Unit Root Tests in the Presence of Multi-Variance Break and Level Shifts That Have Power Against the Piecewise Stationary Alternative

Structural breaks in the level as well as in the volatility have often been exhibited in economic time series. In this paper, we propose new unit root tests when a time series has multiple shifts in its level and the corresponding volatility. The proposed tests are Lagrangian multiplier type tests based on the residual's marginal likelihood which is free from the nuisance mean parameters. The limiting null distributions of the proposed tests are the χ²distributions, and are affected not by the size and the location of breaks but only by the number of breaks.

We set the structural breaks under both the null and the alternative hypotheses to relieve a possible vagueness in interpreting test results in empirical work. The null hypothesis implies a unit root process with level shifts and the alternative connotes a stationary process with level shifts. The Monte Carlo simulation shows that our tests are locally more powerful than the OLSE-based tests, and that the powers of our tests, in a fixed time span, remain stable regardless the number of breaks. In our application, we employ the data which are analyzed by Perron (1990), and some results differ from those of Perron's (1990).     

大学生关于能力与热情的内隐混合性别刻板印象：来自IAT的证据

目的：考察大学生关于能力与热情的内隐混合性别刻板印象。方法：从某高校选取90名大学生,采用两个内隐联想测验（IAT）探讨大学生是否对男女两性存在热情和能力两个内容维度的混合刻板印象。结果：与女性相比,大学生更多地将消极热情和积极能力与男性相联系;与男性相比,被试更多地将积极热情和消极能力与女性相联系。结论：被试对男性存在着消极热情—积极能力的内隐混合性别刻板印象,对女性存在积极热情—消极能力的内隐混合性别刻板印象。     

信息产业对经济增长的效用研究

为量化信息产业对经济增长的作用,通过对投入产出模型进行结构化分解,测算了2007和2012年我国信息产业的乘数效应、反馈效应和溢出效应。结果表明,溢出效应最为显著,这说明信息产业投入越高,对其他产业的贡献也越高。同时,基于2001—2012年29个省份面板数据,采用向量自回归模型和面板Granger因果检验,分析了信息产业对经济增长的效用机制,验证了信息产业对经济增长的直接贡献和间接贡献。结果表明,信息产业和国内生产总值(GDP)之间存在协整关系,信息产业与经济增长双向相关性显著,经济增长是信息产业增长的同质Granger因,各省份GDP以相同的效用机制影响信息产业;信息产业是GDP的异质Granger因,各省份信息产业的增长以不同机制影响GDP增长。     

Disappointment without prior expectation: a unifying perspective on decision under risk

The central idea of Disappointment theory is that an individual forms an expectation about a risky alternative, and may experience disappointment if the outcome eventually obtained falls short of the expectation. We abandon the hypothesis of a well-defined prior expectation: disappointment feelings may arise from comparing the outcome received with anyof the gamble’s outcomes that the individual failed to get. This leads to a new, general form of Disappointment model. It encompasses Rank Dependent Utility with an explicit one-parameter probability transformation, and Risk-Value models with a generic risk measure including Variance, providing a unifying behavioral foundation for these models. JEL Classification D80 . D81     

A simulation study on power comparisons for group sequential tests of non-parametric statistics

In this article, a group sequential test (GST) of non-parametric statistics for survival data is briefly reviewed. An asymptotic joint distribution of the test statistics, obtained after each interim analysis, is given to illustrate the applicability of the critical values of the GST procedures. It should be noted that censored observations are generally seen in survival data. Therefore, if one makes power calculations irrespective of censoring, reliable results may not be achieved, due to the lack of information about the censoring structure. A wide simulation study, covering different censoring rates and tied observations, is conducted to make the power comparisons under various scenarios. The simulation results are interpreted and compared with the results obtained by using power analysis and sample size (PASS) software.     

Measuring non-linear dependence for two random variables distributed along a curve

We propose new dependence measures for two real random variables not necessarily linearly related. Covariance and linear correlation are expressed in terms of principal components and are generalized for variables distributed along a curve. Properties of these measures are discussed. The new measures are estimated using principal curves and are computed for simulated and real data sets. Finally, we present several statistical applications for the new dependence measures.     

A survey of permutation tests for censored survival data

The present paper surveys tests with censored sur-vival data for 2-and k-samples and for association by a framework which classifies them into complete or restric-ted permutation tests and into tests based on U-or Savage-scores. The formulae of the resulting twelve tests are briefly described for quick reference. Some of the tests have been applied frequently in the past as the tests by Mantel, Breslow or Gehan; others have been developed rather recently, partly by the author. The concluding discussion presents the results of a simulation study, clarifying similarities and differences of the restricted and complete permutation approach, and deals with rela-tive efficiencies of the two scoring systems.     

Group sequential x2 statistic for testing the difference of two mean vectors in clinical trials

In this study we discuss the group sequential procedures for comparing two treatments based on multivariate observations in clinical trials. Also we suppose that a response vector on each of two treatments has a multivariate normal distribution with unknown covariance matrix. Then we propose a group sequential x² statistic in order to carry out repeated significance test for hypothesis of no difference between two population mean vectors. In order to realize the group sequential test where average sample number is reduced, we propose another modified group sequential x² statistic by extension of Jennison and Turnbull ( 1991 ). After construction of repeated confidence boundaries for making the repeated significance test, we compare two group sequential procedures based on two statistics regarding the average sample number and the power of the test in the simulations.     

On concurrence of several linear regressions and applications to problems of enzyme kinetics

In comparing several regressions E(y_ij) =α_i + β_ix_ij i = 1, 2, ..., k, j = 1,2, ..., n_i, researchers are generally interested in the following five problems: whether they have (1) equal slope, (2) equal intercept, (3) coincidence, (4) common intersection on X-axis, and (5) common intersection on (X,Y) - plane. Problems (1) - (3) can be put into the framework of the general linear hypothesis and the F-test can be used. However, problems (4) and (5) cannot be put into the general linear hypothesis because they are ratios of parameters. Hence, in this paper we consider the generalized likelihood ratio test for hypothesis testing. An application to an enzyme kinetics problem in Aniline Metabolism is demonstrated     

Using adaptive weighted least squares to reduce the lengths of confidence intervals

The author proposes an adaptive method which produces confidence intervals that are often narrower than those obtained by the traditional procedures. The proposed methods use both a weighted least squares approach to reduce the length of the confidence interval and a permutation technique to insure that its coverage probability is near the nominal level. The author reports simulations comparing the adaptive intervals to the traditional ones for the difference between two population means, for the slope in a simple linear regression, and for the slope in a multiple linear regression having two correlated exogenous variables. He is led to recommend adaptive intervals for sample sizes superior to 40 when the error distribution is not known to be Gaussian.