) and Sharma (1987). Our simulations show that the LR test statistic generally outperforms the IM and LM tests. We also revisit the work of van den Berg and Ridder (1998) on unemployment durations and of Ghysels et al. (2004) on interarrival times between stock trades, and, as it turns out, affirm their original informal inferences.     

An improved bootstrap test of stochastic dominance

We propose a new method of testing stochastic dominance that improves on existing tests based on the standard bootstrap or subsampling. The method admits prospects involving infinite as well as finite dimensional unknown parameters, so that the variables are allowed to be residuals from nonparametric and semiparametric models. The proposed bootstrap tests have asymptotic sizes that are less than or equal to the nominal level uniformly over probabilities in the null hypothesis under regularity conditions. This paper also characterizes the set of probabilities so that the asymptotic size is exactly equal to the nominal level uniformly. As our simulation results show, these characteristics of our tests lead to an improved power property in general. The improvement stems from the design of the bootstrap test whose limiting behavior mimics the discontinuity of the original test’s limiting distribution.     

小议如何提高电网调度员的服务

电网调度员是电网运行的指挥者,必须将调度员的水平提高作为电网调度安全管理的切入点和落脚点。从增强调度员的安全责任意识,规范调度员的安全管理行为,提升调度员的实际操作水平,提高调度员的事故处理能力等几个方面阐述了加强电网调度安全管理的方法和途径。     

新能效标准在核电站干式变压器的应用研究

针对新能效标准GB 20052—2020电力变压器能效限定值及能效等级发布实施后核电站用干式变压器能效等级和损耗水平的选择方案进行分析研究,回顾了核电站用干式变压器损耗水平的历史情况,以新能效标准实施前所采用的老1级能效为基准分析了新能效标准干变参数实现的技术路线,可通过提高硅钢片牌号或采用长圆形铁心截面的方式来达到降...     

Narrating the Power of Non‐Standard Employment: The Case of the Israeli Public Sector*

abstract Applying a critical discourse analysis approach, this study shows how the argument equating non‐standard employment (NSE) with organizational efficiency gains dominance over the contesting argument that views NSE as reducing organizational efficiency. We interviewed 24 senior‐level HR managers in the Israeli public sector, where NSE is used extensively, in order to shed light on the discursive order and the power struggle between these contesting arguments. Findings point to two story‐lines that helped discredit the argument in which NSE reduces organizational efficiency: (1) a statement of loyalty to organizational efficiency, accompanied by a gesture of concern for employees in non‐standard arrangements; and (2) subordinating an implied preference for eliminating NSE to organizational constraints.     

加强供电企业成本控制的预算管理

本文对供电企业以标准成本为基础制定成本预算的方法进行了介绍,结合实践,系统论述了建立在标准成本基础上的预算管理对供电企业成本控制所具有的独特作用,标准成本体系是供电企业加强成本管理的重要举措,将标准成本管理与企业全面预算管理体系进行良好的结合,必定会在供电企业成本管理中发挥巨大的实际作用。     

A robust version of the KPSS test based on indicators

This paper proposes a test of the null hypothesis of stationarity that is robust to the presence of fat-tailed errors. The test statistic is a modified version of the so-called KPSS statistic. The modified statistic uses the “sign” of the data minus the sample median, whereas KPSS used deviations from means. This “indicator” KPSS statistic has the same limit distribution as the standard KPSS statistic under the null, without relying on assumptions about moments, but a different limit distribution under unit root alternatives. The indicator test has lower power than standard KPSS when tails are thin, but higher power when tails are fat.     

The effect of corporate-level organizational factors on the transfer of human resource management practices: European and US MNCs and their Greek subsidiaries

One of the central questions in the literature on MNCs is the extent to which their subsidiaries act and behave as local firms (local isomorphism) versus the extent to which their practices resemble those of the parent company or some other global standard (internal consistency). Drawing on the resource-based view and resource-dependency theory, this paper aims to provide an insight into the interplay of several corporate-level organizational factors that affect the transfer of HRM practices across borders. Data collected from 80 European and US multinationals with subsidiaries in Greece are used to test specific hypotheses. Our results indicate that the level of importance attached to HRM by the MNC's top management and international experience have the highest explanatory power for the transfer of HRM practices, while international competitive strategy, informal control and the presence of expatriates also have a marginally significant influence.     

