论文检索

含协变量的尾指数估计：股票收益尾部风险动态AI 解读

This paper provides novel theoretical results for the estimation of the conditional tail index of Pareto and Pareto-type distributions in a time series context. We show that both the estimators and relevant test statistics are normally distributed in the limit, when independent and identically distributed or dependent data are considered. Simulation results provide support for the theoretical findings and highlight the good finite sample properties of the approach in a time…

总量需求随机系数 Logit 模型的半非参数估计AI 解读

In this paper, we propose a two-step semi-nonparametric estimator for the widely used random coefficients logit demand model. The approach applies to the same setup as Berry et al. (1995, BLP)-type of models with many products, but has the advantage of not requiring computing demand inversion. In particular, the first step of our approach estimates the fixed coefficients via a computationally very easy linear sieve generalized method of moments (GMM). The second step…

非线性局部非平稳滤波器的随机性质AI 解读

This article delivers conditions for the existence of a unique stationary, ergodic and φ-mixing invertible solution for nonlinear time-varying parameter models that are locally non-stationary or explosive. The assumptions are different from existing conditions as they do not impose Lipschitz, bounded growth or drift restrictions. Instead we require that the time series contains resetting dynamics. These dynamics are present in time series with sudden changes, such as stock…

非可分三角模型中个体处理效应的推断AI 解读

In nonseparable triangular models with a binary endogenous treatment and a binary instrumental variable, Vuong and Xu (2017) established identification results for individual treatment effects (ITEs) under the rank invariance assumption. Using their approach, Feng et al. (2019) proposed a uniformly consistent kernel estimator for the density of the ITE that utilizes estimated ITEs. In this paper, we establish the asymptotic normality of the density estimator of Feng et al.…

稀疏分位数回归AI 解读

We consider both ℓ0-penalized and ℓ0-constrained quantile regression estimators. For the ℓ0-penalized estimator, we derive an exponential inequality on the tail probability of excess quantile prediction risk and apply it to obtain non-asymptotic upper bounds on the mean-square parameter and regression function estimation errors. We also derive analogous results for the ℓ0-constrained estimator. The resulting rates of convergence are nearly minimax-optimal and the same as…

含群组效应的高效同伴效应估计AI 解读

We study linear peer effect models where peers interact in groups and individual’s outcomes are linear in the group mean outcome and characteristics. We allow for unobserved random group effects as well as observed fixed group effects. The specification is in part motivated by the moment conditions imposed in Graham (2008). We show that these moment conditions can be cast in terms of a linear random group effects model and that they lead to a class of GMM estimators with…

双重差分的新进展：近期计量文献的综合梳理AI 解读

This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of “canonical” assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on (i) multiple periods and variation in treatment timing,…

社会互动阈值回归AI 解读

This paper develops a threshold social interaction model which introduces group-specific endogenous effects as well as contextual effects into a conventional spatial Durbin model. We propose a two-step GMM estimator for the threshold and regression parameters, derive asymptotic theory, and provide bootstrap inference. Finally, we assess the performance of our methods using a Monte Carlo simulation and provide an empirical application on the role of peer effects on student…

线性回归中适当聚类层级的检验AI 解读

The overwhelming majority of empirical research that uses cluster-robust inference assumes that the clustering structure is known, even though there are often several possible ways in which a dataset could be clustered. We propose two tests for the correct level of clustering in regression models. One test focuses on inference about a single coefficient, and the other on inference about two or more coefficients. We provide both asymptotic and wild bootstrap implementations.…

连续时间非参数回归的一致与 Lp 收敛及半参数应用AI 解读

We obtain uniform and Lp convergence rates of kernel type nonparametric estimators for the instantaneous conditional mean and variance functions of continuous time regressions, where the regressor is assumed to be a general recurrent diffusion. Our asymptotics are developed under a general set-up, with a shrinking sampling interval and an increasing time span, and without the stationarity assumption. Based on our convergence results, we develop a semiparametric inferential…

线性不等式约束下的 Wald、QLR 与得分检验AI 解读

This paper develops Wald-type, QLR, and score-type tests for linear equality constraints in a general class of extremum estimation problems where the parameter space is characterized by a finite number of linear equality and inequality constraints. We show that the asymptotic null distributions of the Wald and QLR statistics are discontinuous in an implicit nuisance parameter and propose an algorithm to identify it. In contrast, the asymptotic null distribution of the score…

空间动态面板中潜在群组结构的识别AI 解读

This paper considers the identification of latent group structures in spatial dynamic panels. We follow Lee and Yu (2010) and consider a rich spatial dynamic panel data (SDPD) model with two-way fixed effects. In addition, we also allow for latent panel structures where individuals can be classified into a few groups such that individuals within the same group share the common slope parameters and do not otherwise. Both the number of groups and the individuals’ group…

