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交互固定效应高维面板模型的估计与推断AI 解读

We develop new econometric methods for estimation and inference in high‐dimensional panel data models with interactive fixed effects. Our approach can be regarded as a nontrivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross‐sectionally averaged…

有限样本下动态离散博弈的同质性检验AI 解读

The literature on dynamic discrete games often assumes that the conditional choice probabilities and the state transition probabilities are homogeneous across markets and over time. We refer to this as the “homogeneity assumption” in dynamic discrete games. This assumption enables empirical studies to estimate the game's structural parameters by pooling data from multiple markets and from many time periods. In this paper, we propose a hypothesis test to evaluate whether the…

随机系数潜在效用模型的识别AI 解读

This paper provides nonparametric identification results for random coefficient distributions in perturbed utility models. We cover discrete choice and models of multiple purchases. We establish identification using variation in mean quantities. The results apply even when an analyst observes only aggregate demands but not whether goods are chosen together. The identification results use exclusion restrictions on covariates and independence between random slope coefficients…

稀有灾难下的学习AI 解读

Financial crises appear to have long‐lasting effects, even after the crisis itself has passed. This paper offers a simple explanation based on Bayesian learning from rare events. Agents face a latent and time‐varying probability of economic disaster. When a disaster occurs, learning results in greater effects on asset prices because agents update their probability of future disasters. Moreover, agents' belief that the disaster risk is high can rationally persist for years,…

日内波动率曲线均值平稳性的检验AI 解读

We develop a test for mean stationarity of latent volatility curves using high‐frequency data. To derive the asymptotic test size and power, we establish a functional invariance principle for semimartingales under a strong mixing condition. The power properties are analyzed under alternatives featuring deterministic trends in the volatility curve dynamics. Application to S&P 500 futures data provides strong evidence of nonstationary variation in the volatility pattern, with…

使用影子利率 VAR 进行预测AI 解读

Vector autoregressions (VARs) are popular for forecasting, but ill‐suited to handle occasionally binding constraints, like the effective lower bound on nominal interest rates. We examine reduced‐form “shadow rate VARs” that model interest rates as censored observations of a latent shadow rate process and develop an efficient Bayesian estimation algorithm that accommodates large models. When compared to a standard VAR, our better‐performing shadow rate VARs generate superior…

平稳与非平稳自回归时变参数模型中的推断AI 解读

This paper considers nonparametric estimation and inference in first‐order autoregressive (AR(1)) models with deterministically time‐varying parameters. A key feature of the proposed approach is to allow for time‐varying stationarity in some time periods, time‐varying nonstationarity (i.e., unit root or local‐to‐unit root behavior) in other periods, and smooth transitions between the two. The estimation of the AR parameter at any time point is based on a local least squares…

政策约束下最优动态处理分配规则的估计AI 解读

Many policies involve dynamics in their treatment assignments where individuals receive sequential interventions over multiple stages. We study estimation of an optimal dynamic treatment regime that guides the optimal treatment assignment for each individual at each stage based on their history. We propose an empirical welfare maximization approach in this dynamic framework, which estimates the optimal dynamic treatment regime using data from an experimental or…

动态有序面板 Logit 模型AI 解读

This paper studies a dynamic ordered logit model for panel data with fixed effects. The main contribution of the paper is to construct a set of valid moment conditions that are free of the fixed effects. The moment functions can be computed using four or more periods of data, and the paper presents sufficient conditions for the moment conditions to identify the common parameters of the model, namely the regression coefficients, the autoregressive parameters, and the…

收入风险与异质性的预期收入增长路径AI 解读

We add to the debate about whether models of earnings dynamics should allow for unobservable heterogeneity in expected earnings growth rates by examining implications for a statistic originally proposed to estimate the variance of persistent earnings shocks. While that statistic is unbiased under a common specification, we derive biases that would arise under alternative models and use them to draw inferences about their empirical relevance and to estimate key parameters.…

弱外生性下的线性回归AI 解读

This paper studies linear time‐series regressions with many regressors. Weak exogeneity is the most used identifying assumption in time series. Weak exogeneity requires the structural error to have zero conditional expectation given present and past regressor values, allowing errors to correlate with future regressor realizations. We show that weak exogeneity in time‐series regressions with many controls may produce substantial biases and render the least squares (OLS)…

商品市场中的储存作用：基于粮食数据的间接推断AI 解读

We develop an indirect inference approach relying on a linear supply and demand model serving as an auxiliary model to provide the first full empirical test of the rational expectations commodity storage model. We build a rich storage model that incorporates a supply response and four structural shocks and show that exploiting information on both prices and quantities is critical for relaxing previous restrictive identifying assumptions and assessing the empirical…

