基于ECM模型的城镇居民收入与消费问题的实证研究——以杭州为例

本文根据协整理论与误差修正模型,利用1978-2007年度数据以杭州市为例对城镇居民收入与消费的协整关系进行了实证分析。结果表明:杭州市城镇居民家庭的人均消费与收入之间存在单向长期稳定的因果关系,即人均可支配收入变动是影响人均消费变动的原因,而人均消费变动不是影响人均可支配收入变动的原因。因此,从长期来看,要刺激城镇居民消费来拉动经济增长,必须增加城镇居民的可支配收入。     

The cost of not knowing the radius

Robust Statistics considers the quality of statistical decisions in the presence of deviations from the ideal model, where deviations are modelled by neighborhoods of a certain size about the ideal model. We introduce a new concept of optimality (radius-minimaxity) if this size or radius is not precisely known: for this notion, we determine the increase of the maximum risk over the minimax risk in the case that the optimally robust estimator for the false neighborhood radius is used. The maximum increase of the relative risk is minimized in the case that the radius is known only to belong to some interval [r _l ,r _u ]. We pursue this minmax approach for a number of ideal models and a variety of neighborhoods. Also, the effect of increasing parameter dimension is studied for these models. The minimax increase of relative risk in case the radius is completely unknown, compared with that of the most robust procedure, is 18.1% versus 57.1% and 50.5% versus 172.1% for one-dimensional location and scale, respectively, and less than 1/3 in other typical contamination models. In most models considered so far, the radius needs to be specified only up to a factor , in order to keep the increase of relative risk below 12.5%, provided that the radius–minimax robust estimator is employed. The least favorable radii leading to the radius–minimax estimators turn out small: 5–6% contamination, at sample size 100.      

上海证券市场A市股盈率与收益率相关性分析

在西方发达国家成熟的证券市场中,低市盈率股票的收益率大于高市盈率股票收益率的现象持续存在,市盈率指标可以作为投资选择的一个依据。文章通过上海证券市场A股市盈率与二级市场收益率相关性分析,指出我国证券市场价值投资理念已初步凸显的结论。     

牛津版《技术史》及其中译本大厦是怎样架构起来的

牛津版《技术史》是1931年在伦敦召开的第二届国际科学史大会之后,掀起科学和技术与社会结合思潮的主要产物之一。这个思潮的主要代表人物成为后来英国左翼的中坚力量,对于科学史、技术史、科学学、科学社会学和科学管理学的产生起了奠基作用。威廉斯作为该书主编,为中译本写了序言为新中译本的出版创造了一定条件。堪与原版本相比拟和装订精良的中译本的问世,说明我国的科技、翻译和出版的水平有了很大发展。     

Stepwise Multiple Testing as Formalized Data Snooping

In econometric applications, often several hypothesis tests are carried out at once. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. This paper suggests a stepwise multiple testing procedure that asymptotically controls the familywise error rate. Compared to related single‐step methods, the procedure is more powerful and often will reject more false hypotheses. In addition, we advocate the use of studentization when feasible. Unlike some stepwise methods, the method implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect false hypotheses. The methodology is presented in the context of comparing several strategies to a common benchmark. However, our ideas can easily be extended to other contexts where multiple tests occur. Some simulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.     

TSNP: A two-stage nonparametric phase I/II clinical trial design for immunotherapy

We develop a transparent and efficient two-stage nonparametric (TSNP) phase I/II clinical trial design to identify the optimal biological dose (OBD) of immunotherapy. We propose a nonparametric approach to derive the closed-form estimates of the joint toxicity–efficacy response probabilities under the monotonic increasing constraint for the toxicity outcomes. These estimates are then used to measure the immunotherapy's toxicity–efficacy profiles at each dose and guide the dose finding. The first stage of the design aims to explore the toxicity profile. The second stage aims to find the OBD, which can achieve the optimal therapeutic effect by considering both the toxicity and efficacy outcomes through a utility function. The closed-form estimates and concise dose-finding algorithm make the TSNP design appealing in practice. The simulation results show that the TSNP design yields superior operating characteristics than the existing Bayesian parametric designs. User-friendly computational software is freely available to facilitate the application of the proposed design to real trials. We provide comprehensive illustrations and examples about implementing the proposed design with associated software.     

Testing the Martingale Hypothesis

We propose new tests of the martingale hypothesis based on generalized versions of the Kolmogorov–Smirnov and Cramér–von Mises tests. The tests are distribution-free and allow for a weak drift in the null model. The methods do not require either smoothing parameters or bootstrap resampling for their implementation and so are well suited to practical work. The article develops limit theory for the tests under the null and shows that the tests are consistent against a wide class of nonlinear, nonmartingale processes. Simulations show that the tests have good finite sample properties in comparison with other tests particularly under conditional heteroscedasticity and mildly explosive alternatives. An empirical application to major exchange rate data finds strong evidence in favor of the martingale hypothesis, confirming much earlier research.     

Efficient Estimation for Semi-varying Coefficient Model with An Invertible Linear Process Error

For semi-varying-coefficient model with an invertible linear process error, we propose an efficient estimator procedure. This procedure is based on a pre-whitening transformation of the dependent variable that must be estimated from the data. We establish the proposed estimations’ asymptotic normalities, and assess their finite sample performance. Monte Carlo simulation suggest that the efficiency gain can be achieved in moderate-sized samples.     

Optimal partially replicated parallel-flats designs for 2×3 mixed factorial experiments

Augmenting additional repeated runs to an unreplicated factorial design provides an economical scheme of obtaining an unbiased estimate for the error variance based on pure replicates. The augmented partially replicated design usually performs satisfactorily in identifying truly active effects regardless of whether the effect sparsity principle holds. Liao and Chai (2009) proposed a set of sufficient conditions for a partially replicated two-level design to be D-optimal over the class of parallel-flats designs. In this article, we generalize their result to the 2^n₁×3^n₂ mixed factorial under D-, A- and E-optimality, and include the 2ⁿ and 3ⁿ symmetric factorials as special cases. In addition, some examples are given to illustrate the main results.     

Evaluation of realized volatility predictions from models with leptokurtically and asymmetrically distributed forecast errors

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized volatility. The forecasting evaluation is valid for standardized forecast errors with leptokurtic distribution as well as with leptokurtic and asymmetric distributions. Additionally, the widely applied forecasting evaluation function, the predicted mean-squared error, fails to select the adequate model in the case of models with residuals that are leptokurtically and asymmetrically distributed. Hence, the realized volatility forecasting evaluation should be based on the standardized forecast errors instead of their unstandardized version.