我国碳排放权期货产品开发研究

目前,国际碳交易市场定价权和碳计价结算货币主导权的缺失成为我国无论是在政府层面、学术领域还是实践方面,都是倍感担忧的问题。争夺碳交易定价权与推进人民币国际化有着重要的联系,两者之间可以构建一个重要的国家金融战略予以实施,其中适时推出CERs期货是该战略的重心。本文通过比较分析国外CERs期货合约设计的异同点及其对交易运行的影响,结合我国实际和市场特点,探讨了中国CERs标准期货合约的设计,并提出了我国发展碳排放权期货的战略目标、路径选择和政策建议。     

Aggregation is not the solution: the PPP puzzle strikes back

Recently, Imbs, Mumtaz, Ravn and Rey (2005, hereinafter IMRR) have argued that much of the purchasing power parity (PPP) puzzle is due to upwardly biased estimates of persistence. According to them, the source of the bias is the existence of heterogeneous price adjustment dynamics at the sectoral level that established time series or panel data methods fail to control for. This paper re‐examines this claim in two steps. Firstly, we demonstrate that IMRR's measures of sectoral persistence are systematically downwardly biased because they are based on an inaccurate definition of the ‘average’ impulse response function (IRF). We then show that standard estimates of shock persistence are recovered after this bias is corrected. Secondly, building on the results in Mayoral (2008), which prove that aggregate and micro models induce the same shock persistence behavior, we show that estimates based on aggregate and sectoral exchange rates are, in fact, highly consistent. Thus, aggregation is not the solution to the PPP puzzle. Copyright © 2009 John Wiley & Sons, Ltd.     

Testing Integration of Macroeconomic Time Series in Transitional Socialist Economies. A Modification of Perron Test

The presence of structural breaks reduces the power of integration tests. A number of methods were suggested to improve the statistical properties of integration tests in the presence of structural breaks. The most known are Perron tests, which allow to test for the level of integration of time series with one structural break. Perron tests allow for two types of structural breaks: additive outlier an innovative outlier. These tests are, however, not very useful in testing the level of integration of macroeconomic time series in countries in transition from centrally-planned to market economy. In such case one should expect two structural breaks to affect the time series: one at the beginning and one at the end of the transformation process. Test that allows for two additive outlier type structural breaks in time series is developed in this paper. This test has superior power as compared to standard Dickey-Fuller and Perron tests. This paper provides asymptotic distribution as well as finite sample properties of proposed test. Therefore practitioners receive a reliable tool for analyzing macroeconomic processes in transitional economies. This revised version was published online in July 2006 with corrections to the Cover Date.     

通信用热交换型户外机柜节能降噪设计

通信用热交换型户外机柜节能降噪设计突破常规使用的集成式热交换器的设计,以一个全新设计的机柜结构,以最简单的风机加散热风道的设计革新了热交换户外机柜的设计,在直接成本、噪音、能耗上均比集成式热交换户外机柜要好,甚至在噪音和能耗上高于风扇型户外机柜,各项性能指标均高于行标。真正达到功耗小、无污染、防护等级高。     

Mixtures of t-distributions for finance and forecasting

We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. Particularly desirable for econometric applications are closed-form expressions for antiderivatives (e.g., the cumulative density function). We illustrate the usefulness of these distributions in two applications. In the first application, we produce density forecasts of U.S. inflation and show that these forecasts are more accurate, out-of-sample, than density forecasts obtained using normal or standard t-distributions. In the second application, we replicate the option-pricing exercise of Abadir and Rockinger [Density functionals, with an option-pricing application. Econometric Theory 19, 778–811.] and obtain comparably good results, while gaining analytical tractability.     