理解长时间跨度下的高频观测回归AI 解读

In this paper, we analyze regressions with observations collected at small time intervals over a long period of time. For the formal asymptotic analysis, we assume that samples are obtained from continuous time stochastic processes, and let the sampling interval δ shrink down to zero and the sample span T increase up to infinity. In this setup, we show that the standard Wald statistic diverges to infinity and the regression becomes spurious as long as δ → 0 sufficiently fast…

教育与岗位纵向错配的经济后果AI 解读

We study two first‐order economic consequences of vertical mismatch, using a simple (neoclassical) model of under and overemployment. Individuals of high type can perform both skilled and unskilled jobs, but only a fraction of low‐type workers can perform skilled jobs. People have different costs over these jobs. First, we calibrate the model to match U.S. CPS time series since the 1980s. To control for unobserved heterogeneity, we compute wages based on workers who have…

连续更新 GMM 的双重稳健推断AI 解读

We propose the double robust Lagrange multiplier (DRLM) statistic for testing hypotheses specified on the minimizer of the population continuous updating objective function. The (bounding) χ 2 limiting distribution of the DRLM statistic is robust to both misspecification and weak identification, hence its name. The minimizer is the so‐called pseudo‐true value, which equals the true value of the structural parameter under correct specification. To emphasize its importance for…

单调最佳反应博弈经验分析的序数方法AI 解读

We develop a nonparametric and ordinal approach for testing pure strategy Nash equilibrium play in games with monotone best responses, such as those with strategic complements/substitutes. The approach makes minimal assumptions on unobserved heterogeneity, requires no parametric assumptions on payoff functions, and no restriction on equilibrium selection from multiple equilibria. The approach can also be extended in order to make inferences and predictions. Both…

教学能否被教会？秘鲁农村教师教学技能的大规模提升AI 解读

We evaluate the impact of a large‐scale teacher coaching program in Peru, a context with high teacher turnover, on teachers' pedagogical skills and student learning. Previous studies find that small‐scale coaching programs can improve teaching of reading and science in developing countries. However, scaling up can reduce programs' effectiveness, and teacher turnover can erode compliance and cause spillovers onto non‐program schools. We develop a framework that defines…

金融危机宏观模型的估计：内生状态切换方法AI 解读

We develop a new model of cycles and crises in emerging markets, featuring an occasionally binding borrowing constraint and stochastic volatility, and estimate it with quarterly data for Mexico since 1981. We propose an endogenous regime‐switching formulation of the occasionally binding borrowing constraint, develop a general perturbation method to solve the model, and estimate it using Bayesian methods. We find that the model fits the Mexican data well without…

多维类型保险模型的计量分析AI 解读

In this paper, we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damages and allows for several claims, while insurees choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverages. Our identification…

含不可观测异质性与大状态空间的不完全信息博弈估计和推断AI 解读

Building on the sequential identification result of Aguirregabiria and Mira (2019), this paper develops estimation and inference procedures for static games of incomplete information with payoff‐relevant unobserved heterogeneity and multiple equilibria. With payoff‐relevant unobserved heterogeneity, sequential estimation and inference face two main challenges: the matching‐types problem and a large number of matchings . We tackle the matching‐types problem by constructing a…

处理效应异质性下的动态断点回归AI 解读

Regression discontinuity is a popular tool for analyzing economic policies or treatment interventions. This research extends the classic static RD model to a dynamic framework, where observations are eligible for repeated RD events and, therefore, treatments. Such dynamics often complicate the identification and estimation of long‐term average treatment effects. Empirical papers with such designs have so far ignored the dynamics or adopted restrictive identifying…

利用两个次序统计量进行反卷积AI 解读

Economic data are often contaminated by measurement errors and truncated by ranking. This paper shows that the classical measurement error model with independent and additive measurement errors is identified nonparametrically using only two order statistics of repeated measurements. The identification result confirms a hypothesis by Athey and Haile (2002) for a symmetric ascending auction model with unobserved heterogeneity. Extensions allow for heterogeneous measurement…

面板数据中对分配设计稳健的双向固定效应回归AI 解读

We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two‐way‐fixed‐effects specification with unit‐specific weights that arise from a model for the assignment mechanism. We show how to construct these weights in various settings, including the staggered adoption setting, where units opt into the treatment sequentially but permanently. The resulting estimator…

用于文本分析的稳健机器学习算法AI 解读

We study the Latent Dirichlet Allocation model, a popular Bayesian algorithm for text analysis. We show that the model's parameters are not identified, which suggests that the choice of prior matters. We characterize the range of values that the posterior mean of a given functional of the model's parameters can attain in response to a change in the prior, and we suggest two algorithms that report this range. Both of our algorithms rely on obtaining multiple Nonnegative…