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内生处理下异质性个体处理效应的估计AI 解读

This article estimates individual treatment effects (ITE) and its probability distribution in a triangular model with binary-valued endogenous treatments. Our estimation procedure takes two steps. First, we estimate the counterfactual outcome and hence, the ITE for every observational unit in the sample. Second, we estimate the ITE density function of the whole population. Our estimation method does not suffer from the ill-posed inverse problem associated with inverting a…

高维约束最大似然推断AI 解读

Inference in a high-dimensional situation may involve regularization of a certain form to treat overparameterization, imposing challenges to inference. The common practice of inference uses either a regularized model, as in inference after model selection, or bias-reduction known as “debias.” While the first ignores statistical uncertainty inherent in regularization, the second reduces the bias inbred in regularization at the expense of increased variance. In this article,…

从距离相关到多尺度图相关AI 解读

Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. In this paper, we establish a new framework that generalizes distance correlation (Dcorr)—a correlation measure that was recently proposed and shown to be universally consistent for dependence testing against all joint distributions of finite moments—to the…

贝叶斯排斥型高斯混合模型AI 解读

We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process). The asymptotic results for the posterior distribution of the proposed models are derived, including posterior consistency and posterior contraction rate in the context of nonparametric density estimation. More importantly, we show that…

单指标模型平均偏效应的去偏推断：对Wooldridge与Zhu的评论AI 解读

There has recently been a considerable amount of interest in developing methods for statistical inference in high-dimensional regimes with more covariates than data points (Javanmard and Montanari ...

近似稀疏相关随机效应面板Probit模型的统计推断AI 解读

We propose a simple procedure based on an existing “debiased” l1-regularized method for inference of the average partial effects (APEs) in approximately sparse probit and fractional probit models with panel data, where the number of time periods is fixed and small relative to the number of cross-sectional observations. Our method is computationally simple and does not suffer from the incidental parameters problems that come from attempting to estimate as a parameter the…

倾向得分匹配与加权估计量推断方法的有限样本表现AI 解读

This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity,…

高维资产条件时变因子模型中的 Alpha 检验AI 解读

For conditional time-varying factor models with high-dimensional assets, this article proposes a high-dimensional alpha (HDA) test to assess whether there exist abnormal returns on securities (or portfolios) over the theoretical expected returns. To employ this test effectively, a constant coefficient test is also introduced. It examines the validity of constant alphas and factor loadings. Simulation studies and an empirical example are presented to illustrate the finite…

非对称金融市场中的条件极值AI 解读

The global financial crisis of 2007–2009 revealed the great extent to which systemic risk can jeopardize the stability of the entire financial system. An effective methodology to quantify systemic risk is at the heart of the process of identifying the so-called systemically important financial institutions for regulatory purposes as well as to investigate key drivers of systemic contagion. The article proposes a method for dynamic forecasting of CoVaR, a popular measure of…

混合边际分布的 Copula 建模AI 解读

This article extends the literature on copulas with discrete or continuous marginals to the case where some of the marginals are a mixture of discrete and continuous components. We do so by carefully defining the likelihood as the density of the observations with respect to a mixed measure. The treatment is quite general, although we focus on mixtures of Gaussian and Archimedean copulas. The inference is Bayesian with the estimation carried out by Markov chain Monte Carlo.…

形状约束核加权最小二乘：智利制造业生产函数估计AI 解读

In this article, we examine a novel way of imposing shape constraints on a local polynomial kernel estimator. The proposed approach is referred to as shape constrained kernel-weighted least squares (SCKLS). We prove uniform consistency of the SCKLS estimator with monotonicity and convexity/concavity constraints and establish its convergence rate. In addition, we propose a test to validate whether shape constraints are correctly specified. The competitiveness of SCKLS is…

非因果自回归的 Bootstrap 推断及其在爆炸性泡沫建模中的应用AI 解读

In this article, we develop new bootstrap-based inference for noncausal autoregressions with heavy-tailed innovations. This class of models is widely used for modeling bubbles and explosive dynamics in economic and financial time series. In the noncausal, heavy-tail framework, a major drawback of asymptotic inference is that it is not feasible in practice as the relevant limiting distributions depend crucially on the (unknown) decay rate of the tails of the distribution of…

