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随机对象的共形推断AI 解读

We develop an inferential tool kit for analyzing object-valued responses, which correspond to data situated in general metric spaces, paired with Euclidean predictors within the conformal framework. To this end, we introduce conditional profile average transport costs, where we compare distance profiles that correspond to one-dimensional distributions of probability mass falling into balls of increasing radius through the optimal transport cost when moving from one distance…

高斯序列模型中的稀疏性与相关性AI 解读

We study estimation of an s-sparse signal in the p-dimensional Gaussian sequence model with equicorrelated observations and derive the minimax rate. A new phenomenon emerges from correlation, namely, the rate scales with respect to p−2s and exhibits a phase transition at p−2s≍p. Correlation is shown to be a blessing, provided it is sufficiently strong and the critical correlation level exhibits a delicate dependence on the sparsity level. Due to correlation, the minimax rate…

坐标上升变分推断的收敛性AI 解读

As a computational alternative to Markov chain Monte Carlo approaches, variational inference (VI) is becoming more and more popular for approximating intractable posterior distributions in large-scale Bayesian models due to its comparable efficacy and superior efficiency. Several recent works provide theoretical justifications of VI by proving its statistical optimality for parameter estimation under various settings; meanwhile, formal analysis on the algorithmic convergence…

非参数回归模型的涵盖检验AI 解读

We set up a formal framework to characterize encompassing of nonparametric models through the $L^2$ distance. We contrast it to previous literature on the comparison of nonparametric regression models. We then develop testing procedures for the encompassing hypothesis that are fully nonparametric. Our test statistics depend on kernel regression, raising the issue of bandwidth’s choice. We investigate two alternative approaches to obtain a “small bias property” for our test…

条件分位数处理效应异质性的非参数检验AI 解读

This paper proposes a nonparametric test to assess whether there exist heterogeneous quantile treatment effects (QTEs) of an intervention on the outcome of interest across different sub-populations defined by covariates of interest. Specifically, a consistent test statistic based on the Cramér–von Mises type criterion is developed to test if the treatment has a constant quantile effect for all sub-populations defined by covariates of interest. Under some regularity…

协整多项式回归：完全修正最小二乘法的稳健性AI 解读

Cointegrating polynomial regressions (CPRs) include deterministic variables, integrated variables, and their powers as explanatory variables. Based on a novel kernel-weighted limit result and a novel functional central limit theorem, this paper shows that the fully modified ordinary least squares (FM-OLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57, 99–125) is robust to being used in CPRs. Being used in CPRs refers to a widespread empirical practice…

维度增长下稀疏单指标模型的半参数估计与变量选择AI 解读

This paper considers semiparametric sieve estimation in high-dimensional single index models. The use of Hermite polynomials in approximating the unknown link function provides a convenient framework to conduct both estimation and variable selection. The estimation of the index parameter is formulated from solutions obtained by the routine penalized weighted linear regression procedure, where the weights are used in order to tackle the unbounded support of the regressors.…

不可观测因素是否可分离？AI 解读

It is common to assume in empirical research that observables and unobservables are additively separable, especially when the former are endogenous. This is because it is widely recognized that identification and estimation challenges arise when interactions between the two are allowed for. Starting from a nonseparable IV model, where the instrumental variable is independent of unobservables, we develop a novel nonparametric test of separability of unobservables. The…

交互固定效应下部分识别面板模型的推断AI 解读

In this paper, we develop methods for statistical inferences in a partially identified nonparametric panel data model with endogeneity and interactive fixed effects. Under some normalization rules, we can concentrate out the large-dimensional parameter vector of factor loadings and specify a set of conditional moment restrictions that are involved with only the finite-dimensional factor parameters along with the infinite-dimensional nonparametric component. For a conjectured…

时变向量无限阶移动平均过程的渐近理论AI 解读

This paper introduces a new class of time-varying vector moving average processes of infinite order. These processes serve dual purposes: (1) they can be used to model time-varying dependence structures, and (2) they can be used to establish asymptotic theories for multivariate time series models. To illustrate these two points, we first establish some fundamental asymptotic properties and use them to infer the trending term of a vector moving average infinity process. We…

具有得分驱动权重的动态混合向量自回归AI 解读

ABSTRACT We propose a novel dynamic mixture vector autoregressive (VAR) model where the time‐varying mixture weights are driven by the predictive likelihood score. Intuitively, the weight of a component VAR model is increased in the subsequent period if the current observation is more likely to be drawn from this state. The model is not limited to a specific distributional assumption and allows for straightforward likelihood‐based estimation and inference. In a Monte Carlo…

处理交互作用与内生参与AI 解读

ABSTRACT We study causal inference in randomized experiments (or quasi‐experiments) following a factorial design. There are two treatments, denoted and , and units are randomly assigned to one of four categories: treatment alone, treatment alone, joint treatment, or none. Allowing for endogenous non‐compliance with the two binary instruments representing the intended assignment, as well as unrestricted interference across the two treatments, we derive the causal…

