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青少年就寝决策中的同伴效应：基于抽样数据的社会网络模型AI 解读

Using unique information on a representative sample of US teenagers, we investigate peer effects in adolescent bedtime decisions. We extend the nonlinear least‐squares estimator for spatial autoregressive models to estimate network models with network fixed effects and sampled observations on the dependent variable. We show the extent to which neglecting the sampling issue yields misleading inferential results. When accounting for sampling, we find that, besides the…

含测量误差动态面板回归的多工具变量估计AI 解读

In this paper, we investigate a dynamic linear panel regression model with measurement error. We consider the panel data estimation whose time dimension (T) is not small and comparable to the cross sectional dimension (N). First, we show that the 2SLS estimator suffers from the bias problem due to many instrumental variables. Using the alternative asymptotics where T3N goes to a constant as N,T→∞, we characterize its asymptotic bias due to many IVs. As a bias reduction…

含测量误差线性面板模型的一致估计AI 解读

Measurement error causes a bias towards zero when estimating a panel data linear regression model. The panel data context offers various opportunities to derive instrumental variables allowing for consistent estimation. We consider three sources of moment conditions: (i) restrictions on the covariance matrix of the errors in the equations, (ii) nonzero third moments of the regressors, and (iii) heteroskedasticity and nonlinearity in the relation between the error-ridden…

不可观测变量计量：测量误差模型在产业组织与劳动经济学中的应用AI 解读

This paper reviews the recent developments in nonparametric identification of measurement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to an observed distribution. The identification and estimation of measurement error models focus on how to obtain the latent distribution and the measurement error distribution from…

大型网络博弈中依赖社会影响力的同伴压力估计AI 解读

Research on peer effects in sociology has long been focused on social interactions and the associated social influence process. In this paper, we extend a large‐network‐based game model to a model that allows for the dependence of social interactions on social‐influence status. In particular, we use the Katz–Bonacich centrality to measure individuals' social influences, which are obtained directly from the observation of a social network. To solve the computational burden…

利用大型协方差矩阵对交互效应面板模型进行推断AI 解读

We consider efficient estimation of panel data models with interactive effects, which relies on a high-dimensional inverse covariance matrix estimator. By using a consistent estimator of the error covariance matrix, we can take into account both cross-sectional correlations and heteroskedasticity. In the presence of cross-sectional correlations, the proposed estimator eliminates the cross-sectional correlation bias, and is more efficient than the existing methods. The rate…

结构 FECM：大规模结构 FAVAR 模型中的协整AI 解读

Summary Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by…

使用估计因子与特异成分的模型选择AI 解读

Summary This paper provides consistent information criteria for the selection of forecasting models that use a subset of both the idiosyncratic and common factor components of a big dataset. This hybrid model approach has been explored by recent empirical studies to relax the strictness of pure factor‐augmented model approximations, but no formal model selection procedures have been developed. The main difference to previous factor‐augmented model selection procedures is…

利用异质性扩展的双层随机前沿估计劳动力进入者与退出者AI 解读

In a seminal paper, Basmann (1985) introduced a serial correlation structure based on an intertemporal adjustment mechanism. Basmann’s 1985 paper of course was built on his previous pioneering work on estimation and identifiability in structural equations leading to 2SLS (Basmann, 1957, 1960). In this paper, we follow a similar path. We derive a non-standard unit root serial correlation formulation for intertemporal adjustments in the labor force participation rate. This…

成对非可分测量误差模型的识别AI 解读

This paper studies the paired nonseparable measurement error models, where two measurements, X and Y , are produced by mutually independent unobservables, U , V , and W , through the system, X = g ( U,V ) and Y = h ( U,W ). We propose restrictions to identify the marginal distribution of the common component U and the conditional distributions of X and Y given U . Applying this method to twin panel data, we find the following robust reporting patterns for years of education:…

合成控制法与 Hsiao 等人的面板数据项目评估法：实证比较AI 解读

Summary We compare two program evaluation methodologies: the synthetic control method and the panel data approach. We apply both methods to estimate the effect of the political and economic integration of Hong Kong. The results obtained differ depending on the methodology used. We then conduct a simulation that shows that the synthetic control method results in a post‐treatment mean squared error, mean absolute percentage error, and mean error with a smaller interquartile…

信贷繁荣后的崩溃：对Schularick与Taylor研究的复现AI 解读

Summary This paper replicates the results in Schularick and Taylor ( American Economic Review 2012; 102 (2): 1029–1061; ST hereafter). Specifically, I replicate ST's results in the ‘narrow’ sense by reproducing their calculations in the open source econometrics package gretl. (Gretl is an acronym for Gnu Regression, Econometrics and Time‐series Laboratory. It is available for Windows, Mac and Linux at www.gretl.sourceforge.net .) I also demonstrate the robustness of ST's…

