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Hug 与 Hop：离散时间非可逆马尔可夫链蒙特卡洛算法AI 解读

Summary This article introduces the hug and hop Markov chain Monte Carlo algorithm for estimating expectations with respect to an intractable distribution. The algorithm alternates between two kernels, referred to as hug and hop. Hug is a nonreversible kernel that repeatedly applies the bounce mechanism from the recently proposed bouncy particle sampler to produce a proposal point that is far from the current position yet on almost the same contour of the target density,…

协变量自适应随机化下的 Lasso 调整处理效应估计AI 解读

Summary We consider the problem of estimating and inferring treatment effects in randomized experiments. In practice, stratified randomization, or more generally, covariate-adaptive randomization, is routinely used in the design stage to balance treatment allocations with respect to a few variables that are most relevant to the outcomes. Then, regression is performed in the analysis stage to adjust the remaining imbalances to yield more efficient treatment effect estimators.…

2022年 Denis Sargan 计量经济学奖AI 解读

The editors of The Econometrics Journal decided to award the 2022 Denis Sargan Econometrics Prize to Chiara Casoli (Fondazione Eni Enrico Mattei, Milan) for her article “Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices” with Riccardo (Jack) Lucchetti in the May 2022 issue of The Econometrics Journal.1 Chiara and Jack’s prize winning article develops a cointegration-based Permanent-Transitory…

通过稀疏精度矩阵估计高维向量自回归AI 解读

Summary We consider the problem of estimating sparse vector autoregression (VAR) via penalized precision matrices. This matrix is the output of the underlying directed acyclic graph of the VAR process, whose zero components correspond to the zero coefficients of the graphical representation of the VAR. The sparsity-based precision matrix estimator is deduced from the D-trace loss with convex and nonconvex penalty functions. We establish the consistency of the penalized…

离散博弈中合作行为的非参数检验AI 解读

Summary We propose a nonparametric test for cooperative behaviour among players in discrete, static games. Assuming that certain exchangeability conditions hold if we match observable characteristics across all players, we obtain testable implications for cooperative (coalitional) behaviour, which we define as occurring when players maximise an unknown joint objective function that is symmetric conditional on the matching. Cooperation implies the existence of a class of…

差异化产品总量需求模型中随机系数的非参数识别AI 解读

Summary This paper studies nonparametric identification in market-level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual-specific coefficients to vary continuously across the population and give conditions under which the density of these coefficients, and hence also functionals such as the fractions of individuals who benefit from a counterfactual intervention, is identified.

含高维协变量的断点回归设计推断AI 解读

Summary We study regression discontinuity designs in which many predetermined covariates, possibly much more than the number of observations, can be used to increase the precision of treatment effect estimates. We consider a two-step estimator which first selects a small number of ‘important’ covariates through a localised lasso-type procedure, and then, in a second step, estimates the treatment effect by including the selected covariates linearly into the usual local linear…

Summary We propose new ways to compare two latent distributions when only ordinal data are available, and without imposing parametric assumptions on the underlying continuous distributions. First, we contribute identification results. We show how certain ordinal conditions provide evidence of between-group inequality, quantified by particular quantiles being higher in one latent distribution than in the other. We also show how other ordinal conditions provide evidence of…

无排除工具变量时的可行工具变量回归AI 解读

Summary The relevance condition of integrated conditional moment (ICM) estimators is significantly weaker than the conventional instrumental variable's in at least two respects: (1) consistent estimation without excluded instruments is possible, provided endogenous covariates are nonlinearly mean-dependent on exogenous covariates, and (2) endogenous covariates may be uncorrelated with but mean-dependent on instruments. These remarkable properties notwithstanding,…

半参数因子模型的可行加权投影主成分分析AI 解读

Summary Various factor estimation procedures have been developed, based on the latent factor model. They often consider general conditions that allow for correlations and heteroscedasticity. However, the conventional principal components method does not efficiently estimate the parameters. It also does not accommodate additional covariates, which explain the unknown factors, even if they are available. In particular, a few aggregated macroeconomic variables can be used as…

密度比模型下两个半连续总体基尼指数的半参数推断AI 解读

Summary The Gini index is a popular inequality measure with many applications in social and economic studies. This paper studies semi-parametric inference on the Gini indices of two semi-continuous populations. We characterise the distribution of each semi-continuous population by a mixture of a discrete point mass at zero and a continuous skewed positive component. A semi-parametric density ratio model is then employed to link the positive components of the two…

含交互效应的空间动态面板IV估计：大样本理论与银行风险态度应用AI 解读

Summary This paper develops a new instrumental variables estimator for spatial, dynamic panels with interactive effects under large N and T asymptotics. For this class of models, most approaches available in the literature are based on quasi-maximum likelihood estimation. The approach put forward here is appealing from both a theoretical and a practical point of view for a number of reasons. First, it is linear in the parameters of interest and computationally inexpensive.…