部分效应方向已知时的简短置信区间AI 解读

Abstract We introduce adaptive confidence intervals on a parameter of interest in the presence of nuisance parameters, such as coefficients on control variables, with known signs. Our confidence intervals are trivial to compute and can provide significant length reductions relative to standard ones when the nuisance parameters are small. At the same time, they entail minimal length increases at any parameter values. We apply our confidence intervals to the linear regression…

簇刀切工具变量估计AI 解读

Abstract Researchers commonly use jackknife-based instrumental variables estimators to eliminate the many-instruments bias of two-stage least squares. Where inference must be clustered, however, the jackknife fails to eliminate the bias. We propose a cluster-jackknife approach in which first-stage predicted values for each observation are constructed from a regression that leaves out the observation’s entire cluster. The cluster-jackknife instrumental variables estimator…

特质波动率的横截面依赖AI 解读

This paper introduces an econometric framework for analyzing cross-sectional dependence in the idiosyncratic volatilities of assets using high frequency data. We first consider the estimation of standard measures of dependence in the idiosyncratic volatilities such as covariances and correlations. Naive estimators of these measures are biased due to the use of the error-laden estimates of idiosyncratic volatilities. We provide bias-corrected estimators and the relevant…

允许单整回归量的函数系数模型局部多项式估计极限理论AI 解读

Limit theory for functional coefficient cointegrating regression was recently found to be considerably more complex than earlier understood. The issues were explained and correct limit theory derived for the kernel weighted local level estimator in Phillips and Wang (2023b). The present paper provides complete limit theory for the general kernel weighted local pth order polynomial estimators of the functional coefficient and the coefficient derivatives. Both stationary and…

持续性与异方差预测变量下的预测分位数回归：高功效的两阶段最小二乘检验方法AI 解读

Funding Information: The authors thank the Co-Editor, Michael Jansson, an Associate Editor and two anonymous referees for their helpful and constructive comments. We also thank participants of the IAAE Annual conference 2023 at the BI Norwegian Business School, Oslo, Norway, and seminar participants at the University of the Balearic Islands, Palma de Maiorca, Spain for their comments. Demetrescu gratefully acknowledges the support of the Deutsche Forschungsgemeinschaft (DFG,…

双向聚类的渐近理论AI 解读

This paper proves a new central limit theorem for a sample that exhibits two-way dependence and heterogeneity across clusters. Statistical inference for situations with both two-way dependence and cluster heterogeneity has thus far been an open issue. The existing theory for two-way clustering inference requires identical distributions across clusters (implied by the so-called separate exchangeability assumption). Yet no such homogeneity requirement is needed in the existing…

二元指标设定下的剥夺状况分布推断AI 解读

The paper addresses the issue of comparing deprivation distributions when the severity of deprivation is measured by a sum of (weighted) binary variables. To accomplish this task, it provides a graphical tool, the Three I’s of Deprivation (TID) curve, which summarises the incidence, intensity and inequality aspects of deprivation in a society and is the natural counterpart to the TIP curve widely used in income poverty analysis. Uncertainty around the estimated deprivation…

因子增广回归的分位数预测：结构不稳定性与模型不确定性AI 解读

The quantile regression is an effective tool in modeling data with heterogeneous conditional distribution. This paper considers the time-varying coefficient quantile predictive regression with factor-augmented predictors, to capture smooth structural changes and incorporate high-dimensional data information in prediction simultaneously. Uniform consistency of the local linear quantile coefficient estimators is established under misspecification. To further improve the…

不稳定环境下的分位数格兰杰因果关系AI 解读

We propose a new framework for assessing Granger causality in quantiles in unstable environments, for a fixed quantile or over a continuum of quantile levels. Our proposed test statistics are consistent against fixed alternatives, they have nontrivial power against local alternatives, and they are pivotal in certain important special cases. In addition, we show the validity of a bootstrap procedure when asymptotic distributions depend on nuisance parameters. Monte Carlo…

可能非平稳自回归的模型平均预测AI 解读

As an alternative to model selection (MS), this paper considers model averaging (MA) for integrated autoregressive processes of infinite order (AR(∞)). We derive a uniformly asymptotic expression for the mean squared prediction error (MSPE) of the averaging prediction with fixed weights and then propose a Mallows-type criterion to select the data-driven weights that minimize the MSPE asymptotically. We show that the proposed MA estimator and its variants, Shibata and Akaike…

高维线性时间序列的监督因子建模AI 解读

Motivated by Tucker tensor decomposition, this paper imposes low-rank structures to the column and row spaces of coefficient matrices in a multivariate infinite-order vector autoregression (VAR), which leads to a supervised factor model with two factor modelings being conducted to responses and predictors simultaneously. Interestingly, the stationarity condition implies an intrinsic weak group sparsity mechanism of infinite-order VAR, and hence a rank-constrained group Lasso…

高维因子模型的 Huber 主成分分析AI 解读

Factor models have been widely used in economics and finance. However, the heavy-tailed nature of macroeconomic and financial data is often neglected in statistical analysis. To address this issue, we propose a robust approach to estimate factor loadings and scores by minimizing the Huber loss function, which is motivated by the equivalence between conventional Principal Component Analysis (PCA) and the constrained least squares method in the factor model. We provide two…