Bounds on Parameters in Panel Dynamic Discrete Choice Models

Identification of dynamic nonlinear panel data models is an important and delicate problem in econometrics. In this paper we provide insights that shed light on the identification of parameters of some commonly used models. Using these insights, we are able to show through simple calculations that point identification often fails in these models. On the other hand, these calculations also suggest that the model restricts the parameter to lie in a region that is very small in many cases, and the failure of point identification may, therefore, be of little practical importance in those cases. Although the emphasis is on identification, our techniques are constructive in that they can easily form the basis for consistent estimates of the identified sets.     

边限检验理论及几点讨论

检验经济变量之间长期关系的协整技术要求变量是同阶单整的,这不可避免地涉及一定程度的预检验问题,而预检验问题会增加变量间长期关系分析的不确定性。当不能确定变量的单整类型时,边限检验理论提出了一个可以直接检验一个变量和一组解释变量之间长期关系的新方法。在介绍了边限检验方法中基本的VAR模型和假设及边限检验方法中用到的重要统计量——Wald统计量和T统计量及它们各自的渐近分布形式后,说明了边限检验理论在理论和实际运用当中需要注意的几个问题,最后通过实例分析说明了边限检验理论的运用。     

SELECTING THE t BEST POPULATIONS — SCALE PARAMETER CASE

Selection from k independent populations of the t (< k) populations with the smallest scale parameters has been considered under the Indifference Zone approach by Bechhofer k Sobel (1954). The same problem has been considered under the Subset Selection approach by Gupta & Sobel (1962a) for the normal variances case and by Carroll, Gupta & Huang (1975) for the more general case of stochastically increasing distributions. This paper uses the Subset Selection approach to place confidence bounds on the probability of selecting all “good” populations, or only “good” populations, for the Case of scale parameters, where a “good” population is defined to have one of the t smallest scale parameters. This is an extension of the location parameter results obtained by Bofinger & Mengersen (1986). Special results are obtained for the case of selecting normal populations based on variances and the necessary tables are presented.     

An improved algorithm for reliability bounds of multistate networks

Abstract

Indirect approaches based on minimal path vectors (d-MPs) and/or minimal cut vectors (d-MCs) are reported to be efficient for the reliability evaluation of multistate networks. Given the need to find more efficient evaluation methods for exact reliability, such techniques may still be cumbersome when the size of the network and the states of component are relatively large. Alternatively, computing reliability bounds can provide approximated reliability with less computational effort. Based on Bai’s exact and indirect reliability evaluation algorithm, an improved algorithm is proposed in this study, which provides sequences of upper and lower reliability bounds of multistate networks. Novel heuristic rules with a pre-specified value to filter less important sets of unspecified states are then developed and incorporated into the algorithm. Computational experiments comparing the proposed methods with an existing direct bounding algorithm show that the new algorithms can provide tight reliability bounds with less computational effort, especially for the proposed algorithm with heuristic L1.     

A fast general extension algorithm of Latin hypercube sampling

A fast general extension algorithm of Latin hypercube sampling (LHS) is proposed, which reduces the time consumption of basic general extension and preserves the most original sampling points. The extension algorithm starts with an original LHS of size m and constructs a new LHS of size m?+?n that remains the original points. This algorithm is the further research of basic general extension, which cost too much time to get the new LHS. During selecting the original sampling points to preserve, time consumption is cut from three aspects. The first measure of the proposed algorithm is to select isolated vertices and divide the adjacent matrix into blocks. Secondly, the relationship of original LHS structure and new LHS structure is discussed. Thirdly, the upper and lower bounds help reduce the time consumption. The proposed algorithm is applied for two functions to demonstrate the effectiveness.     

A new diagnostic tool for VARMA(p,q) models

A new diagnostic method for VARMA(p,q) time series models is introduced. The procedure is based on a statistic that generalizes to a multivariate setting the properties of the usual univariate ARMA(p,q) residual correlations. A multiple version of the cumulative periodogram statistic is also suggested. Simulation studies and one real data application are presented.     

ON BAYES PREDICTION OF FUTURE MEDIAN

An expression for the Bayesian predictive survival function of the median of a set of future observations is obtained whether its size is assumed to be odd or even. Both of the informative and future samples are drawn from a population whose distribution is a general class that includes several distributions used in life testing (and other areas as well) such as the Weibull (including the exponential and Rayleigh), compound Weibull (including the compound exponential and compound Rayleigh), Pareto, beta, Gompertz and compound Gompertz, among other distributions. A general proper (conjugate) prior density function is used to cover most prior distributions that have been used in literature. Applications to the Weibull, exponential and Rayleigh models are illustrated.     

A note on bounding monte carlo variances michael lavine

A density bounded class P of probability distributions on a space χ is the set of all probability distributions corresponding to probability densities bounded below by a given subprob-ability density and bounded above by a given superprobability density. Density bounded classes arise in robust Bayesian analysis (Lavine 1991) and also in Monte Carlo integration (Fishman Granovsky and Rubin 1989). Finding upper and lower bounds on the variance over all p? P allows one to bound the Monte Carlo variance. Fishman Granovsky and Rubin (1989) find bounds on the variance over all p ? P and also find the densities in P achieving those bounds in the case where χ is discrete; that is, where P is actually a set of probability mass functions. This article generalizes their result by showing how to bound the variance and find the densities achieving the bounds when χ is continuous.     

Simultaneous upper confidence bounds for distances from the best two-parameter exponentlal distribution

Consider k independent exponential distributions possibly with different location parameters and a common scale parameter. If the best population is defined to be the one having the largest mean or equivalently having the largest location parameter, we then derive a set of simultaneous upper confidence bounds for all distances of the means from the largest one. These bounds not only can serve as confidence intervals for all distances from the largest parameter but they also can be used to identify the best population. Relationships to ranking and selection procedures are pointed out. Cases in which scale parameters are known or unknown and samples are complete or type II censored are considered. Tables to implement this procedure are given.