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非参数 Gini–Frisch 界AI 解读

The Gini-Frisch bounds partially identify the constant slope coefficient in a linear equation when the explanatory variable suffers from classical measurement error. This paper generalizes these quintessential bounds to accommodate nonparametric heterogeneous effects. It provides suitable conditions under which the main insights that underlie the Gini-Frisch bounds apply to partially identify the average marginal effect of an error-laden variable in a nonparametric…

矩条件模型中识别失效的检测AI 解读

This paper develops an approach to detect identification failure in moment condition models. This is achieved by introducing a quasi-Jacobian matrix computed as the slope of a linear approximation of the moments on an estimate of the identified set. It is asymptotically singular when local and/or global identification fails, and equivalent to the usual Jacobian matrix which has full rank when the model is globally and locally identified. Building on this property, a simple…

正态混合误差下非平稳位置模型的极大似然估计AI 解读

We consider an observation-driven location model where the unobserved location variable is modeled as a random walk process and where the error variable is from a mixture of normal distributions. The time-varying location can be extended with a stationary process to account for cyclical and/or higher order autocorrelation. The mixed normal distribution can accurately approximate many continuous error distributions. We obtain a flexible modeling framework for the robust…

含协整解释变量的半参数单指数预测回归AI 解读

This paper considers the estimation of a semi-parametric single-index regression model that allows for nonlinear predictive relationships. This model is useful for predicting financial asset returns, whose observed behaviour resembles a stationary process, if the multiple nonstationary predictors are cointegrated. The presence of cointegrated regressors imposes a single-index structure in the model, and this structure not only balances the nonstationarity properties of the…

面板实验中的群体干扰AI 解读

The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit’s outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in this setting are now readily recognized, and partial remedies are well known. Less understood is the impact of population interference in panel experiments where treatment is sequentially randomized in the…

基于汇总表数据的免调参非参数密度估计AI 解读

Administrative data are often easier to access as tabulated summaries than in the original format due to confidentiality concerns. Motivated by this practical feature, we propose a novel nonparametric density estimation method from tabulated summary data based on maximum entropy and prove its strong uniform consistency. Unlike existing kernel-based estimators, our estimator is free from tuning parameters and admits a closed-form density that is convenient for post-estimation…

高维低秩张量自回归时间序列建模AI 解读

Modern technological advances have enabled an unprecedented amount of structured data with complex temporal dependence, urging the need for new methods to efficiently model and forecast high-dimensional tensor-valued time series. This paper provides a new modeling framework to accomplish this task via autoregression (AR). By considering a low-rank Tucker decomposition for the transition tensor, the proposed tensor AR can flexibly capture the underlying low-dimensional tensor…

基于局部线性化的矩不等式子向量推断AI 解读

This paper introduces a bootstrap-based profiling inference method for subvectors in moment inequality models following insights from Bugni et al. (2017). Compared to their paper, the new method calculates the critical value by searching over a local neighborhood of a pre-estimator, instead of the whole null parameter space, to profile out nuisance parameters. In this way, non-linear moment conditions are simplified by linear expansion and the bootstrap iterates over…

匹配点：在三角模型中用协变量补充工具变量AI 解读

Models with a discrete endogenous variable and an instrument that takes on fewer values are common in economics. This paper presents a new method that matches pairs of covariates and instruments to restore the order condition in this scenario and to achieve point-identification of the outcome function. The outcome function must be monotonic in a scalar disturbance, but it can be nonseparable. The first stage for the discrete endogenous variable needs to have a multi-index…

神经网络资产定价中的显著性检验AI 解读

This study proposes a novel hypothesis test for evaluating the statistical significance of input variables in multi-layer perceptron (MLP) regression models. Theoretical foundations are established through consistency results and estimation rate analysis using the sieves method. To validate the test’s performance in complex and realistic settings, an extensive Monte Carlo simulation is conducted. Results of the simulation reveal that the test has a high power and low rate of…

含不可观测异质性的时空模型估计与自助法AI 解读

Proposed herein is a novel spatiotemporal model to characterize the unobserved heterogeneity across individuals using quantile-function-based correlated random effects and heteroscedastic innovations in a general framework. This model can be used to explore the influence of space-specific factors on latent effects at different quantile levels by controlling for spatiotemporal effects. A two-stage estimation procedure is introduced in which (i) the method of moments is used…

无单调性条件下弱可分模型的内生性处理AI 解读

We identify and estimate treatment effects when potential outcomes are weakly separable with a binary endogenous treatment. Vytlacil and Yildiz (2007) proposed an identification strategy that exploits the mean of observed outcomes, but their approach requires a monotonicity condition. In comparison, we exploit full information in the entire outcome distribution, instead of just its mean. As a result, our method does not require monotonicity and is also applicable to general…

