Identification robust confidence set methods for inference on parameter ratios with application to discrete choice models

We study the problem of building confidence sets for ratios of parameters, from an identification robust perspective. In particular, we address the simultaneous confidence set estimation of a finite number of ratios. Results apply to a wide class of models suitable for estimation by consistent asymptotically normal procedures. Conventional methods (e.g. the delta method) derived by excluding the parameter discontinuity regions entailed by the ratio functions and which typically yield bounded confidence limits, break down even if the sample size is large ( Dufour, 1997). One solution to this problem, which we take in this paper, is to use variants of  Fieller’s ( 1940, 1954) method. By inverting a joint test that does not require identifying the ratios, Fieller-based confidence regions are formed for the full set of ratios. Simultaneous confidence sets for individual ratios are then derived by applying projection techniques, which allow for possibly unbounded outcomes. In this paper, we provide simple explicit closed-form analytical solutions for projection-based simultaneous confidence sets, in the case of linear transformations of ratios. Our solution further provides a formal proof for the expressions in Zerbe et al. (1982) pertaining to individual ratios. We apply the geometry of quadrics as introduced by  and , in a different although related context. The confidence sets so obtained are exact if the inverted test statistic admits a tractable exact distribution, for instance in the normal linear regression context. The proposed procedures are applied and assessed via illustrative Monte Carlo and empirical examples, with a focus on discrete choice models estimated by exact or simulation-based maximum likelihood. Our results underscore the superiority of Fieller-based methods.     

Bootstrapping sequential change-point tests for linear regression

Bootstrap methods for sequential change-point detection procedures in linear regression models are proposed. The corresponding monitoring procedures are designed to control the overall significance level. The bootstrap critical values are updated constantly by including new observations obtained from the monitoring. The theoretical properties of these sequential bootstrap procedures are investigated, showing their asymptotic validity. Bootstrap and asymptotic methods are compared in a simulation study, showing that the studentized bootstrap tests hold the overall level better especially for small historic sample sizes while having a comparable power and run length.     

On reconciling purchases and sales estimates of a regional input-output table

This paper develops two alternative methods for reconciling purchases and sales estimates of a regional input-output table. The present procedures are extentions of the well known Jensen-McGaurr (J-M) procedures. It is claimed that the proposed techniques would reduce to a great extent the subjective elements in the J-M procedure and thus lower the time costs to the researcher. The present methods have been applied to the data for the Central Queensland Economy and comparisons made between the results coming out of the present procedures and the J-M procedure. In particular, it has been found that the present procedures, though less subjective in nature, produce estimates that are as reliable as the J-M estimates.     

Linear prediction and estimation methods for regression models with stationary stochastic coefficients

A linear regression model is proposed in which the coefficient vector is a weakly stationary multivariate stochastic process. The model provides a convinient representation of a general class of nonstationary processes. Prediction and estimation methods are proposed that are linear and relatively easy to compute. The proposed procedures are illustrated by estimation of time-varying GNP multipliers of several macro policy instruments over the period 1891-1970. The results are compatible with theoretical priors and suggest that predictability of policy outcomes depends on the mixture of policy instruments.     

Parametric bootstrap tests for continuous and discrete distributions

Statistical procedures based on the estimated empirical process are well known for testing goodness of fit to parametric distribution families. These methods usually are not distribution free, so that the asymptotic critical values of test statistics depend on unknown parameters. This difficulty may be overcome by the utilization of parametric bootstrap procedures. The aim of this paper is to prove a weak approximation theorem for the bootstrapped estimated empirical process under very general conditions, which allow both the most important continuous and discrete distribution families, along with most parameter estimation methods. The emphasis is on families of discrete distributions, and simulation results for families of negative binomial distributions are also presented.     

