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区分暂时与持续成本无效率的银行业务模式贝叶斯非参数推断AI 解读

This paper introduces a novel econometric framework for identifying and modeling bank business models (BBMs), which dynamically evolve in response to changing financial and economic conditions. Building on the stochastic frontier literature, we extend the traditional cost-efficiency models by decomposing inefficiency into persistent and transient components. We propose a Bayesian nonparametric approach that adapts to the data through an infinite mixture model with…

非中心化随机波动率下结构向量自回归的部分识别AI 解读

We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails—unlike the common centred parameterization. This feature makes it well suited for…

结构突变、模型不确定性与因子选择AI 解读

This paper addresses the non-standard problem of detecting multiple structural breaks when the data-generating model in each regime is uncertain, with an application to factor selection in empirical asset pricing. Detection is based on the marginal likelihood of break points, obtained by a novel integration over all possible pairings of models across regimes, from all possible models within regimes. The optimal break points maximize this marginal likelihood. Applying this…

经济增长建模的贝叶斯方法：变量选择与截面依赖AI 解读

This paper introduces a Bayesian framework designed to address two critical econometric challenges in empirical growth studies: variable selection and cross-sectional dependence. We first propose a growth regression model that incorporates cross-sectional dependence through a multi-factor structure and accounts for variable selection with sparse coefficients. We then introduce a new Bayesian Adaptive LASSO estimator specifically designed for this model, capable of performing…

校正测量偏差：大型教育评估中国家排名的新框架AI 解读

International Large-scale Assessments (ILSAs), such as the Program for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS), are cornerstone tools for global educational research and policy-making. By benchmarking educational quality and performance trends, these assessments enable countries to evaluate and share effective pedagogical structures. Specifically, ILSAs employ Item Response Theory (IRT) models to rank…

利用特殊回归变量识别第一类与第二类错误概率：司法误判应用AI 解读

We nonparametrically identify Type I and Type II error rates when mistakes are unobserved. Our strategy reframes the setting as a misclassified binary choice model, using a special regressor to identify the misclassification probabilities. Identification requires an exclusion restriction and a large support condition. When the exclusion restriction is in doubt, we show how our estimands can be interpreted as bounds under a weaker monotonicity condition. Bounds are also…

利用双重机器学习估计直接与间接分位数处理效应AI 解读

We propose a framework to disentangle the quantile treatment effect of a binary treatment at a specific rank into an indirect quantile treatment effect that operates through a mediator and an unmediated direct quantile treatment effect. We establish identification results for these effects under the sequential ignorability assumption and propose double/debiased machine learning estimators, based on the efficient influence functions of the cumulative distribution functions of…

未观测混杂下预测算法的稳健设计与评估AI 解读

Abstract Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an identification problem under unobserved confounding — when selected and unselected units differ in unobserved ways that affect outcomes. We propose a framework for robust design and evaluation of predictive algorithms that bounds how much outcomes may differ between selected and unselected…

作为组织问题的国家能力AI 解读

Abstract We investigate how technologies that reduce the costs of monitoring by central authorities have shaped the historical transition from small patrimonial states to large bureaucratic organizations. Our analysis is based on a novel dataset that traces changes in the organizational structure and geographic presence of the U.S. federal government over the 19th century. To identify causal effects, we develop a new identification strategy that exploits the expansion of the…

含议价机制双向拍卖的非参数识别与估计AI 解读

This article studies the nonparametric identification and estimation of double auctions with one buyer and one seller. This model assumes that both bidders submit their own sealed bids, and the transaction price is determined by a weighted average between the submitted bids when the buyer’s offer is higher than the seller’s ask. It captures the bargaining process between two parties. Working within this double auction model, we first establish the nonparametric…

线性模型中利用异方差进行稳健推断AI 解读

Summary We study inference via heteroskedasticity in linear models commonly used for macroeconomic policy analysis, where covariate endogeneity must often be addressed with limited time and data. Our framework nests standard heteroskedasticity-based approaches, allows for new non-nested restrictions, and does not require ex ante regime labelling. We propose an easily implementable weak identification robust test and derive sufficient conditions for its validity. Simulation…

合成控制的识别与推断：近端因果推断框架AI 解读

Synthetic control (SC) methods are commonly used to estimate the treatment effect on a single treated unit in panel data settings. An SC is a weighted average of control units built to match the treated unit, with weights typically estimated by regressing pre-treatment outcomes and measured covariates of the treated unit to those of the control units. However, the classical SC method was primarily proposed for empirical settings where a good pre-treatment fit is attainable…

收入不平等与就业创造AI 解读

Abstract We propose a novel channel through which rising income inequality affects job creation and macroeconomic outcomes. High-income households save relatively more in stocks and bonds but less in bank deposits. A rising top income share thereby increases the relative financing cost for bank-dependent firms, which in turn create fewer jobs compared to other firms. Exploiting variation in top income shares across US states and an instrumental variable strategy, we provide…

