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工具变量估计下细分模型与加总模型的选择AI 解读

A choice criterion is proposed for discriminating between disaggregate and aggregate models estimated by the instrumental variables method. The criterion, based on prediction errors, represents a generalization of criteria developed in the context of classical regressions models. The paper also derives general tests for aggregation bias in the instrumental variables context. The criterion and tests are applied in an analysis of U.K. employment demand. It is shown that a…

一般相对风险回归中参数可识别性与偏似然有效性札记AI 解读

In this note we deal with the question of identifiability of the regression parameter in general relative risk regression. We show that, apart from the well understood kind of nonidentifiability, as usually occurs with regression, there is a second kind of nonidentiliability of the regression parameter which may still be present even if the information matrix is positive definite. However, when this happens, the partial likelihood approach is still valid, even though some…

平稳与部分非平稳 VARMA 过程精确似然的快速算法AI 解读

An expression for the likelihood function of a stationary vector autoregressive-moving average process is developed. The expression is very efficient numerically and applies to any stationary but not necessarily invertible model. In particular, when the multivariate process is autoregressive, the exact likelihood can be evaluated with a small number of operations depending on the order of the autoregressive operator and the process dimension, but not on the size of the…

线性模型中多个离群点的识别程序AI 解读

We consider the problem of identifying and testing multiple outliers in linear models. The available outlier identification methods often do not succeed in detecting multiple outliers because they are affected by the observations they are supposed to identify. We introduce two test procedures for the detection of multiple outliers that appear to be less sensitive to this problem. Both procedures attempt to separate the data into a set of “clean” data points and a set of…

影子工资与农户家庭劳动供给：秘鲁山区的计量应用AI 解读

This paper develops a methodology for estimating structural time-allocation models for self-employed households and applies it to peasant family labour supply behaviour in the Peruvian Sierra. The opportunity costs of time, or shadow wages, of household workers are explicitly estimated from an agricultural production function. Using an instrumental variables procedure, the household's structural labour supply parameters are recovered from variation in these shadow wages. The…

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共同特征检验AI 解读

This article introduces a class of statistical tests for the hypothesis that some feature that is present in each of several variables is common to them. Features are data properties such as serial correlation, trends, seasonality, heteroscedasticity, autoregressive conditional heteroscedasticity, and excess kurtosis. A feature is detected by a hypothesis test taking no feature as the null, and a common feature is detected by a test that finds linear combinations of…

通过Gibbs采样进行变量选择AI 解读

A crucial problem in building a multiple regression model is the selection of predictors to include. The main thrust of this article is to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure entails embedding the regression setup in a hierarchical normal mixture model where latent variables are used to identify subset choices. In this framework the promising subsets of predictors can be identified as those…

多个异常值的识别AI 解读

One approach to identifying outliers is to assume that the outliers have a different distribution from the remaining observations. In this article we define outliers in terms of their position relative to the model for the good observations. The outlier identification problem is then the problem of identifying those observations that lie in a so-called outlier region. Methods based on robust statistics and outward testing are shown to have the highest possible breakdown…

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摘要暂缺

ARCH 模型中的自适应估计AI 解读

We construct efficient estimators of the identifiable parameters in a regression model when the errors follow a stationary parametric ARCH( P ) process. We do not assume a functional form for the conditional density of the errors, but do require that it be symmetric about zero. The estimators of the mean parameters are adaptive in the sense of Bickel [2]. The ARCH parameters are not jointly identifiable with the error density. We consider a reparameterization of the variance…

多类别选择模型的非参数识别与估计AI 解读

In this paper we provide conditions guaranteeing the identification of nonparametric polychotomous choice models. In these models, neither the subutility function of observable attributes nor the distribution of the unobservable random terms is specified parametrically. Sets of nonparametric functions that possess properties that are often implied by economic theory and satisfy the restrictions required to identify the models are described. We use the identification results…

作为不可观测成分的远期外汇溢价：札记AI 解读

We reconsider the signal-extraction approach to measuring premia in the pricing of forward foreign exchange, put forward by Wolff, in which the difference between the forward rate and the associated future spot rate is modeled as an autoregressive moving average (ARMA) model for the risk premium buried in a white-noise forecast error. We point out that an ARMA model for the risk premium is not always identifiable from information on the difference between the forward rate…