Evaluating density forecasts: forecast combinations,model mixtures,calibration and sharpness

This paper reviews current density forecast evaluation procedures, and considers a proposal that such procedures be augmented by an assessment of ‘sharpness’. This was motivated by an example in which some standard evaluation procedures using probability integral transforms cannot distinguish the ideal forecast from several competing forecasts. We show that this example has some unrealistic features from a time series forecasting perspective, and so provides insecure foundations for the argument that existing calibration procedures are inadequate in practice. Our alternative, more realistic example shows how relevant statistical methods, including information‐based methods, provide the required discrimination between competing forecasts. We introduce a new test of density forecast efficiency. Copyright © 2010 John Wiley & Sons, Ltd.     

市政道路路基施工质量控制

阐述了市政道路路基施工质量控制的关键程序及施工质量控制要点,分析了施工中易出现的问题,并提出了相应的处治方法。     

Bayesian inference for the mover–stayer model in continuous time with an application to labour market transition data

This paper presents Bayesian inference procedures for the continuous time mover–stayer model applied to labour market transition data collected in discrete time. These methods allow us to derive the probability of embeddability of the discrete‐time modelling with the continuous‐time one. A special emphasis is put on two alternative procedures, namely the importance sampling algorithm and a new Gibbs sampling algorithm. Transition intensities, proportions of stayers and functions of these parameters are then estimated with the Gibbs sampling algorithm for individual transition data coming from the French Labour Force Surveys collected over the period 1986–2000. Copyright © 2003 John Wiley & Sons, Ltd.     

技术标准参数确定方法研究

技术参数和标准界限是技术标准的重要组成部分。本文在简要介绍质量特性分类的基础上，对质量功能展开和参数设计两种技术参数设计方法进行了说明，给出了质量功能展开和参数设计的核心工具与设计步骤，并讨论了应用质量损失函数进行标准界限的设计方法，最后给出了应用实例。     

Indirect inference

In this paper we present inference methods which are based on an ‘incorrect’ criterion, in the sense that the optimization of this criterion does not directly provide a consistent estimator of the parameter of interest. Moreover, the argument of the criterion, called the auxiliary parameter, may have a larger dimension than that of the parameter of interest. A second step, based on simulations, provides a consistent and asymptotically normal estimator of the parameter of interest. Various testing procedures are also proposed. The methods described in this paper only require that the model can be simulated, therefore they should be useful for models whose complexity rules out a direct approach. Various fields of applications are suggested (microeconometrics, finance, macroeconometrics).     

How different are social choice functions: a rough sets approach

The number of different social choice procedures currently in use is vast. So is the number of criteria used in the evaluation of the procedures. We discuss several voting procedures in the light of methodology based on rough sets introduced by Pawlak. This methodology is used in identifying the crucial characteristics of various choice methods and in defining a metric for measuring the quantitative differences of choice methods.     

Nested analysis of variance: Review of methods

Summary Methods of the nested analysis of variance procedures are discussed and a formalized three-way nested model is outlined. A summary of the statistical tests for the assumptions relevant to the model is given together with an outline of the numerical methods for calculating the sum of squares, the degrees of freedom, the mean squares, the tests of significance, and the estimate of variance components. Several methods are given for obtaining confidence limits for variance components. Procedures are presented for utilizing the variance components to estimate process and/or control limits and to obtain the most efficient or optimum sampling scheme. Some differences between the nested analysis of variance model and the crossed analysis of variance model are explained.     

就地冷再生技术在改建工程中的应用

论述旧沥青路面冷再生配合比设计,分析施工程序和方法,并指出了施工控制要点。     

Short run and long run causality in time series: inference

We propose methods for testing hypothesis of non-causality at various horizons, as defined in Dufour and Renault (Econometrica 66, (1998) 1099–1125). We study in detail the case of VAR models and we propose linear methods based on running vector autoregressions at different horizons. While the hypotheses considered are nonlinear, the proposed methods only require linear regression techniques as well as standard Gaussian asymptotic distributional theory. Bootstrap procedures are also considered. For the case of integrated processes, we propose extended regression methods that avoid nonstandard asymptotics. The methods are applied to a VAR model of the US economy.     

