Modeling rare events through a pRARMAX process

7 and 8 introduce a power max-autoregressive process, in short pARMAX, as an alternative to heavy tailed ARMA when modeling rare events. In this paper, an extension of pARMAX is considered, by including a random component which makes the model more applicable to real data. We will see conditions under which this new model, here denoted as pRARMAX, has unique stationary distribution and we analyze its extremal behavior. Based on Bortot and Tawn (1998), we derive a threshold-dependent extremal index which is a functional of the coefficient of tail dependence of 14 and 15 which in turn relates with the pRARMAX parameter. In order to fit a pRARMAX model to an observed data series, we present a methodology based on minimizing the Bayes risk in classification theory and analyze this procedure through a simulation study. We illustrate with an application to financial data.     

Recurrence relations for moments of progressively censored order statistics from logistic distribution with applications to inference

In this paper, we establish several recurrence relations for the single and product moments of progressively Type-II right censored order statistics from a logistic distribution. The use of these relations in a systematic manner allows us to compute all the means, variances and covariances of progressively Type-II right censored order statistics from the logistic distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes (R₁,…,R_m). The results established here generalize the corresponding results for the usual order statistics due to [Shah, 1966] and [Shah, 1970]. These moments are then utilized to derive best linear unbiased estimators of the location and scale parameters of the logistic distribution. A comparison of these estimators with the maximum likelihood estimations is then made. The best linear unbiased predictors of censored failure times are briefly discussed. Finally, an illustrative example is presented.     

Deletions in random binary search trees: A story of errors

The usual assumptions for the average case analysis of binary search trees (BSTs) are random insertions and random deletions. If a BST is built by n random insertions the expected number of key comparisons necessary to access a node is 2 ln n+O(1). This well-known result is already contained in the first papers on such ‘random’ BSTs. However, if random insertions are intermixed with random deletions the analysis of the resulting BST seems to become more intricate. At least this is the impression one gets from the related publications since 1962, and it is quite appropriate to speak of a story of errors in this context, as will be seen in the present survey paper, giving an overview on this story.     

Extension and necessity of Cheng and Wu conditions

Repeated Measurement Designs, with two treatments, n (experimental) units and p periods are examined. The model examined is with uncorrelated observations following a continuous distribution with constant variance and the parameters of interest are (i) the difference of direct effects and (ii) the difference of residual effects. In this paper (a) the difference of Universal optimality and Φ-optimality is clarified and (b) the sufficient conditions of Cheng and Wu (1980) are extended to include the case n=2 mod 4, p even, (c) also it is shown that these conditions are also necessary for Φ-optimality for estimating direct as well as residual effects, and (d) a method is proposed to construct Φ-optimal designs and examples are given when n even and p=3, n=0 mod 4 and p=4, n=2 mod 4 and p=4. In the last case the estimated parameters in the optimal design are correlated.     

The generalized linear chirp process

The linear chirp process is an important class of time series for which the instantaneous frequency changes linearly in time. Linear chirps have been used extensively to model a variety of physical signals such as radar, sonar, and whale clicks (see 1, 5 and 6). We introduce the stochastic linear chirp model and then define the generalized linear chirp (GLC) process as a special case of the G-stationary process studied by Jiang et al. (2006) to model data with time-varying frequencies. We then define GLC(p,q) processes and show that the relationship between stochastic linear chirp processes and GLC(p,q) processes is analogous to that between harmonic and ARMA models. The new methods are then applied to both simulated and actual data sets.     

金融周期、金融波动与中国经济增长——基于省际面板门槛模型的研究

本文应用面板门槛模型,研究了2005-2017年间我国31个省（市、自治区）金融波动对经济增长的影响。研究发现,随着金融周期所处阶段的变化,金融波动对经济增长会产生显著的非对称性双重门槛效应,主要体现为如下两点：首先,金融周期处于膨胀期、平稳期和萧条期时金融波动对经济增长会产生负向影响,但从影响系数值的大小来看,处于膨胀期时最大,是后两者的2倍之多,处于平稳期时最小且并不显著;其次,分区域的稳健性检验表明,金融发展水平高的区域双重门槛值出现得早,且两个门槛值间的区间要比金融发展水平低的区域宽28%。这些结论说明,经济增长对于金融波动的容忍弹性会随着金融周期所处的阶段而变化,金融发展水平的提高会放大经济增长对金融波动的容忍区间,但也会加速金融周期处于膨胀期时爆发金融危机的可能性,这使得当前我国存在着进一步发展金融水平和严控金融风险的矛盾,对此本文也提出了相应的政策建议。     

