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测量可交换性与正态单因子模型AI 解读

The one‐factor model restricts the covariance structure of the observed variables on the basis of assumptions about their relationship with an unobserved variable. It is hard to justify these assumptions on substantive or empirical grounds. In this paper, alternative measurement models are proposed that are based on exchangeability of variables after admissible scale transformations. They provide an alternative interpretation of the model and do not involve unobserved…

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This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of nonstationarity in the data. We refer to it as PANIC-Panel Analysis of Nonstationarity in Idiosyncratic and Common components. PANIC can detect whether the nonstationarity in a series is pervasive, or variable-specific, or both. It can determine the number of independent stochastic trends driving the common factors. PANIC also permits valid…

病例对照研究中的纵向数据分析AI 解读

Case‐control studies for longitudinal data are considered. Among repeated binary measurements of disease status in each subject, the exposure levels of risk factors for all diseased cases are identified and the exposure levels for only a small fraction of disease‐free cases, to be regarded as controls, are identified. Case‐control studies for longitudinal data bring about economies in cost and time when the disease is rare and when assessing the exposure level of risk…

空间相关数据的贝叶斯因子分析及地区物质贫困指数构建AI 解读

This article describes a Bayesian hierarchical model for factor analysis of spatially correlated multivariate data. The first level specifies, for each area on a map, the distribution of a vector of manifest variables conditional on an underlying latent factor; at the second level, the area-specific latent factors have a joint distribution that incorporates spatial correlation. The framework allows for both marginal and conditional (e.g., conditional autoregressive)…

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相关残差多因子模型的识别AI 解读

We specify some conditions for the identification of a multi‐factor model with correlated residuals, uncorrelated factors and zero restrictions in the factor loadings. These conditions are derived from the results of Stanghellini (1997) and Vicard (2000) which deal with single‐factor models with zero restrictions in the concentration matrix. Like these authors, we make use of the complementary graph of residuals and the conditions build on the role of odd cycles in this…

基于稳健离散度的主成分渐近分布AI 解读

Algebraically, principal components can be defined as the eigenvalues and eigenvectors of a covariance or correlation matrix, but they are statistically meaningful as successive projections of the multivariate data in the direction of maximal variability. An attractive alternative in robust principal component analysis is to replace the classical variability measure, i.e. variance, by a robust dispersion measure. This projection‐pursuit approach was first proposed in Li &…

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相关矩阵的主成分模型AI 解读

Distributional theory regarding principal components is less well developed for correlation matrices than it is for covariance matrices. The intent of this paper is to reduce this disparity. Methods are proposed that enable investigators to fit and to make inferences about flexible principal components models for correlation matrices. The models allow arbitrary eigenvalue multiplicities and allow the distinct eigenvalues to be modelled parametrically or nonparametrically.…

基于月度与季度序列的新经济周期同步指数AI 解读

Abstract Popular monthly coincident indices of business cycles, e.g. the composite index and the Stock–Watson coincident index, have two shortcomings. First, they ignore information contained in quarterly indicators such as real GDP. Second, they lack economic interpretation; hence the heights of peaks and the depths of troughs depend on the choice of an index. This paper extends the Stock–Watson coincident index by applying maximum likelihood factor analysis to a…

部分共同主成分子空间的加权卡方检验AI 解读

We consider tests of the null hypothesis that g covariance matrices have a partial common principal component subspace of dimension s. Our approach uses a dimensionality matrix which has its rank equal to s when the hypothesis holds. The test can then be based on a statistic computed from the eigenvalues of an estimate of this dimensionality matrix. The asymptotic distribution of this statistic is that of a linear combination of independent one‐degree‐of‐freedom chi‐squared…

异方差因子分析AI 解读

Two moment‐based model‐fitting procedures for the heteroscedastic factor analysis model are introduced and compared. The procedures produce consistent parameter estimators and asymptotically valid inferences for heteroscedasticity without specifying the distributional forms for the factor and heteroscedastic errors. Also, an individual‐specific inference procedure for the factor score is developed. Simulation studies show the practical usefulness of the procedures. An…

高维因子模型的推断理论AI 解读

This paper develops an inferential theory for factor models of large dimensions. The principal components estimator is considered because it is easy to compute and is asymptotically equivalent to the maximum likelihood estimator (if normality is assumed). We derive the rate of convergence and the limiting distributions of the estimated factors, factor loadings, and common components. The theory is developed within the framework of large cross sections ("N") and a large time…

共同主成分模型中的影响函数与离群点检测：稳健方法AI 解读

The common principal components model for several groups of multivariate observations assumes equal principal axes but different variances along these axes among the groups. Influence functions for plug‐in and projection‐pursuit estimates under a common principal component model are obtained. Asymptotic variances are derived from them. Outlier detection is possible using partial influence functions.

利用大量预测变量的主成分进行预测AI 解读

This article considers forecasting a single time series when there are many predictors (N) and time series observations (T). When the data follow an approximate factor model, the predictors can be summarized by a small number of indexes, which we estimate using principal components. Feasible forecasts are shown to be asymptotically efficient in the sense that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the…

利用降维识别大规模时间序列集合中的共享成分AI 解读

In this article we present a framework for parsimonious modeling of large ensembles of time series. The idea is to identify a small number of stochastic time series components such that each series in the ensemble is a weighted sum of series-specific realizations of the components. We present an estimation procedure that is computationally tractable for large datasets. Through simulations, we argue that the estimators perform well. We illustrate the method using two…

含函数型预测变量的广义线性模型AI 解读

Summary We present a technique for extending generalized linear models to the situation where some of the predictor variables are observations from a curve or function. The technique is particularly useful when only fragments of each curve have been observed. We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional principal…

ABSTRACT We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that range‐based volatility proxies are not only highly efficient, but also approximately Gaussian and robust to microstructure noise. Hence range‐based Gaussian quasi‐maximum likelihood estimation produces highly efficient estimates of stochastic volatility models and extractions of latent volatility. We use our method to examine…

结构变化与表面未识别的结构方程AI 解读

This paper introduces structural equations that do not satisfy the rank and/or order condition(s) for identification but still are identifiable. These equations are called seemingly unidentified structural equations. The key to the identifiability of these equations is that the right-hand-side endogenous variables undergo structural changes with respect to the exogenous and/or predetermined variables. To estimate the seemingly unidentified structural equations, this paper…

基于扩散指数的宏观经济预测AI 解读

This article studies forecasting a macroeconomic time series variable using a large number of predictors. The predictors are summarized using a small number of indexes constructed by principal component analysis. An approximate dynamic factor model serves as the statistical framework for the estimation of the indexes and construction of the forecasts. The method is used to construct 6-, 12-, and 24-month ahead forecasts for eight monthly U.S. macroeconomic time series using…

确定近似因子模型中的因子数AI 解读

In this paper we develop some econometric theory for factor models of large dimensions. The focus is the determination of the number of factors (r), which is an unresolved issue in the rapidly growing literature on multifactor models. We first establish the convergence rate for the factor estimates that will allow for consistent estimation of r. We then propose some panel criteria and show that the number of factors can be consistently estimated using the criteria. The…