论文检索

时变因子模型的区分检验AI 解读

Time-varying factor models have been widely used to model changing relationships among economic and financial variables. The existing literature usually specifies the time-varying factor loadings as deterministic functions of time or unit root processes. This article proposes two consistent tests to distinguish between these two specifications based on a randomization approach. By setting the null hypothesis as either specification, we show that the proposed test statistics…

均值泛函估计的改进分治方法及其平均处理效应应用AI 解读

Mean estimation is an important issue in statistical inference and machine learning. We are concerned with estimating mean functional that is a function of several nonparametric functions when there is a large amount of observations. Directly estimating such mean functional through nonparametric smoothing has the complexity of at least a quadratic order of the sample size, which is computationally prohibitive for massive data. The divide-and-conquer approach are thus readily…

基于日内蜡烛图影线的已实现方差估计AI 解读

We develop a novel nonparametric estimator of integrated variance by summing up the squared wick lengths of intraday candlesticks over a fixed time interval. The proposed wick-based estimator is robust to short-lived extreme price movements, such as gradual jumps and flash crashes. We investigate the asymptotic properties of the proposed estimator, and show that its asymptotic variance is about four times smaller than the state-of-the-art differenced-return volatility (DV)…

一般缺失模式下高维因子模型的推断AI 解读

This paper establishes the inferential theory for the least squares estimation of large factor models with missing data. We propose a unified framework for asymptotic analysis of factor models that covers a wide range of missing patterns, including heterogenous random missing, selection on covariates/factors/loadings, block/staggered missing, mixed frequency and ragged edge. We establish the average convergence rates of the estimated factor space and loading space, the limit…

利用经验能量距离检测函数型数据变点AI 解读

We propose a novel family of test statistics to detect the presence of changepoints in a sequence of dependent, possibly multivariate, functional-valued observations. Our approach allows to test for a very general class of changepoints, including the “classical” case of changes in the mean, and even changes in the whole distribution. Our statistics are based on a generalisation of the empirical energy distance; we propose weighted functionals of the energy distance process,…

多项数据排名的有限样本与大样本推断：政党排名应用AI 解读

It is common to rank different categories by means of preferences that are revealed through data on choices. A prominent example is the ranking of political candidates or parties using the estimated share of support each one receives in surveys or polls about political attitudes. Since these rankings are computed using estimates of the share of support rather than the true share of support, there may be considerable uncertainty concerning the true ranking of the political…

交互固定效应与短面板下的动态处理效应估计AI 解读

We study the estimation and inference of dynamic average treatment effect parameters when parallel trends holds conditional on interactive fixed effects and where units enter into treatment at different time periods. Our proposed generalized method of moments estimator consists of two parts: first, we estimate the unobserved time effects by applying the fixed-T consistent quasi-long-differencing estimator of Ahn et al., (2013) to the never-treated group. Second, we estimate…

分组固定效应模型的简便估计方法AI 解读

This paper introduces a new fixed effects estimator for linear panel data models with clustered time patterns of unobserved heterogeneity. The method avoids non-convex and combinatorial optimization by combining a preliminary consistent estimator of the slope coefficient, an agglomerative pairwise-differencing clustering of cross-sectional units, and a pooled ordinary least squares regression. Asymptotic guarantees are established in a framework where T can grow at any power…

不依赖维度区分的变点检测AI 解读

Change point testing for high-dimensional data has attracted a lot of attention in statistics, econometrics and machine learning owing to the emergence of high-dimensional data with structural breaks from many fields. In practice, when the dimension is less than the sample size but is not small, it is often unclear whether a method that is tailored to high-dimensional data or simply a classical method that is developed and justified for low-dimensional data is preferred. In…

成对有效的工具变量AI 解读

Finding valid instruments is difficult. We propose Validity Set Instrumental Variable (VSIV) estimation, a method for estimating local average treatment effects (LATEs) in heterogeneous causal effect models when the instruments are partially invalid. We consider settings with pairwise valid instruments, that is, instruments that are valid for a subset of instrument value pairs. VSIV estimation exploits testable implications of instrument validity to remove invalid pairs and…

信任作为进入壁垒：金融科技采用的证据AI 解读

This paper studies the role of trust in incumbent lenders (banks) as an entry barrier to emerging FinTech lenders in credit markets. The empirical setting exploits the outbreak of the Wells Fargo scandal as a negative shock to borrowers’ trust in banks. Using a difference-in-differences framework, I find that increased exposure to the Wells Fargo scandal leads to an increase in the probability of borrowers using FinTech as mortgage originators. Utilizing political…

2024年 Denis Sargan 计量经济学奖AI 解读

The editors1 of The Econometrics Journal decided to award the 2024 Denis Sargan Econometrics Prize to Rahul Singh (Harvard) and Liyang Sun (CEMFI and UCL) for their article “Double robustness for complier parameters and a semi-parametric test for complier characteristics” in the January 2024 issue of The Econometrics Journal.2 Suppose that we are interested in the effects of a binary treatment on some outcome and allow these effects to vary arbitrarily in the studied…