Comparison of seasonal estimation methods in multi-item short-term forecasting

This paper addresses the issue of estimating seasonal indices for multi-item, short-term forecasting, based upon both individual time series estimates and groups of similar time series. This development of the joint use of individual and group seasonal estimation is extended in two directions. One class of methods is derived from the procedures developed for combining forecasts. The second employs the general class of Stein Rules to obtain shrinkage estimates of seasonal components. A comparative evaluation has been undertaken of several versions of these methods, based upon a sample of retail sales data. The results favour these newly developed methods and provide some interesting insights for practical implementation.     

重视油罐清洗工作的安全管理

介绍了储油罐沉积物3种清洗方法的适用范围、操作步骤以及在作业过程中应采取的安全措施,从而实现油罐的安全清洗。     

On fitting distributed lag models subject to polynomial restrictions

A methodology is presented for fitting distributed lag models with polynomial restrictions on the lag coefficients. The model incorporates autoregressive residuals. Orthogonal methods are employed so that the procedures are numerically sound. Furthermore, these have the effect of allowing for inference to be made about the three integer parameters in the model: (i) the length of the lag, (ii) the degree of the polynomial, and (iii) the order of the autoregression. The methodology is applied to the extended Almon data. This analysis suggests that estimation of polynomial distributed lags is highly sensitive to autoregressive disturbances. This underlines the importance of modeling the disturbances.     

On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks

Likelihoods and posteriors of instrumental variable (IV) regression models with strong endogeneity and/or weak instruments may exhibit rather non-elliptical contours in the parameter space. This may seriously affect inference based on Bayesian credible sets. When approximating posterior probabilities and marginal densities using Monte Carlo integration methods like importance sampling or Markov chain Monte Carlo procedures the speed of the algorithm and the quality of the results greatly depend on the choice of the importance or candidate density. Such a density has to be ‘close’ to the target density in order to yield accurate results with numerically efficient sampling. For this purpose we introduce neural networks which seem to be natural importance or candidate densities, as they have a universal approximation property and are easy to sample from. A key step in the proposed class of methods is the construction of a neural network that approximates the target density. The methods are tested on a set of illustrative IV regression models. The results indicate the possible usefulness of the neural network approach.     

Non‐parametric Analysis of Gap Times for Multiple Event Data: An Overview

Multiple event data are frequently encountered in medical follow‐up, engineering and other applications when the multiple events are considered as the major outcomes. They may be repetitions of the same event (recurrent events) or may be events of different nature. Times between successive events (gap times) are often of direct interest in these applications. The stochastic‐ordering structure and within‐subject dependence of multiple events generate statistical challenges for analysing such data, including induced dependent censoring and non‐identifiability of marginal distributions. This paper provides an overview of a class of existing non‐parametric estimation methods for gap time distributions for various types of multiple event data, where sampling bias from induced dependent censoring is effectively adjusted. We discuss the statistical issues in gap time analysis, describe the estimation procedures and illustrate the methods with a comparative simulation study and a real application to an AIDS clinical trial. A comprehensive understanding of challenges and available methods for non‐parametric analysis can be useful because there is no existing standard approach to identifying an appropriate gap time method that can be used to address research question of interest. The methods discussed in this review would allow practitioners to effectively handle a variety of real‐world multiple event data.     

On input-output type output multipliers from incomplete information

Incomplete knowledge of data usually hinders the establishment of detailed input-output tables. It is for this reason that up-dating procedures (RAS) as well as short-cut methods have been developed. In this paper the short-cut output multiplier formula by Drake (1976) is compared with the output multiplier involved in the RAS procedures. It will turn out that both these output multiplier estimates are two special cases of a more general class of estimating procedures. It is demonstrated by an empirical example how this generalized procedure should be applied in practice. The kind of reasoning underlying this paper can be extended to other problems of input-output analysis as well.