矩阵序列应采用单向还是双向因子模型？AI 解读

This paper investigates the issue of determining the dimensions of row and column factor spaces in matrix-valued data. Exploiting the eigen-gap in the spectrum of sample second moment matrices of the data, we propose a family of randomised tests to check whether a one-way or two-way factor structure exists or not. Our tests do not require any arbitrary thresholding on the eigenvalues, and can be applied with (virtually) no restrictions on the relative rate of divergence of…

具有较弱载荷的近似因子模型AI 解读

Pervasive cross-section dependence is increasingly recognized as a characteristic of economic data and the approximate factor model provides a useful framework for analysis. Assuming a strong factor structure where Λ0′Λ0/Nα is positive definite in the limit when α=1, early work established convergence of the principal component estimates of the factors and loadings up to a rotation matrix. This paper shows that the estimates are still consistent and asymptotically normal…

利用全球与国家因子分析大型波动率矩阵AI 解读

Several large volatility matrix inference procedures have been developed, based on the latent factor model. They often assumed that there are a few of common factors, which can account for volatility dynamics. However, several studies have demonstrated the presence of local factors. In particular, when analyzing the global stock market, we often observe that nation-specific factors explain their own country’s volatility dynamics. To account for this, we propose the Double…

NPIV模型平均导数的有效估计：神经网络估计量的模拟比较AI 解读

Artificial Neural Networks (ANNs) can be viewed as nonlinear sieves that can approximate complex functions of high dimensional variables more effectively than linear sieves. We investigate the performance of various ANNs in nonparametric instrumental variables (NPIV) models of moderately high dimensional covariates that are relevant to empirical economics. We present two efficient procedures for estimation and inference on a weighted average derivative (WAD): an…

多重门限因子模型的收缩估计AI 解读

This paper proposes a multiple threshold factor model to enhance the flexibility in modeling the underlying regime switching mechanism for high dimensional time series. The factor loadings are assumed to switch between different regimes according to the value of a threshold variable. A novel estimation procedure is proposed to consistently estimate the multiple thresholds with the aid of sorting operation, principal component analysis and shrinkage estimation, which is…

第一价格拍卖模型的函数估计方法AI 解读

This paper introduces new methods of identification and estimation of the first-price sealed bid auction model and compares them with the previous existing ones. The first method of estimation allows us to estimate directly (through an iterative algorithm) the cumulative distribution function of the private values without estimating the private values beforehand. In the second method, we use a quantile approach. Although the first-price auction is a complex nonlinear inverse…

Tobit模型的工具变量方法AI 解读

This paper studies models of processes generating censored outcomes with endogenous explanatory variables and instrumental variable restrictions. Tobit-type left censoring at zero is the primary focus in the exposition. Extension to stochastic censoring is sketched. The models do not specify the process determining endogenous explanatory variables and they do not embody restrictions justifying control function approaches. Consequently, they can be partially or point…

存在溢出与不依从的实验中如何识别因果效应AI 解读

This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of spillovers–one person’s treatment may affect another’s outcome–and one-sided non-compliance—subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person’s own treatment changes her outcome, while indirect effects quantify how her peers’…

IV估计与推断中的工具变量强度：理论与实践指南AI 解读

Two stage least squares (2SLS) has poor properties if instruments are exogenous but weak. But how strong do instruments need to be for 2SLS estimates and test statistics to exhibit acceptable properties? A common standard is that first-stage F≥10. This is adequate to ensure two-tailed t-tests have modest size distortions. But other problems persist: In particular, we show 2SLS standard errors are artificially small in samples where the estimate is most contaminated by the…

线性回归中基于Stein型平均估计量的联合推断AI 解读

While averaging unrestricted with restricted estimators is known to reduce estimation risk, it is an open question whether this reduction in turn can improve inference. To analyze this question, we construct joint confidence regions centered at James–Stein averaging estimators in both homoskedastic and heteroskedastic linear regression models. These regions are asymptotically valid when the number of restrictions increases possibly proportionally with the sample size. When…

多弱工具变量下聚类样本 IV 回归模型的刀切估计AI 解读

This paper proposes new jackknife IV estimators that are robust to the effects of many weak instruments and error heteroskedasticity in a cluster sample setting with cluster-specific effects and possibly many included exogenous regressors. The estimators that we propose are designed to properly partial out the cluster-specific effects and included exogenous regressors while preserving the re-centering property of the jackknife methodology. To the best of our knowledge, our…

利用独立性细化 VAR 的集合识别AI 解读

Identification in VARs has traditionally mainly relied on second moments. Some researchers have considered using higher moments as well, but there are concerns about the strength of the identification obtained in this way. In this paper, we propose refining existing identification schemes by augmenting sign restrictions with a requirement that rules out shocks whose higher moments significantly depart from independence. This approach does not assume that higher moments help…