GMM 模型中正态近似的一般诊断方法AI 解读

Summary This paper proposes a diagnostic method for evaluating the quality of the normal approximation in generalized method of moments (GMM) models through sampling from a quasi-Bayesian parameter distribution. GMM estimates are consistent and asymptotically normal under certain regularity conditions, and researchers often assume normality when conducting inference. However, the literature has identified several violations to the normal approximation, such as in cases with…

非线性面板数据模型的共同相关效应估计AI 解读

Summary This paper focuses on estimating the coefficients and average partial effects of observed regressors in nonlinear panel data models with interactive fixed effects, using the common correlated effects framework. The proposed two-step estimation method involves applying principal component analysis to estimate the latent factors based on cross-sectional averages of the regressors in the first step, and jointly estimating the coefficients of the regressors and the…

伪面板最小距离估计中的部分匹配样本修正AI 解读

Summary Certain repeated cross-sectional data sets, such as the Current Population Survey (CPS), use special sampling designs by which samples from different times periods are partially matched. This paper proposes a correction to the optimal weighting matrix in minimum distance (MD) estimation of pseudo-panel models to account for such partially matched samples. This partially matched sample correction may be needed if the sample matching rate is nontrivial and, at the same…

时间序列回归中的稳健推断AI 解读

Summary Least squares regression with heteroskedasticity consistent standard errors (‘OLS-HC regression’) has proved very useful in cross-section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HC technology to time-series environments via heteroskedasticity and autocorrelation consistent standard errors (‘OLS-HAC regression’). First, in plausible time-series environments, OLS parameter estimates can…

不可观测项对称性下进入博弈的识别与估计AI 解读

Summary This paper provides a new point identification and estimation method for two-player entry games with complete information, based on symmetry of unobservables. Neither equilibrium selection nor parametric distributional assumptions are required. In addition, a weaker support condition is used relative to the existing literature. Unlike other semiparametric estimators, the estimator proposed here is $\sqrt{n}$-consistent. A test of the required symmetry condition is…

用于高斯近似的自归一化 Cramér 型中等偏差定理AI 解读

Berry–Esseen type bounds for Gaussian approximation of standardized sums have been extensively studied under exponential type moment conditions. In this paper, a Cramér type moderate deviation theorem is established for self-normalized Gaussian approximation under finite moment conditions. More specifically, let X1,X2,…,Xn be i.i.d. Rp-valued random vectors with zero means. Let Sn,j=∑ i=1nXij and Vn,j2=∑i=1nXij2. We show that if the correlation matrix of X1 is Ip and the…

交叉随机效应广义线性混合模型中最大似然估计量的渐近分布AI 解读

Generalized linear mixed models (GLMM) with crossed random effects is infamously known to present major challenges not only computationally but also theoretically. In fact, to date only consistency of the maximum likelihood estimators (MLE) has been proved for GLMM with crosses random effects. We introduce a new technique in asymptotic analysis built on a second-order Laplace approximation (LA) of a conditional expectation, whose coefficients are carefully evaluated. The LA…

概率密度 L2 范数的自适应估计及相关问题（二）：基于预言方法的上界AI 解读

This is the second part of the research project initiated in Cleanthous, Georgiadis and Lepski (2024a). We deal with the problem of the adaptive estimation of the L2-norm of a probability density on Rd, d≥1, from independent observations. The unknown density is assumed to be uniformly bounded by unknown constant and to belong to the union of balls in the isotropic/anisotropic Nikolskii’s spaces. In Cleanthous, Georgiadis and Lepski (2024a), we have proved that the optimally…

强化学习中的平稳性检验与变点检测AI 解读

We consider reinforcement learning (RL) in possibly nonstationary environments. Many existing RL algorithms in the literature rely on the stationarity assumption that requires the state transition and reward functions to be constant over time. However, this assumption is restrictive in practice and is likely to be violated in a number of applications, including traffic signal control, robotics and mobile health. In this paper, we develop a model-free test to assess the…

低频扩散数据的统计算法：偏微分方程方法AI 解读

We consider the problem of making nonparametric inference in a class of multi-dimensional diffusions in divergence form, from low-frequency data. Statistical analysis in this setting is notoriously challenging due to the intractability of the likelihood and its gradient, and computational methods have thus far largely resorted to expensive simulation-based techniques. In this article, we propose a new computational approach which is motivated by PDE theory and is built…

基于自适应收集数据的半参数推断AI 解读

Many standard estimators, when applied to adaptively collected data, fail to be asymptotically normal, thereby complicating the construction of confidence intervals. We address this challenge in a semiparametric context: estimating the parameter vector of a generalized linear regression model contaminated by a nonparametric nuisance component. We construct suitably weighted estimating equations that account for adaptivity in data collection and provide conditions under which…

依赖结构下最大 Pearson 相关系数的渐近分布AI 解读

Given a random sample from a multivariate normal distribution whose covariance matrix is a Toeplitz matrix, we study the largest off-diagonal entry of the sample correlation matrix. Assuming the multivariate normal distribution has the covariance structure of an autoregressive sequence, we establish a phase transition in the limiting distribution of the largest off-diagonal entry. We show that the limiting distributions are of Gumbel-type (with different parameters),…