货币国际化、金融稳定与储备需求

本文基于央行应对银行挤兑，卖出储备干预外汇市场维持固定汇率的理论模型，提出货币国际化、金融稳定和外汇储备需求规模变动的实证检验假设，采用固定效应面板数据回归发现，与外汇储备的进口交易需求相比，当前金融稳定动机更能解释处于动态转变过程中的外汇储备规模需求；一国货币国际化程度越高，出于金融稳定动机的储备规模需求越小；非国际货币发行国储备规模在1993-2001年经历了从进口交易需求到金融稳定需求的转型；国际货币发行国储备规模则主要由金融稳定因素驱动。金融危机前后，外汇储备规模变动的金融稳定驱动因素更加明显。因此，适度规模外汇储备有助于金融稳定，金融稳定为实体经济创造良好发展环境，经济实力增长有助于顺利实施货币国际化进程。而货币高度国际化后，将会逐步减少外汇储备的金融稳定需求。     

金融稳定与金融竞争力协同效应测度及其国际比较研究

在金融市场的发展过程中,政府以稳定为主与市场以竞争为主的导向之间存在对立统一关系,而这种对立统一的关系在管理中则需要实现两者的协同,方能产生协同效应。根据金融稳定与金融协同的内涵,分别构建评价金融稳定与金融竞争力的指标体系,利用典型相关对两者的协调性进行研究;同时以典型相关中的典型变量为基础,建立金融稳定与金融竞争力耦合度函数,利用耦合度函数对50个国家的协调度进行测度并进行国际比较,结果表明:金融稳定与金融竞争力具有高度相关性;金融稳定与金融竞争力协同效应分布和经济发展水平密切相关;金融稳定与金融竞争力的滞后表现形式多样。     

On moment stability properties for a class of state-dependent stochastic networks

We consider a class of stochastic networks with state-dependent arrival and service rates. The state dependency is described via multi-dimensional birth/death processes, where the birth/death rates are dependent upon the current population size in the system. Under the uniform (in state) stability condition, we establish several moment stability properties of the system:

• (i) 

  the existence of a moment generating function in a neighborhood of zero, with respect to the unique invariant measure of the state process;

• (ii) 

  the convergence of the expected value of unbounded functionals of the state process to the expectation under the invariant measure, at an exponential rate;

• (iii) 

  uniform (in time and initial condition) estimates on exponential moments of the process;

• (iv) 

  growth estimates of polynomial moments of the process as a function of the initial conditions.

Our approach provides elementary proofs of these stability properties without resorting to the convergence of the scaled process to a stable fluid limit model.     

Randomized p-values for multiple testing of composite null hypotheses

We are considered with the problem of m simultaneous statistical test problems with composite null hypotheses. Usually, marginal p-values are computed under least favorable parameter configurations (LFCs), thus being over-conservative under non-LFCs. Our proposed randomized p-value leads to a tighter exhaustion of the marginal (local) significance level. In turn, it is stochastically larger than the LFC-based p-value under alternatives. While these distributional properties are typically nonsensical for m  =1, the exhaustion of the local significance level is extremely helpful for cases with m>1$m>1$ in connection with data-adaptive multiple tests as we will demonstrate by considering multiple one-sided tests for Gaussian means.     

Developing a new BIC for detecting change-points

Usual derivation of BIC for the marginal likelihood of a model or hypothesis via Laplace approximation does not hold for a change-point which is a discrete parameter. We provide an analogue l BIC, which is a lower bound to the marginal likelihood of a model with change points and has an approximation error up to O_p(1) like standard Schwartz BIC. Several applications are provided covering simulated r.v.'s and real financial figures on short-term interest rate.     

On-Line Learning for the Infinite Hidden Markov Model

We develop a sequential Monte Carlo algorithm for the infinite hidden Markov model (iHMM) that allows us to perform on-line inferences on both system states and structural (static) parameters. The algorithm described here provides a natural alternative to Markov chain Monte Carlo samplers previously developed for the iHMM, and is particularly helpful in applications where data is collected sequentially and model parameters need to be continuously updated. We illustrate our approach in the context of both a simulation study and a financial application.     

中国IFDI统计现状分析与评价

内向外商直接投资（IFDI）是反映中国经济发展的重要指标,本文试图以国际标准来研究中国IFDI统计的现状。文章首先分析了《FDI基准定义（第四版）》（BD4）关于统计对象、统计内容和统计方法等方面的建议,然后从制度基础和实际数据分析了中国IFDI统计的现状,进而根据BD4对中国IFDI统计现状进行了评价。本文对中国IFDI统计的研究可以为客观认识中国IFDI现状,完善中国IFDI统计提供参考。     

Consistency of Bayesian linear model selection with a growing number of parameters

Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesian approaches, which provide a stochastic search of informative variables, have gained popularity. In this paper, we will study the asymptotic properties related to Bayesian model selection when the model dimension p is growing with the sample size n. We consider p≤n and provide sufficient conditions under which: (1) with large probability, the posterior probability of the true model (from which samples are drawn) uniformly dominates the posterior probability of any incorrect models; and (2) the posterior probability of the true model converges to one in probability. Both (1) and (2) guarantee that the true model will be selected under a Bayesian framework. We also demonstrate several situations when (1) holds but (2) fails, which illustrates the difference between these two properties. Finally, we generalize our results to include g-priors, and provide simulation examples to illustrate the main results.     