多元正态混合：快速且一致的矩估计方法AI 解读

A longstanding difficulty in multivariate statistics is identifying and evaluating nonnormal data structures in high dimensions with high statistical efficiency and low search effort. Here the possibilities of using sample moments to identify mixtures of multivariate normals are investigated. A particular system of moment equations is devised and then shown to be one that identifies the true mixing distribution, with some limitations (indicated in the text), and thus…

删失数据中二元生存曲线的可识别性AI 解读

We show that the survival curve is identifiable in bivariate censored data problems under weaker independence assumptions than have commonly been made. The common assumption has been mutual independence of (T 1, T 2) and (Z 1, Z 2), where (T 1, T 2) is the true survival vector, (Z 1, Z 2) is a nuisance censoring vector, and bivariate right-censored data is observed. We show that the distribution of (T 1, T 2) is identifiable under weaker, conditional independence assumptions…

特定死因失效概率与条件失效概率的使用：临床肿瘤数据示例AI 解读

Nonparametric maximum likelihood estimation of the probability of failing from a particular cause by time t in the presence of other acting causes (i.e., the cause-specific failure probability) is discussed. A commonly used incorrect approach is to take 1 minus the Kaplan-Meier (KM) estimator (1 – KM), whereby patients who fail of extraneous causes are treated as censored observations. Examples showing the extent of bias in using the 1-KM approach are presented using…

利用动态回归模型进行预测AI 解读

A Primer on ARIMA Models. A Primer on Regression Models. Rational Distributed Lag Models. Building Dynamic Regression Models: Model Identification. Building Dynamic Regression Models: Model Checking, Reformulation, and Evaluation. Intervention Analysis. Intervention and Outlier Detection and Treatment. Estimation and Forecasting. Dynamic Regression Models in a Vector ARMA Framework. Appendices. References. Index.

删失数据中双变量生存曲线的可识别性AI 解读

We show that the survival curve is identifiable in bivariate censored data problems under weaker independence assumptions than have commonly been made. The common assumption has been mutual independence of (T 1, T 2) and (Z 1, Z 2), where (T 1, T 2) is the true survival vector, (Z 1, Z 2) is a nuisance censoring vector, and bivariate right-censored data is observed. We show that the distribution of (T 1, T 2) is identifiable under weaker, conditional independence assumptions…

参数服从 ARIMA 过程的 ARIMA 模型AI 解读

This article introduces a general class of nonlinear and nonstationary time-series models whose basic scheme is an autoregressive integrated moving average (ARIMA). The main feature i s that the parameters are assumed to behave like a vector ARIMAx model in which the exogenous (x) component is represented by the regressors o f the observable process. For this class, a general algorithm of identification-estimation is outlined based on the sampling information alone. The…

精确全信息最大似然与工具变量之间的一个特殊结果AI 解读

The iterative application of an instrumental variable method to a system of simultaneous equations may exactly produce FIML, upon convergence. Instruments achieving this target do not need to be either uncorrelated with the error terms or correlated as much as possible with the replaced explanatory variables. This curious mathematical result, which contradicts some common wisdom and intuition, is proved in our paper. Our proof also provides a unified scheme that covers the…

工具变量模型中的可识别性与设定检验AI 解读

The paper develops and explores tests, based on standard moment specifications, for the identifiability of parameters apparently estimable by instrumental variables. An asymptotic expansion under standard restrictive assumptions on the error distribution suggests a correction to the asymptotic distribution. A small sampling experiment indicates that the tests are of use.

利用面板数据识别和估计含外生变量的连续时间动态系统AI 解读

This paper deals with the identification and maximum likelihood estimation of the parameters of a stochastic differential equation from discrete time sampling. Score function and maximum likelihood equations are derived explicitly. The stochastic differential equation system is extended to allow for random effects and the analysis of panel data. In addition, we investigate the identifiability of the continuous time parameters, in particular the impact of the inclusion of…