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通过一阶条件揭示工厂—产品生产率：对 Orr（2022）的稳健复现AI 解读

ABSTRACT We assess the replicability of Orr's (2022) method for estimating within‐plant productivity across product lines, which combines demand estimation with cost minimization. The original study uses input price shocks in other output markets as instrumental variables, with exclusion restrictions based on downstream purchase shares. Reconstructing the original dataset of Indian machinery producers from 2000–2007, we reproduce the main productivity patterns and…

处理效应估计的 PCDID 方法：进一步考察AI 解读

ABSTRACT In the present paper, we study the so‐called “PCDID” approach to treatment effects estimation in panels with interactive effects where the factors represent trends whose effect need not be parallel across the cross‐sectional units. Our interest in this step‐wise approach originates with the observation that the interactive effects are ignored in the first step, which should not be possible without affecting subsequent steps. We confirm that the estimated second‐step…

天然气价格的实时预测AI 解读

ABSTRACT This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in complexity and economic content. We find that considerable reductions in mean‐squared prediction error relative to a no‐change benchmark can be achieved in real time for horizons of up to 2 years. A particularly promising model is a vector autoregressive (VAR) model that…

含交互固定效应的高维离散选择模型及其因果推断应用AI 解读

ABSTRACT We propose a two‐step procedure to estimate a high dimensional discrete choice panel with interactive fixed effects where the initial and final estimators are obtained via a nuclear‐norm regularized (NNR) maximum likelihood estimation and post‐NNR iterated estimation, respectively. We apply the method to make counterfactual predictions of choice probabilities. Simulations demonstrate nice finite sample performance in estimation and tests. An illustrative application…

检验预测效率的工具变量方法AI 解读

ABSTRACT We study a specific form of forecast efficiency that requires forecast errors to be unpredictable from forecast revisions. One approach aggregates forecasts and estimates an aggregated efficiency regression, while another estimates the relationship by running separate regressions for each individual and then aggregating. We demonstrate that both estimators can be asymptotically biased in the presence of public noise. To address these biases, we propose instrumenting…

欧洲总体层面主观幸福感随收入饱和的计量证据AI 解读

ABSTRACT The Easterlin paradox explores the link between subjective well‐being (SWB) and income, questioning if a saturation point exists where more income no longer boosts SWB. In the literature, the studies dealing with a saturation point of SWB at the cross‐sectional level are ambiguous. The results of this study provide evidence for its existence. The EU‐SILC data are used to test for satiation by means of kink regressions and generalized additive models. The unique…

收入增长中不存在的矩AI 解读

ABSTRACT This study addresses the limitations of traditional earnings risk measures, which often rely on moments such as variance, skewness, and kurtosis. For heavy‐tailed distributions, these moments may not exist, challenging such analyses. We propose robust conditional Pareto exponents as novel measures of earnings risk, with accompanying estimation and inference methods. Using UK NESPD and US PSID data, we find (1) moments often fail to exist; (2) tail risk rises over…

不确定的短期约束与统计识别的结构向量自回归AI 解读

ABSTRACT This study proposes a combination of a statistical identification approach with potentially invalid short‐run zero restrictions. The estimator shrinks towards imposed restrictions and stops shrinkage when the data provide evidence against a restriction. We demonstrate that incorporating valid restrictions through the shrinkage approach enhances the efficiency of the statistically identified estimator, and the impact of invalid restrictions vanishes as the sample…

检验两个参数的符号一致性AI 解读

ABSTRACT We test the null hypothesis that two parameters have the same sign, assuming that (asymptotically) normal estimators are available. Examples of this problem include the analysis of heterogeneous treatment effects, causal interpretation of reduced‐form estimands, meta‐studies, and mediation analysis. A number of tests were recently proposed. We recommend a test that is simple and rejects more often than many of these recent proposals. Like all other tests in the…

低噪声动态因子模型中 EM 算法的缓慢收敛AI 解读

ABSTRACT This paper addresses the slow convergence of the expectation–maximization (EM) algorithm in the estimation of low‐noise dynamic factor models, commonly used in macroeconomic nowcasting and forecasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a low‐noise environment, leading to inaccurate estimates of factor loadings and factor realizations. An adaptive version of EM considerably speeds up convergence, producing…

差分带来的差异：观测冲击的局部投影应使用水平还是差分？AI 解读

ABSTRACT We show there are large finite‐sample estimation improvements from estimating local projections (LP) in a cumulated differences (long‐differenced) specification vs. a specification in levels when the impulse response of interest is to an externally identified (“observed”) shock. The long‐differenced specification substantially reduces, and in many cases eliminates, estimation bias, as well as significantly improves confidence interval coverage. These improvements…

非线性 DSGE 模型的有限样本识别稳健推断AI 解读

ABSTRACT We develop identification‐robust likelihood‐free simultaneous confidence sets for dynamic stochastic general equilibrium models, without relying on linear approximations. Our methodology integrates simulation estimation methods using auxiliary statistics with Monte Carlo test principles. Results cover deep parameters and impulse responses. Auxiliary statistics include coefficients of linear and nonlinear vector autoregressions and local projections. Proposed…