条件分位数中的样本选择检验AI 解读

Summary This paper provides distribution free tests for detecting sample selection in conditional quantile functions. The first test is an omitted predictor test with the propensity score as the omitted variable. In the case of rejection we cannot distinguish between rejection due to genuine selection or to misspecification. Thus, we suggest a second test using only individuals with propensity score close to one. The latter relies on an ‘identification at infinity’ argument,…

自动化的有益影响：来自专利文本的新证据AI 解读

Abstract We provide a new measure of automation based on patents and study its employment effects. Classifying all U.S. patents granted between 1976 and 2014 as automation or nonautomation patents, we document a strong rise in the number and share of automation patents. We link patents to their industries of use and to commuting zones. To estimate the effect of automation, we use an instrumental variables strategy that relies on innovations developed independently from U.S.…

高维面板函数型时间序列的统计推断AI 解读

Abstract In this paper, we develop statistical inference tools for high-dimensional functional time series. We introduce a new concept of physical dependent processes in the space of square integrable functions, which adopts the idea of basis decomposition of functional data in these spaces, and derive Gaussian and multiplier bootstrap approximations for sums of high-dimensional functional time series. These results have numerous important statistical consequences.…

存在测量误差的连续处理效应非参数估计AI 解读

Abstract We identify the average dose–response function (ADRF) for a continuously valued error-contaminated treatment by a weighted conditional expectation. We then estimate the weights nonparametrically by maximising a local generalised empirical likelihood subject to an expanding set of conditional moment equations incorporated into the deconvolution kernels. Thereafter, we construct a deconvolution kernel estimator of ADRF. We derive the asymptotic bias and variance of…

基于多元谱积分 Frobenius 范数的模型识别AI 解读

Abstract We study the integral of the Frobenius norm as a measure of the discrepancy between two multivariate spectra. Such a measure can be used to fit time series models, and ensures proximity between model and process at all frequencies of the spectral density. We develop new asymptotic results for linear and quadratic functionals of the periodogram, and apply the integrated Frobenius norm to fit time series models and test whether model residuals are white noise. The…

利用时变工具变量估计与改进动态治疗方案AI 解读

Abstract Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected. In this work, we develop a framework of estimating properly defined ‘optimal’ DTRs with a time-varying instrumental variable (IV) when unmeasured covariates confound the treatment and outcome, rendering the potential outcome distributions only partially identified. We derive a novel Bellman equation under…

多因素条件下的单调响应曲面：估计与贝叶斯分类器AI 解读

Abstract We formulate the estimation of monotone response surface of multiple factors as the inverse of an iteration of partially ordered classifier ensembles. Each ensemble (called product-of-independent-probability-escalation (PIPE)-classifiers) is a projection of Bayes classifiers on the constrained space. We prove that the inverse of PIPE-classifiers (iPIPE) exists, and propose algorithms to efficiently compute iPIPE by reducing the space over which optimisation is…

以分布函数为结果的因果推断AI 解读

Abstract Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on outcomes coming from the Euclidean space Rp. However, it is increasingly common that complex datasets are best summarized as data points in nonlinear spaces. In this paper, we present a novel framework of causal effects for outcomes from the Wasserstein space of cumulative distribution functions, which in…

利用外部数据估计未来临床试验中协变量调整的效率增益AI 解读

Abstract We present a framework for using existing external data to identify and estimate the relative efficiency of a covariate-adjusted estimator compared to an unadjusted estimator in a future randomized trial. Under conditions, these relative efficiencies approximate the ratio of sample sizes needed to achieve a desired power. We develop semiparametrically efficient estimators of the relative efficiencies for several treatment effect estimands of interest with either…

贝叶斯金字塔：离散数据的可识别多层离散潜结构模型AI 解读

Abstract High-dimensional categorical data are routinely collected in biomedical and social sciences. It is of great importance to build interpretable parsimonious models that perform dimension reduction and uncover meaningful latent structures from such discrete data. Identifiability is a fundamental requirement for valid modeling and inference in such scenarios, yet is challenging to address when there are complex latent structures. In this article, we propose a class of…

异方差与相关误差线性模型的去偏及阈值化岭回归AI 解读

Abstract High-dimensional linear models with independent errors have been well-studied. However, statistical inference on a high-dimensional linear model with heteroskedastic, dependent (and possibly nonstationary) errors is still a novel topic. Under such complex assumptions, the paper at hand introduces a debiased and thresholded ridge regression estimator that is consistent, and is able to recover the model sparsity. Moreover, we derive a Gaussian approximation theorem…

通过曲率分析识别网络模型的潜在空间几何AI 解读

Abstract A common approach to modelling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection likelihood. More positive manifold curvature encourages more and tighter communities; negative curvature induces repulsion. We consistently estimate manifold type, dimension, and curvature from simply connected, complete Riemannian manifolds of constant curvature. We represent the graph as a noisy distance…