Probabilistic Approach to Solution of the Neumann Problem for Some Nonlinear Equation

In this article we will consider the Neumann boundary-value problem for the nonlinear Helmholtz equation ? Δ?u + a?u = gexp?(u) + f₀. We will assume that there exists the solution to our problem and this permits us to construct an unbiased estimator on the trajectories of certain branching processes. To do so, we apply Green’s formula and an elliptic mean value theorem. This allows us to derive a special integral equation that gives the value of the function u(x) at the point x, with its integral over the domain D and on boundary of the domain ?D = G. The solution of the problem in the form of a mathematical expectation of some random variable is also obtained. In accordance with the probabilistic representation, a branching process is constructed and an unbiased estimator of the solution of the problem is built on its trajectories. The derived unbiased estimator has finite variance. The proposed branching process has a finite average number of branches, and easily simulated. We provide numerical results based on numerical experiments carried out with these algorithms.     

农村金融生态质量的实证分析——以安徽为个案

新农村建设离不开金融支持。在农村金融竞争不充分,金融质量不高,存在农村金融资金大量外流的背景下,寻求解决这一问题的路径成为新农村建设中必须解决的首要问题。农村金融生态是将金融学和生态学有机结合,以生态学的方法和成果研究农村金融问题,为研究农村金融问题提供了全新的视角。作为具有代表性的中部农业大省,对安徽省的农村金融生态作为个案分析和比较,能较为全面地分析了我国的农村金融生态质量,进而为新农村建设提供有效的支持。     

Developing an immigration policy for Germany on the basis of a nonparametric labor market classification

Summary: Based on a nonparametrically estimated model of labor market classifications, this paper makes suggestions for immigration policy using data from West Germany of the 1990s. It is demonstrated that nonparametric regression is feasible in higher dimensions with only a few thousand observations. In sum, labor markets are able to absorb immigrants are characterized by above average age and by professional occupations. On the other hand, labor markets for young workers in service occupations exhibit rising unemployment due to wage rigidities and are therefore not recommended for immigration. This raises a potential conflict between financing Germanys ailing social security system and protecting decreasing or rigid labor markets by immigration control.*We are grateful to Daron Acemoglu, Joshua Angrist, Herbert S. Buscher, Lars P. Feld, Simon Gächter, Ira Gang, Mark Killingsworth, Gebhardt Kirchgässner, Roger Klein, John Landon-Lane, Michael Lechner, Stephen Machin, Ruth Miquel, Michael Piore, Winfried Pohlmeier, Hans-Joachim Voth, two anonymous referees and seminar participants at Rutgers University, MIT, University of Konstanz, and University of St. Gallen for helpful comments. Markus Frölich gratefully acknowledges financial support by the Swiss National Science Foundation (NSF 4043-058311). Patrick Puhani gratefully acknowledges financial support by the Volkswagen Foundation, Hannover. We thank the Center for European Economic Research (ZEW), Mannheim, for letting us work with the full sample of the German Socio-Economic Panel. All remaining errors are our own.     

Testing of two sample proportional intensity assumption for non-homogeneous Poisson processes

For two independent non-homogeneous Poisson processes with unknown intensities we propose a test for testing the hypothesis that the ratio of the intensities is constant versus it is increasing on (0,t]. The existing test procedures for testing such relative trends are based on conditioning on the number of failures observed in (0,t] from the two processes. Our test is unconditional and is based on the original time truncated data which enables us to have meaningful asymptotics. We obtain the asymptotic null distribution (as t becomes large) of the proposed test statistic and show that the proposed test is consistent against several large classes of alternatives. It was observed by Park and Kim (IEEE. Trans. Rehab. 40 (1), 1992, 107–111) that it is difficult to distinguish between the power-law and log-linear processes for certain parameter values. We show that our test is consistent for such alternatives also.     

On the E-optimality of complete block designs under a mixed interference model

In experiments in which the response to a treatment can be affected by other treatments, the interference model with neighbor effects is usually used. It is known that circular neighbor balanced designs (CNBDs) are universally optimal under such a model if the neighbor effects are fixed (Druilhet, 1999) or random (4 and 7). However, such designs cannot exist for every combination of design parameters. In the class of block designs with the same number of treatments as experimental units per block, a CNBD cannot exist if the number of blocks, b  , is equal to p(t−1)±1$p(t-1)\pm 1$, where p is a positive integer and t is the number of treatments. Filipiak et al. (2008) gave the structure of the left-neighboring matrix of E-optimal complete block designs with p  =1 under the model with fixed neighbor effects. The purpose of this paper is to generalize E-optimality results for designs with p∈N$p\in \mathbb{N}$ assuming random neighbor effects.