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非代表性网络样本：通过加权估计网络结构性质AI 解读

This paper analyzes statistical issues arising from non-representative samples of a network. Sampled network data could systematically bias the network properties and generate non-classical measurement error problems. Apart from the sampling rate and the elicitation procedure, the biases on network structural measures depend non-trivially on which subpopulations of nodes are missing with higher probability. We propose a methodology, adapting weighted estimators to networked…

对跳跃、微观结构噪声及异步观测稳健的高频相关矩阵主成分分析AI 解读

This paper developed the high frequency estimation for the principal component analysis (PCA) based on correlation matrix. This estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously, which is enabled by the newly proposed Truncated and Smoothed Two-Scales Realized Volatility (Truncated S-TSRV) estimator. The general framework of our methodology is constructed based on the estimation of realized spectral functions…

交互固定效应面板模型中处理效应的置信区间AI 解读

We augment the factor-based estimation of treatment effects proposed by Bai and Ng (2021) with easy-to-implement and nonparametric confidence intervals of treatment effects on every treated unit at every post-treatment time. The construction of confidence intervals entails a residual-based bootstrap resampling procedure. This method does not rely on any parametric assumption on the distribution of idiosyncratic errors and it is robust to weak cross-sectional and time-series…

面向极端风险的面板分位数回归AI 解读

Panel quantile regression models play an essential role in finance, insurance, and risk management applications. However, a direct application of panel regression for extreme conditional quantiles may suffer from a significant estimation uncertainty due to data sparsity on the far tail. We introduce a two-stage method to predict extreme conditional quantiles over cross-sections, which uses panel quantile regression at a selected intermediate level and then extrapolates the…

利用成对 Copula 构造对捆绑保险风险进行依赖建模、定价与管理AI 解读

We propose a dependence-aware predictive modeling framework for multivariate risks stemmed from an insurance contract with bundling features — an important type of policy increasingly offered by major insurance companies. The bundling feature naturally leads to longitudinal measurements of multiple insurance risks, and correct pricing and management of such risks is of fundamental interest to financial stability of the macroeconomy. We build a novel predictive model that…

理性疏忽离散选择模型的识别AI 解读

This paper studies the non-parametric identification and estimation of an empirical rational inattention discrete choice model. Decision-makers do not observe their realized utility perfectly but can obtain a costly signal to inform them about the utility. This model nests the standard discrete choice model as a special case. We characterize the identified set of decision-makers’ prior beliefs based on cross-sectional market-level choice probabilities. We show that the…

具有时变潜在分组结构的面板数据模型AI 解读

This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism, the model may have different numbers of groups and/or different group memberships before and after the break. With preliminary nuclear norm regularized estimation followed by row- and column-wise linear regressions, we estimate the break…

无需样本分割的低秩矩阵补全推断及处理效应应用AI 解读

This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that the least square estimation of eigenvectors following the nuclear norm penalization attains the asymptotic normality. The key contribution of our method is that it does not require sample splitting. In addition, this paper allows dependent observation patterns and heterogeneous observation…

政策评估中处理效应识别的计算方法AI 解读

For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This paper investigates the possibility of extrapolating local treatment effects to different…

从期权中估计波动率的波动率与杠杆效应AI 解读

We propose model-free (nonparametric) estimators of the volatility of volatility and leverage effect using high-frequency observations of short-dated options. At each point in time, we integrate available options into estimates of the conditional characteristic function of the price increment until the options’ expiration and we use these estimates to recover spot volatility. Our volatility of volatility estimator is then formed from the sample variance and first-order…

共同相关效应回归的横截面 BootstrapAI 解读

The Common Correlated Effects (CCE) methodology is now well established for the analysis of factor-augmented panel data models. Yet, it is often neglected that the pooled variant is biased unless the cross-section dimension (N) of the dataset dominates the time series length (T). This is problematic for inference with typical macroeconomic datasets, where T is often equal or larger than N. In response, we establish in this paper the theoretical foundation of the…

具有交互效应的动态面板模型似然方法AI 解读

This paper studies dynamic panel models with a factor error structure that is correlated with the regressors. Both short panels (small T) and long panels (large T) are considered. A dynamic panel forms a simultaneous-equation system, and under the factor error structure, there exist constraints between the mean and the covariance matrix. We explore the constraints through a quasi-FIML (full information maximum likelihood) approach. The quasi-FIML approach does not estimate…