参与状态误报时项目收益的界限识别AI 解读

Instrumental variables (IV) are commonly used to estimate treatment effects in case of noncompliance. However, program participation is often misreported in survey data and standard techniques are not sufficient to point identify and consistently estimate the effects of interest. In this paper, we show that the identifiable IV estimand that ignores treatment misclassification is a weighted average of local average treatment effects with weights that can also be negative.…

基于价格区间的最优非参数波动率估计AI 解读

We present a general framework for optimal nonparametric spot volatility estimation based on intraday range data, comprised of the first, highest, lowest, and last price over a given time-interval. We rely on a decision-theoretic approach together with a coupling-type argument to directly tailor the form of the nonparametric estimator to the specific volatility measure of interest and relevant loss function. The resulting new optimal estimators offer substantial efficiency…

高维分位数回归的假设检验AI 解读

Quantile regression has been an important analytical tool in econometrics since it was proposed in 1970s. Many advantages make it still popular in the era of big data. This paper focuses on the testing problems of high-dimensional quantile regression, supplementing to robust methods in the literature of high-dimensional hypothesis testing. We first construct a new test statistic based on the quantile regression score function. The new test statistic avoids the inverse…

系统性价格停滞AI 解读

Asset prices are stale. We define a measure of systematic (market-wide) staleness as the percentage of small price adjustments across multiple assets. A notion of idiosyncratic (asset-specific) staleness is also established. For both systematic and idiosyncratic staleness, we provide a limit theory based on joint asymptotics relying on increasingly-frequent observations over a fixed time span and an increasing number of assets. Using systematic and idiosyncratic staleness as…

应税收入弹性的极大似然聚束估计AI 解读

Summary This paper develops a maximum likelihood (ML) bunching estimator of the elasticity of taxable income (ETI). Our structural approach provides a natural framework to simultaneously account for unobserved preference heterogeneity and optimization errors and for measuring their relative importance. We characterize the conditions under which the parameters of the model are identified and show that the ML estimator performs well in terms of bias and precision. The paper…

住房市场繁荣与衰退期间边际消费倾向是否改变？AI 解读

Summary We extend a widely used semi‐structural model to identify and estimate dynamic consumption elasticities with respect to transitory income shocks. Applying our model to household survey data, we find a structural break in marginal propensities to consume following the end of the housing market boom, with the average across households increasing significantly. There is important heterogeneity by different household balance sheet characteristics, and the increase in the…

网络模型中的异质性与动态变化AI 解读

Summary We propose an empirical spatial modeling framework that allows for both heterogeneity and dynamics in economic network connections. We establish the model's stationarity and ergodicity properties and show that the model's implied filter is invertible. While highly flexible, the model is straightforward to estimate by maximum likelihood. We apply the model to three datasets for Eurozone sovereign credit risk over the period Dec‐2009 to Dec‐2022. Accounting for both…

条件处理效应分布的部分识别与推断AI 解读

Summary This paper considers identification and inference for the distribution of treatment effects conditional on observable covariates. Since the conditional distribution of treatment effects is not point identified without strong assumptions, we obtain bounds on the conditional distribution of treatment effects by using the Makarov bounds. We also consider the case where the treatment is endogenous and propose two stochastic dominance assumptions to tighten the bounds. We…

使用分位数向量自回归进行预测与压力测试AI 解读

Summary A quantile vector autoregressive (VAR) model, unlike standard VAR, traces the interaction among the endogenous random variables at any quantile. Quantile forecasts are obtained by factorizing the joint distribution in a recursive structure but cannot be obtained from reduced form estimation. Identification strategies and structural quantile impulse response functions are derived as generalization of the VAR model. The model is estimated using real and financial…

含零无效率质量点的随机前沿惩罚筛估计AI 解读

Summary Stochastic frontier models for cross‐sectional data typically assume that the one‐sided distribution of firm‐level inefficiency is continuous. However, it may be reasonable to hypothesize that inefficiency is continuous except for a discrete mass at zero capturing fully efficient firms (zero‐inefficiency). We propose a sieve‐type density estimator for such a mixture distribution in a nonparametric stochastic frontier setting under a unimodality‐at‐zero assumption.…

能力代理变量估计教育回报的有效性：基于因子模型的评估AI 解读

Summary A common approach to addressing ability bias is to augment the earnings‐schooling regression with proxies for cognitive and non‐cognitive skills. We evaluate this approach using a factor model framework, which allows consistent estimation of the returns to schooling without relying on proxies. The factor model estimators may be viewed as implicitly estimating proxy measurement error and/or accounting for omitted dimensions of ability. A bias decomposition quantifies…

重新评估增长脆弱性AI 解读

Summary This paper replicates the results of Adrian et al. ( American Economic Review , 2019) that GDP growth volatility is mainly driven by the lower quantiles of the distribution which is predicted by the financial condition. It extends their study by estimating the model with the IVX‐QR estimator of Lee ( Journal of Econometrics , 2016) and double weighted estimator of Cai et al. ( Journal of Econometrics , 2022) considering that the financial condition index is highly…