迈向个体层面数据融合的统一半参数理论AI 解读

We consider inference about a finite-dimensional parameter integrating samples from independent sources. A recently developed theory considers scenarios where sources align with subsets of the conditional distributions of a single factorization of the joint target distribution. While this theory applies in many settings, it falls short in important data fusion problems, such as two-sample instrumental variable analysis, settings that integrate data from epidemiological…

非零中位数 GARCH 型噪声下重尾 AR 模型的两步估计AI 解读

This paper develops a novel two-step estimating procedure for heavy-tailed AR models with nonzero median GARCH-type noises, allowing for time-varying volatility. We first establish the self-weighted quantile regression estimator (SQE) across all quantile levels τ∈(0,1) for the AR parameters θ0. We show that the SQE, less a bias, converges weakly to a Gaussian process at a rate of n−1/2. The bias is zero if and only if τ equals τ0, the probability that the noise is less than…

矩阵时间序列 CP 因子模型的识别与估计AI 解读

We propose a new method for identifying and estimating the CP-factor models for matrix time series. Unlike the generalized eigenanalysis-based method (J. R. Stat. Soc. Ser. B. Stat. Methodol. 85 (2023) 127–148) for which the convergence rates of the associated estimators may suffer from small eigengaps as the asymptotic theory is based on some matrix perturbation analysis, the proposed new method enjoys faster convergence rates which are free from any eigengaps. It achieves…

部分识别变系数模型的子向量推断AI 解读

This article considers a general class of varying coefficient models defined by a set of moment equalities and/or inequalities, where unknown functional parameters are not necessarily point-identified. We propose an inferential procedure for a subvector of the varying parameters and establish the asymptotic validity of the resulting confidence sets uniformly over a broad family of data-generating processes. We also propose a practical specification test for a set of…

大量弱工具变量下的条件似然比检验AI 解读

This article extends the validity of the conditional likelihood ratio (CLR) test developed by Moreira (2003, Econometrica 71(4), 1027-–1048) to instrumental variable regression models with unknown homoskedastic error variance and many weak instruments. We argue that the conventional CLR test with estimated error variance loses exact similarity and is asymptotically invalid in this setting. We propose a modified critical value function for the likelihood ratio (LR) statistic…

没有工具变量时的边际处理效应AI 解读

ABSTRACT We propose a method for defining, identifying, and estimating the marginal treatment effect (MTE) without imposing the instrumental variable (IV) assumptions of independence, exclusion, and separability (or monotonicity). Under a new definition of the MTE based on reduced‐form treatment error that is statistically independent of the covariates, we find that the relationship between the MTE and standard treatment parameters holds in the absence of IVs. We provide a…

存在非均衡行为的不完全信息离散博弈的非参数识别AI 解读

ABSTRACT This paper studies empirical discrete‐choice games with incomplete information under nonparametric specifications of both the payoff function and the distribution of private information. It relaxes the standard global equilibrium assumption —under which players follow equilibrium strategies for all realizations of the control variables—and introduces a weaker local equilibrium assumption that requires the equilibrium restriction only for some, but not all,…

通胀、战争债券与1950年代的选民反弹AI 解读

Abstract We study the role of war bonds and inflation in post-WWII federal elections. After WWII, major bouts of inflation in 1946-48 and 1950-51 depressed the real returns of the bonds sold to households during the war. In a difference-in-differences framework, we find that counties with higher war bond purchases shifted their votes towards the Republican Party in postwar elections. To address the endogeneity of war bond purchases, we use an instrumental variables design,…

全球化与发展阶梯：推向顶端还是困于底部？AI 解读

Abstract We study the relationship between international trade and development in a model where countries differ in their capability, goods differ in their complexity, and capability growth is a function of a country’s pattern of specialization. Theoretically, we show that it is possible for international trade to increase capability growth in all countries and, in turn, to push all countries up the development ladder. This occurs if (i) shifting employment towards more…

结构动态离散选择模型的反事实分析AI 解读

Abstract Discrete choice data allow researchers to recover differences in utilities, but these differences may not suffice to identify policy-relevant counterfactuals of interest. In fact, in the case of dynamic discrete choice models, only a narrow set of counterfactuals are point-identified. In this paper, we explore how much one can learn about counterfactual outcomes of interest within this framework. We focus on the partial identification of counterfactuals, while…

非高斯结构向量自回归中的时变冲击传导AI 解读

Summary This paper analyses possibly time-varying shock transmission in structural vector autoregressive (VAR) models when the reduced-form VAR coefficients are time invariant and the shocks are identified through non-Gaussianity. To check for possible time variation in the impulse responses, we propose Wald tests for two situations: (1) homoskedastic and (2) heteroskedastic structural shocks with changes in the unconditional variances. For the latter case, the challenge is…