浅谈电网谐波危害与消除措施

随着科学技术的发展,工业生产水平和人民生活水平的提高,非线性用电设备在电网中大量投运,造成了电网的谐波分量占的比重越来越大。它不仅增加了电网的供电损耗,而且干扰电网的保护装置与自动化装置的正常运行,造成了这些装置的误动与拒动,直接威胁电网的安全运行。因此,我们对非线性用电设备产生的谐波必须进行治理,使谐波分量不超过国家标准。文中分析了电力系统中谐波的来源及其产生的影响与危害,并总结了相应的消除对策和抑制的方法措施等。     

Interpoint Distance Test of Homogeneity for Multivariate Mixture Models

Finite mixtures offer a rich class of distributions for modelling of a variety of random phenomena in numerous fields of study. Using the sample interpoint distances (IPDs), we propose the IPD‐test statistic for testing the hypothesis of homogeneity of mixture of multivariate power series distribution or multivariate normal distribution. We derive the distribution of the IPDs that are drawn from a finite mixture of the multivariate power series distribution and multivariate normal distribution. Based on the empirical distribution of the IPDs, we construct a bootstrap test of homogeneity for other multivariate finite mixture models. The IPD test is applied to mixture models for matrix‐valued distributions and a test of homogeneity for Wishart mixture is presented. Numerical comparisons show that IPD test has accurate type I errors and is more powerful in most multivariate cases than the expectation–maximization (EM) test and modified likelihood ratio test.     

Assessing Variability of Complex Descriptive Statistics in Monte Carlo Studies Using Resampling Methods

Good statistical practice dictates that summaries in Monte Carlo studies should always be accompanied by standard errors. Those standard errors are easy to provide for summaries that are sample means over the replications of the Monte Carlo output: for example, bias estimates, power estimates for tests and mean squared error estimates. But often more complex summaries are of interest: medians (often displayed in boxplots), sample variances, ratios of sample variances and non‐normality measures such as skewness and kurtosis. In principle, standard errors for most of these latter summaries may be derived from the Delta Method, but that extra step is often a barrier for standard errors to be provided. Here, we highlight the simplicity of using the jackknife and bootstrap to compute these standard errors, even when the summaries are somewhat complicated. © 2014 The Authors. International Statistical Review © 2014 International Statistical Institute     

Factor‐Based Identification‐Robust Interference in IV Regressions

Robust methods for instrumental variable inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade‐offs resulting from weak or many instruments. We show that information reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor‐based modifications to three popular weak‐instrument‐robust statistics, and illustrate their validity asymptotically and in finite samples. Results are derived using asymptotic settings that are commonly used in both the factor and weak‐instrument literature. For the Anderson–Rubin statistic, we also provide analytical finite‐sample results that do not require any underlying factor structure. An illustrative Monte Carlo study reveals the following. Factor‐based tests control size regardless of instruments and factor quality. All factor‐based tests are systematically more powerful than standard counterparts. With informative instruments and in contrast to standard tests: (i) power of factor‐based tests is not affected by k even when large; and (ii) weak factor structure does not cost power. An empirical study on a New Keynesian macroeconomic model suggests that our factor‐based methods can bridge a number of gaps between structural and statistical modeling. Copyright © 2015 John Wiley & Sons, Ltd.     

Exact inference for the difference of Laplace location parameters

We consider exact procedures for testing the equality of means (location parameters) of two Laplace populations with equal scale parameters based on corresponding independent random samples. The test statistics are based on either the maximum likelihood estimators or the best linear unbiased estimators of the Laplace parameters. By conditioning on certain quantities we manage to express their exact distributions as mixtures of ratios of linear combinations of standard exponential random variables. This allows us to find their exact quantiles and tabulate them for several sample sizes. The powers of the tests are compared either numerically or by simulation. Exact confidence intervals for the difference of the means corresponding to those tests are also constructed. The exact procedures are illustrated via a real data example.     

Physics-informed Gaussian process regression for states estimation and forecasting in power grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method.We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power.For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.     

A nonparametric test for changing trends

Many tests of parameter change in dynamic models exhibit nonmonotonic power. An important source of the nonmonotonic power comes from the bias in estimating parameters when there is a change in the deterministic component. To avoid this bias, we propose a nonparametric test for changing trends based on nonparametrically detrended data. The tests are similar in spirit to nonparametric conditional moment tests such as Fan and Li (J. Nonparametr. Stat. 10 (1999a) 245; 11 (1999b) 251) and Zheng (J. Econometrics 75 (1996) 263). The resulting statistics have a standard normal distribution. A Monte Carlo experiment suggests that the tests have good power against changes in the deterministic component.