使用估计因子检验即时预测的单调性AI 解读

This article proposes a test to determine whether “big data” nowcasting methods, which have become an important tool to many public and private institutions, are monotonically improving as new information becomes available. The test is the first to formalize existing evaluation procedures from the nowcasting literature. We place particular emphasis on models involving estimated factors, since factor-based methods are a leading case in the high-dimensional empirical…

随机对照试验的最优数据收集AI 解读

Summary In a randomized control trial, the precision of an average treatment effect estimator and the power of the corresponding t-test can be improved either by collecting data on additional individuals, or by collecting additional covariates that predict the outcome variable. To design the experiment, a researcher needs to solve this trade-off subject to her budget constraint. We show that this optimization problem is equivalent to optimally predicting outcomes by the…

异质动态面板数据的核估计AI 解读

Summary This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel smoothing to compute their density functions. The dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect the required condition on the relative magnitudes of the…

含共同因子面板的一种新结构突变检验AI 解读

Summary This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement—more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is…

信息技术外包与企业生产率：消除因变量选择性缺失带来的偏差AI 解读

Summary Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables’ realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective. Relevant applications range from financial or strategic…

重叠有限时有限总体处理效应的推断AI 解读

Summary This paper studies inference on finite-population average and local average treatment effects under limited overlap, meaning that some strata have a small proportion of treated or untreated units. We model limited overlap in an asymptotic framework, sending the propensity score to zero (or one) with the sample size. We derive the asymptotic distribution of analogue estimators of the treatment effects under two common randomization schemes: conditionally independent…

动态面板模型的初始条件：组内方程与组间方程AI 解读

Summary This paper investigates the quasi-maximum likelihood estimation of short dynamic panel data models. We consider their estimation on both fixed effects and random effects specifications and propose a Hausman test when exogenous variables are present. For a dynamic panel model, initial conditions play important roles in model structure and estimation, and they give rise to a between equation under the random effects framework. With the between equation properly…

非参数趋势推断的自回归野生自助法AI 解读

In this paper we propose an autoregressive wild bootstrap method to construct confidence bands around a smooth deterministic trend. The bootstrap method is easy to implement and does not require any adjustments in the presence of missing data, which makes it particularly suitable for climatological applications. We establish the asymptotic validity of the bootstrap method for both pointwise and simultaneous confidence bands under general conditions, allowing for general…

全球南北半球气温与共同均值转移：向量均值转移自回归分析AI 解读

This paper examines local changes in annual temperature data for the northern and southern hemispheres (1850–2017) by using a multivariate generalization of the shifting-meanautoregressive model of González and Teräsvirta (2008). Univariate models are first fitted to each series by using the QuickShift methodology. Full information maximum likelihood estimates of a bivariate system of temperature equations are then obtained and asymptotic properties of the corresponding…

地球瞬态气候敏感度的计量估计AI 解读

How sensitive is Earth’s climate to a given increase in atmospheric greenhouse gas (GHG) concentrations? This long-standing question in climate science was recently analyzed by dynamic panel data methods using extensive spatio-temporal data of global surface temperatures, solar radiation, and GHG concentrations over the last half century to 2010 (Storelvmo et al, 2016). Those methods revealed that atmospheric aerosol effects masked approximately one-third of the continental…

似不相关协整多项式回归的完全修正OLS估计、推断与二氧化碳环境库兹涅茨曲线AI 解读

This paper develops two fully modified OLS (FM-OLS) estimators for systems of seemingly unrelated cointegrating polynomial regressions, i.e., systems of regressions that include deterministic variables, integrated processes, and integer powers of integrated processes as explanatory variables. The stationary errors are allowed to be serially correlated and the regressors to be endogenous. Furthermore, the errors and regressors are allowed to be dynamically cross-sectionally…

气候系统的计量建模：能量平衡模型与协整向量自回归的等价性AI 解读

Estimates of both the human impact on climate as well as the economic impacts of climate change are crucial to inform policy decisions. Econometric modelling allows us to quantify these impacts and their uncertainties, but models have to be consistent with the underlying physics and the time series properties of the data. Here I show that energy-balance models of climate are equivalent to an econometric cointegrated system and can be estimated in discrete time. This…