The conservative equal costs rule, the serial cost sharing rule and the pivotal mechanism: asymptotic welfare loss comparisons for the case of an excludable public project

We examine the asymptotic behavior of two strategyproof mechanisms discussed by Moulin for public goods – the conservative equal costs rule (CER) and the serial cost sharing rule (SCSR) – and compare their performance to that of the pivotal mechanism (PM) from the Clarke–Groves family. Allowing the individuals’ valuations for an excludable public project to be random variables, we show under very general assumptions that expected welfare loss generated by the CER, as the size of the population increases, becomes arbitrarily large. However, all moments of the SCSR’s random welfare loss asymptotically converge to zero. The PM does better than the SCSR, with its welfare loss converging even more rapidly to zero.     

加权Hardy空间上算子序列的收敛性

设D为复平面C中的开单位圆盘，φ：D→D解析，ψ：D→C，Cψ，φ是H^2（β）上的加权复合算子，S是加权移位算子，该文通过讨论算子序列{S^＊nCψ，φS^n}n=1^∞与{S^＊nCψ，φ^＊S^n}n=1^∞的收敛性来研究H^2（β）上加权复合算子Cψ，φ的渐近Toeplitz性．     

Short-time at-the-money skew and rough fractional volatility

The Black–Scholes implied volatility skew at the money of SPX options is known to obey a power law with respect to the time to maturity. We construct a model of the underlying asset price process which is dynamically consistent to the power law. The volatility process of the model is driven by a fractional Brownian motion with Hurst parameter less than half. The fractional Brownian motion is correlated with a Brownian motion which drives the asset price process. We derive an asymptotic expansion of the implied volatility as the time to maturity tends to zero. For this purpose, we introduce a new approach to validate such an expansion, which enables us to treat more general models than in the literature. The local-stochastic volatility model is treated as well under an essentially minimal regularity condition in order to show such a standard model cannot be dynamically consistent to the power law.     

非线性时序模型LM检验的两类临界值检验统计功效比较

本文基于模拟方法比较了不同非线性时序模型的LM检验的功效和规模，同时也考虑一般化线性检验BDS检验参与比较，目的在于探讨蒙特一卡洛渐近法检验与自举法（bootstrap）检验的两类临界值的统计功效何者更为有效。通过实证与对比分析，结果表明，当样本小于200或自回归系数接近单位根，或者线性性检验是ARCHT或BDS时，就可以考虑应用自举法临界值而非渐近临界值。而且还发现，BDS检验仅在一般性上优于LM检验。     

Growth Optimal Investment Strategy: The Impact of Reallocation Frequency and Heavy Tails

The strategy to maximize the long‐term growth rate of final wealth (maximum expected log strategy, maximum geometric mean strategy, Kelly criterion) is based on probability theoretic underpinnings and has asymptotic optimality properties. This article reviews the allocation of wealth in a two‐asset economy with one risky asset and a risk‐free asset. It is also shown that the optimal fraction to be invested in the risky asset (i) depends on the length of the basic return period and (ii) is lower for heavy‐tailed log returns than for light‐tailed log returns.     

Efficient rank tests for semiparametric competing risk models

We consider a semiparametric competing risk model given by k independent survival times. The paper offers an asymptotic treatment of tests for the semiparametric null hypothesis of equality of the underlying risks. It turns out that modified rank tests are asymptotically efficient for certain semiparametric submodels, where the baseline hazard is a nuisance parameter. In addition, the asymptotic relative efficiency of the present tests is derived. A comparison of asymptotic power functions can then be used to classify various tests proposed earlier in the literature. For instance a chi-square type test is efficient for proportional hazards. Data driven tests of likelihood ratio type are proposed for cones of alternatives. We will consider certain stochastically increasing alternatives as a special example. The paper shows how the concept of local asymptotic normality of Le Cam works for hazard oriented models.     

On the finite sample behavior of testing whether new is better than used of a specified age

Hollander, Park and Proschan (1986) proposed a test of new is better than used of a specified age. It is based on large sample normality of the test statistic. There is, however, no study in the literature on its actual size for small and moderate sample sizes. To shed some lights on this, the results of a Monte Carlo simulation study as well as two real data examples are reported and these indicate that the test can have a quite liberal size, especially for small to moderate sample sizes. In order to improve on this weakness, a modified test is proposed and studied. It is noticed that this modified test seems to over-correct the original test to an extent that it becomes unduly conservative sometimes. Hence we propose another modification that combines the original test and the modified test turns out to have its size quite close to the nominal level and is therefore preferable to both the original and modified tests.     

Poisson Regression for Clustered Data

We compare five methods for parameter estimation of a Poisson regression model for clustered data: (1) ordinary (naive) Poisson regression (OP), which ignores intracluster correlation, (2) Poisson regression with fixed cluster‐specific intercepts (FI), (3) a generalized estimating equations (GEE) approach with an equi‐correlation matrix, (4) an exact generalized estimating equations (EGEE) approach with an exact covariance matrix, and (5) maximum likelihood (ML). Special attention is given to the simplest case of the Poisson regression with a cluster‐specific intercept random when the asymptotic covariance matrix is obtained in closed form. We prove that methods 1–5, except GEE, produce the same estimates of slope coefficients for balanced data (an equal number of observations in each cluster and the same vectors of covariates). All five methods lead to consistent estimates of slopes but have different efficiency for unbalanced data design. It is shown that the FI approach can be derived as a limiting case of maximum likelihood when the cluster variance increases to infinity. Exact asymptotic covariance matrices are derived for each method. In terms of asymptotic efficiency, the methods split into two groups: OP & GEE and EGEE & FI & ML. Thus, contrary to the existing practice, there is no advantage in using GEE because it is substantially outperformed by EGEE and FI. In particular, EGEE does not require integration and is easy to compute with the asymptotic variances of the slope estimates close to those of the ML.     

Voluntary Disclosure of Nonproprietary Information: A Complete Equilibrium Characterization

The so-called disclosure principle is a 'puzzle' in the accounting literature: Game theoretic models of financial markets show that in equilibrium firms should disclose all their private information. Yet, the result is not convincing. Researchers have therefore built sophisticated models in order to demonstrate for which reasons the disclosure principle might fail. This note shows that even in the original model there are multiple equilibria. In those equilibria good types disclose and bad types do not. The commonly known full disclosure equilibrium is a limit point of the equilibrium set.     

The Limit Distribution of the Bootstrap for the Unit Root Test Statistic when the Residuals are Dependent

In this article, the unit root test for the AR(1) model with dependent residuals is considered. We adopt a bootstrap procedure to bootstrap the residuals with bootstrap sample size m less than the size n of the original sample. Under the assumptions that m → ∞ and m/n → 0, the convergence in probability of the bootstrap distribution function is established. Research supported by National Natural Science Foundation of China (No. 10471126)