带薪育儿假影响儿童学业表现吗？复现Danzer与Lavy（2018）AI 解读

Summary Danzer and Lavy (2018) study how the duration of paid parental leave affects children's educational performance using data from PISA. An extension of the maximum duration from 12 to 24 months in Austria had no statistically significant effect on average, but the authors highlight the existence of large and statistically significant heterogenous effects that vary in sign depending on the education of mothers and children's gender. The policy increased the scores…

面板数据即时预测：市盈率案例AI 解读

Abstract The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross‐section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse‐group LASSO regularization which can take advantage of the mixed‐frequency time series panel data structures. Our empirical…

通过大数据分位数回归研究宏观尾部风险的非线性AI 解读

Summary Modeling and predicting extreme movements in GDP is notoriously difficult, and the selection of appropriate covariates and/or possible forms of nonlinearities are key in obtaining precise forecasts. In this paper, our focus is on using large datasets in quantile regression models to forecast the conditional distribution of US GDP growth. To capture possible nonlinearities, we include several nonlinear specifications. The resulting models will be huge dimensional, and…

存在样本流失时的配对与分层实验分析再审视AI 解读

Abstract In this paper, we revisit some common recommendations regarding the analysis of matched‐pair and stratified experimental designs in the presence of attrition. Our main objective is to clarify a number of well‐known claims about the practice of dropping pairs with an attrited unit when analyzing matched‐pair designs. Contradictory advice appears in the literature about whether or not dropping pairs is beneficial or harmful, and stratifying into larger groups has been…

异质系数线性面板模型中的样本选择AI 解读

Abstract We propose a parametric estimation procedure for linear panel data models with sample selection and heterogeneous coefficients that are present in both outcome model and selection model. Our two‐step estimation procedure accounts for endogeneity from the selection process and endogeneity from correlation between the individual unobserved heterogeneity and the observed covariates using control function like methods. Conditional linear projections are used to…

向量误差修正指数模型：表示、估计与识别AI 解读

Summary This paper extends the multivariate index autoregressive model to the case of cointegrated time series of order (1,1). In this new modelling, namely the vector error-correction index model (VECIM), the first differences of series are driven by some linear combinations of the variables, namely the indexes. When the indexes are significantly fewer than the variables, the VECIM achieves a substantial dimension reduction with reference to the vector error correction…

服从者参数的双重稳健性与服从者特征的半参数检验AI 解读

Summary We propose a semi-parametric test to evaluate (a) whether different instruments induce subpopulations of compliers with the same observable characteristics, on average; and (b) whether compliers have observable characteristics that are the same as the full population, treated subpopulation, or untreated subpopulation, on average. The test is a flexible robustness check for the external validity of instruments. To justify the test, we characterise the doubly robust…

参数位于参数空间边界时的惩罚准似然估计与模型选择AI 解读

Summary We consider here penalized likelihood-based estimation and model selection applied to econometric time series models, which allow for nonnegativity (boundary) constraints on some or all of the parameters. We establish that joint model selection and estimation result in standard asymptotic Gaussian distributed estimators. The results contrast with nonpenalized estimation, which, as is well-known, leads to nonstandard asymptotic distributions that depend on the unknown…

有偏技术进步下替代弹性的识别：结构面板 GMM 估计AI 解读

Summary: This paper provides a structural panel GMM (P-GMM) estimator of the elasticity of substitution between capital and labour that does not depend on external instruments, and which can be applied in the presence of biased technical change. We identify the conditions under which P-GMM is a consistent estimator and compare it to a fixed effects estimator. Using a Monte Carlo study, we find that the P-GMM estimator is nearly unbiased provided the number of time periods…

混合因子结构下的大型协方差矩阵估计AI 解读

Summary We extend the principal orthogonal complement thresholding (POET) framework by J. Fan, Y. Liao, and M. Mincheva (2013) to estimate large covariance matrices with a ‘mixed’ structure of observable and unobservable strong/weak factors, and we call this method the extended POET (ePOET). Especially, the weak factor structure allows the existence of slowly divergent eigenvalues of the covariance matrix that are frequently observed in real data. Under some mild conditions,…

随机右删失下的工具变量分位数回归AI 解读

Summary This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification generates a continuum of equations of which the quantile…

含干扰泛函的复杂调查数据增广两步估计方程AI 解读

Summary Statistical inference in the presence of nuisance functionals with complex survey data is an important topic in social and economic studies. The Gini index, Lorenz curves, and quantile shares are among the commonly encountered examples. The nuisance functionals are usually handled by a plug-in nonparametric estimator and the main inferential procedure can be carried out through a two-step generalized empirical likelihood method. Unfortunately, the resulting inference…