基于局部投影的双重差分方法AI 解读

ABSTRACT We propose a local projections (LPs)‐based difference‐in‐differences (DiD) approach that subsumes many of the recent solutions proposed in the literature to address possible biases arising from negative weighting. We combine LPs with a flexible “clean control” condition to define appropriate sets of treated and control units. Our proposed LP‐DiD estimator can be implemented with various weighting and normalization schemes for different target estimands, can be…

双重差分因果森林及其在挪威工资税负担归宿中的应用AI 解读

ABSTRACT This paper introduces the difference‐in‐difference causal forest (DiDCF) method, which extends the causal‐forest technique for estimating heterogeneous treatment effects to settings with dynamic treatment effects. Regular causal forests require independence between treatment assignment and the outcome variable (after conditioning out observables). In contrast, DiDCFs provide consistent estimates with a parallel trend assumption. DiDCFs can be used to create…

非线性趋势结果的政策评价：美国新冠疫苗接种率AI 解读

ABSTRACT This paper discusses pitfalls in two way fixed effects (TWFE) regressions when the outcome variables contain nonlinear and possibly stochastic trend components. If a policy change shifts trend paths of outcome variables, TWFE estimation can distort results and invalidate inference, especially in a context of evolving policy decisions. A robust solution is proposed by allowing for dynamic club membership empirically using a relative convergence test procedure. The…

相关误差对增长脆弱性结论的挑战AI 解读

ABSTRACT We examine the impact of correlated errors on findings regarding fluctuations in US GDP growth, which are primarily driven by the lower quantiles of the distribution as predicted by financial conditions. A proposed zero‐correlation test reveals the presence of correlated residuals in the quantile regression, introducing endogeneity and raising concerns about the validity of the previous analysis. In response, we extend the model by including two additional lags of…

多维断点回归设计的操纵检验AI 解读

ABSTRACT The causal inference model for the regression discontinuity design (RDD) relies on assumptions that imply the continuity of the density of the assignment (running) variable. The test for this implication is commonly referred to as the manipulation test and is regularly reported in applied research to strengthen the design's validity. The multidimensional RDD (MRDD) extends the RDD to contexts where treatment assignment depends on several running variables. This…

面板数据政策评价方法的预测区间AI 解读

ABSTRACT We consider the inference on individual and time specific treatment effects on the treated within the framework of panel data approach for programme evaluation. We formulate the target problem as constructing prediction intervals for high‐dimensional linear regressions with weakly dependent data. Post‐LASSO OLS is used for estimation, while dependent wild bootstrap and simple residual bootstrap are used for the construction of prediction intervals. The proposed…

人力资本积累决定因素的部分识别：教师应用AI 解读

ABSTRACT This paper views career growth in teacher quality through the lens of human capital theory to understand the roles of on‐the‐job training (OJT) and learning by doing (LBD) in human capital formation. If OJT is the primary determinant of human capital, incentive pay policies could create a dynamic multitasking problem, leading teachers to reduce their human capital investments, thereby lowering future student achievement. In contrast, teacher human capital and future…

政策相关处理参数对单调性违背的敏感性AI 解读

ABSTRACT This paper proposes a method to bound policy‐relevant treatment parameters (PRTPs) when the monotonicity assumption that the IV affects individuals' treatment response in the same direction is weakened. The bounding framework uses the proportion of defiers relative to compliers as a sensitivity parameter and yields an identified set that is an interval. The method is illustrated in an empirical application where the same‐sex instrument was used to calculate the…

内生网络中二元结果的同伴效应：战略互补与从众偏好AI 解读

ABSTRACT This paper introduces a generalized model of peer effects for binary outcomes, based on a network game that accounts for strategic complementarity (influence of the number of peers that select the same action) and conformity to social norms (penalizing deviations from the average peers' action). We derive conditions for a unique Bayesian Nash equilibrium under rational expectations about peers' behavior. Unobserved individual heterogeneity affecting network…

基于随机森林的面板数据政策评价方法AI 解读

ABSTRACT It is challenging to conduct controlled experiments to assess the impacts of social policy. To address this, past studies propose a panel data approach using factor models to estimate average treatment effects. The selection of control units is a critical step to balance the goodness of fit within‐sample with the posttreatment forecasting error when the number of observed potential control units is large. In this study, we propose using random forests, an ensemble…

结合代理变量与叙事符号限制：重新考察技术冲击的影响AI 解读

ABSTRACT This paper proposes a novel integration of the proxy structural vector autoregression approach with narrative sign restrictions to identify multiple shocks with multiple instrumental variables. We show that this combination leads to informative inferences on shock effects through both analytical demonstrations and Monte Carlo simulations. Applying this method to identify anticipated and unanticipated technology shocks, we find that anticipated shocks account for…

重新考察高维选择中的群体差异：方法及国会演讲应用AI 解读

ABSTRACT Gentzkow, Shapiro, and Taddy, Econometrica 87, no. 4, 2019 (henceforth GST), use a supervised text‐based regression model to assess changes in partisanship in US congressional speech over time. Their estimates imply that partisanship is far greater in recent years than in the past and that it increased sharply in the early 1990s. The paper at hand provides a replication in the wide sense of GST by complementing their analysis in three ways. First, we propose an…