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集体家庭消费中的规模经济AI 解读

ABSTRACT Browning et al. (2013) model collective‐household economies of scale in goods consumption by having each good be partly shared, instead of each good being public or private. We modify their model to achieve simple point identification and estimation of the economies of scale of consumption of each good, and we provide a new index of household level economies of scale. Our model has a linear form, permits households of varying composition, including children, and…

律师的价值：以附带来源规则变更作为无效工具变量AI 解读

ABSTRACT The value of lawyers to their clients is notoriously difficult to estimate because of endogeneity. We utilize modifications to the collateral source (CS) rule that require reducing trial awards by the amount of payments from first‐party insurance as an instrument for hiring a lawyer. The problem with our instrument is that modifications to the CS rule have a direct effect on recovery and, hence, violate the exclusion restriction required for a valid instrument. We…

重新审视石油供给新闻冲击：代理变量与非高斯结构向量自回归AI 解读

ABSTRACT We replicate a study by Känzig ( American Economic Review , 111 (2021), 1092–1125), who employs structural vector autoregressive techniques to examine the impact of changes in oil supply expectations on the price of oil and other macroeconomic aggregates. Känzig identifies an oil supply news shock by constructing a proxy from OPEC announcements about their production plans. As an alternative approach, we use non‐Gaussianity and independence to identify structural…

样本选择下观测数据中总体平均与分位处理效应的部分识别AI 解读

ABSTRACT This article partially identifies population treatment effects in observational data under both non‐random treatment assignment and sample selection. Bounds are provided for both average and quantile population treatment effects, combining assumptions for the selected and the non‐selected subsamples. We show how different assumptions help narrow identification regions, and we illustrate our methods by partially identifying the effect of maternal education on the…

用分布回归双重差分评估最低工资引入对工资、收入与工时分布的影响AI 解读

ABSTRACT We estimate the causal effects of the introduction of the minimum wage in Germany on the distributions of hourly wages, monthly earnings, and hours worked, combining rich large‐scale data sources. We compare linear and non‐linear versions of distribution regression difference‐in‐differences (DR‐DiD) and also estimate bivariate DR‐DiD. We show that the introduction of the minimum wage in 2015 led to shifts in low hourly wages around the minimum wage threshold…

非线性模型中缺失协变量的插补AI 解读

ABSTRACT I propose a new imputation estimator for missing covariate values in nonlinear models. The estimator provides efficiency gains relative to just using the complete cases, and is consistent for any model that falls under M‐estimation. This is unlike the commonly used dummy variable method and regression imputation, which I show to be generally inconsistent in nonlinear models. The proposed estimator is straightforward to implement, and relies only on the commonly used…

达成议价：工资议价冲击的叙事识别AI 解读

ABSTRACT Wage markup shocks, an important driver of business cycles, are hard to pin down. We quantify the importance of exogenous variation in wage bargaining power, one source of those shocks, from German minimum wage introduction episodes and strikes. This disciplines the impulse responses of unemployment and output, and sharpens inference for other variables, which behave consistently with predictions from search‐and‐matching models. We find that wage bargaining shocks…

两项双层随机前沿研究的复现AI 解读

ABSTRACT We replicate the two foundational studies which proposed the two‐tier stochastic frontier model, first in the cross‐sectional setting and then for the fixed effects panel data model. We explore matters of software then and now, different mathematical formulations of the likelihood, and alternative current software tools. We also highlight several data issues that arose in both replications.

自回归逆Wishart多元随机波动模型及其因子扩展AI 解读

ABSTRACT Despite its conceptual appeal, the autoregressive inverse‐Wishart (AIW) multivariate stochastic volatility model has been hindered by inefficient sampling methods. The existing samplers for the latent covariance matrix severely suffer from the curse of dimensionality and only work when the dimension is very low. In this paper, we introduce a new proposal for the latent covariance matrix in the AIW model, which demonstrates far better scalability. We introduce…

宏观审慎政策的尾部传导：叙事识别证据AI 解读

ABSTRACT We estimate the causal effects of macroprudential policies on the entire distribution of GDP growth for advanced European economies using a narrative‐identification strategy in a quantile‐regression framework. While macroprudential policy has near‐zero effects on the center of the GDP‐growth distribution, tighter policy brings benefits by reducing the variance of future growth, significantly boosting the left tail while simultaneously reducing the right. Assessing a…

欧元区及其成员国大型数据集与共同货币政策的动态效应AI 解读

ABSTRACT We introduce EA‐MD‐QD, a new publicly available dataset comprising 1136 macroeconomic time series for the euro area (EA) and its ten largest member countries observed at monthly or quarterly frequency. Since January 2024, EA‐MD‐QD has been updated monthly and continuously revised, providing a valuable resource for policy analysis in the EA. Using EA‐MD‐QD, we study country‐specific impulse responses to an EA‐wide monetary policy shock. Results reveal moderate yet…

无向连接形成博弈中的网络外部性估计AI 解读

ABSTRACT This paper explores the existence of network externalities in link formation games of incomplete information. It extends the structural estimation method in Leung (2015) to games where links are undirected and proposals are only partially observed. We provide an econometric characterization of the proposed two‐step estimator and document its performance through a simulation exercise. When the estimation method is applied to data on risk‐sharing arrangements in a…

利用预测识别新闻冲击AI 解读

ABSTRACT We propose a method to identify the anticipated components of macroeconomic shocks in a structural VAR. We include empirical forecasts about each time series in the VAR. This introduces enough linear restrictions to identify every structural shock and to further decompose each one into “news” and “surprise” shocks. We estimate a VAR on U.S. time series using forecast data from the SPF, CBO, Federal Reserve, and asset prices. Unanticipated fiscal stimulus and…