非线性非高斯状态空间模型中参数与状态的联合贝叶斯分析AI 解读

Summary We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non‐Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis–Hastings algorithm or in importance sampling. Our method provides a computationally more efficient alternative to several recently proposed algorithms. We present…

纵向研究中基因与环境交互的集合检验AI 解读

We propose a generalized score type test for set-based inference for gene-environment interaction with longitudinally measured quantitative traits. The test is robust to misspecification of within subject correlation structure and has enhanced power compared to existing alternatives. Unlike tests for marginal genetic association, set-based tests for gene-environment interaction face the challenges of a potentially misspecified and high-dimensional main effect model under the…

函数型响应数据的函数型变系数单指标模型AI 解读

Motivated by the analysis of imaging data, we propose a novel functional varying-coefficient single index model (FVCSIM) to carry out the regression analysis of functional response data on a set of covariates of interest. FVCSIM represents a new extension of varying-coefficient single index models for scalar responses collected from cross-sectional and longitudinal studies. An efficient estimation procedure is developed to iteratively estimate varying coefficient functions,…

海量金融时间序列聚类：高维预测变量与因子结构的面板方法AI 解读

This article introduces a new procedure for clustering a large number of financial time series based on high-dimensional panel data with grouped factor structures. The proposed method attempts to capture the level of similarity of each of the time series based on sensitivity to observable factors as well as to the unobservable factor structure. The proposed method allows for correlations between observable and unobservable factors and also allows for cross-sectional and…

遗漏相关随机效应的 LM 检验及其应用AI 解读

This article aims at achieving two distinct goals. The first is to extend the existing LM test of overdispersion to the situation where the alternative hypothesis is characterized by the correlated random effects model. We obtain a result that the test against the random effects model has a certain max-min type optimality property. We will call such a test the LM test of overdispersion. The second goal of the article is to draw a connection between panel data analysis and…

无限维因子空间动态因子模型的渐近分析AI 解读

Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni et al., (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni et al. (2004). Those estimators, however, rely on Brillinger’s concept of dynamic principal components, and thus involve two-sided filters, which leads to…

非可分工具变量模型的独立性最小距离估计AI 解读

I develop a semiparametric minimum distance from independence estimator for a nonseparable instrumental variables model. An independence condition identifies the model for many types of discrete and continuous instruments. The estimator is taken as the parameter value that most closely satisfies this independence condition. Implementing the estimator requires a quantile regression of the endogenous variables on the instrument, so the procedure is two-step, with a finite or…

引力模型的双向效应估计AI 解读

Empirical models for dyadic interactions between n agents often feature agent-specific parameters. Fixed-effect estimators of such models generally have bias of order n−1, which is nonnegligible relative to their standard error. Therefore, confidence sets based on the asymptotic distribution have incorrect coverage. This paper looks at models with multiplicative unobservables and fixed effects. We derive moment conditions that are free of fixed effects and use them to set up…

固定效应有序Logit模型的估计AI 解读

This paper introduces a new estimator for the fixed-effects ordered logit model. The proposed method has two advantages over existing estimators. First, it estimates the differences in the cut points along with the regression coefficient, leading to provide bounds on partial effects. Second, the proposed estimator for the regression coefficient is more efficient. I use the fact that the ordered logit model with J outcomes and T observations can be converted to a binary…

一般纵向数据的可忽略性AI 解读

Likelihood factors that can be disregarded for inference are termed ignorable. We demonstrate that close ties exist between ignorability and identification of causal effects by covariate adjustment. A graphical condition, stability, plays a role analogous to that of missingness at random, but is applicable to general longitudinal data. Our formulation of ignorability does not depend on any notion of missing data, so is appealing in situations where missing data may not…

纵向数据与函数型数据的最优设计AI 解读

Summary We propose novel optimal designs for longitudinal data for the common situation where the resources for longitudinal data collection are limited, by determining the optimal locations in time where measurements should be taken. As for all optimal designs, some prior information is needed to implement the optimal designs proposed. We demonstrate that this prior information may come from a pilot longitudinal study that has irregularly measured and noisy measurements,…

高维重复测量数据的协方差结构检验AI 解读

In regression analysis with repeated measurements, such as longitudinal data and panel data, structured covariance matrices characterized by a small number of parameters have been widely used and play an important role in parameter estimation and statistical inference. To assess the adequacy of a specified covariance structure, one often adopts the classical likelihood-ratio test when the dimension of the repeated measurements ($p$) is smaller than the sample size ($n$).…