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由分位数回归构造密度预测：宏观金融动态中的多峰性AI 解读

Summary Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two‐step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the “data speak.” Simulation evidence and an application revisiting GDP growth uncertainties in the United States…

利用多元收益识别和预测牛熊市场AI 解读

Summary Bull and bear market identification generally focuses on a broad index of returns through a univariate analysis. This paper proposes a new approach to identify and forecast bull and bear markets through multivariate returns. The model assumes that all assets are directed by a common discrete state variable from a hierarchical Markov switching model. The hierarchical specification allows the cross‐section of state‐specific means and variances to differ over bull and…

通过随机占优考察技能分布的尾部AI 解读

Summary Location choices of differently skilled workers are analyzed in previous work on labor mobility, which proposes a model that suggests thicker tails in the skill distributions of large cities. This paper replicates the empirical findings of this work by using quantile regression and density plots as employed in the existing study, while also suggesting an alternative testing method for thick tails in the form of an initial stochastic dominance test. The test reveals…

内生相关随机系数模型中的汽油需求价格弹性估计AI 解读

Summary We propose a per‐cluster instrumental variable (PCIV) approach for estimating linear correlated random coefficient models in the presence of contemporaneous endogeneity and two‐way fixed effects. This approach estimates heterogeneous effects and aggregates them to population averages. We demonstrate consistency, showing robustness over standard estimators, and provide analytic standard errors for robust inference. In Monte Carlo simulation, PCIV performs relatively…

空间计量模型的最优线性与二次矩：美国县域就业增长应用AI 解读

Summary We provide a novel analytic procedure to construct best linear and quadratic moments of the generalized method of moments estimation for a large class of cross‐sectional network and spatial econometric models. These moments generate an estimator that is asymptotically more efficient than the quasi‐maximum likelihood estimator when the disturbances follow a non‐normal and unknown distribution. We apply this procedure to a high‐order spatial autoregressive model with…

基于独立性准则的面板结构 VAR 统计识别AI 解读

Summary This paper introduces a novel panel approach to structural vector autoregressive analysis. For identification, we impose independence of structural innovations at the pooled level. We demonstrate robustness of the method under cross‐sectional correlation and heterogeneity through simulation experiments. In an empirical application on monetary policy transmission in the Euro area, we find that bond spreads rise significantly after an unexpected monetary tightening.…

含狄利克雷过程混合的随机波动模型：贝叶斯折叠Gibbs采样AI 解读

Summary This paper replicates the results of the stochastic volatility–Dirichlet process mixture (SV‐DPM) models proposed in Jensen and Maheu (2010) in both a narrow and a wide sense. By using a normal‐Wishart prior and the collapsed Gibbs sampling method, our algorithm can be applied for more general settings, and it is more efficient for sampling the Dirichlet process mixture. For the stochastic volatility component, we adopt the method in Chan (2017) to further increase…

美国财政政策冲击：通过 GMM 实现代理变量 SVAR 过度识别AI 解读

Summary Using external instruments, one can recover the effects of individual shocks without fully identifying a vector autoregression (VAR). We show that fully or almost fully instrumenting a VAR—that is, using an instrument for each shock—allows one to overidentify the model by incorporating the condition that the structural shocks are uncorrelated, via the generalized method of moments (GMM). We apply our approach to a fiscal VAR for the United States over 1948–2019,…

横截面乘数估计值得信赖吗？AI 解读

Summary I examine the properties of cross‐sectional estimators of multipliers, elasticities, or pass‐throughs when a conventional spatial macroeconomic specification generates the data. A number of important biases plague standard estimates; the most relevant one occurs when the units display heterogeneous dynamics. Methods that work well in this situation are suggested. An experimental setting shows the magnitude of the biases cross‐sectional estimators display. Average…

灵活的面板随机生产前沿模型AI 解读

Summary We propose a flexible stochastic production frontier model with fixed effects for the panel data in which the semiparametric frontier is additive with bivariate interactions. To avoid potential misspecification and/or “wrong skew problem” due to distributional assumptions, we model the conditional mean of the inefficiency to depend on environmental variables and to be known up to a vector of parameters. We propose a difference‐based estimator for parameters…

美国投资 Euler 方程的实证证据AI 解读

Summary Is the typical specification of the Euler equation for investment employed in dynamic stochastic general equilibrium (DSGE) models consistent with aggregate macro data? The answer is yes using state‐of‐the‐art econometric methods that are robust to weak instruments and exploit information in possible structural changes. Unfortunately, however, there is very little information about the values of the parameters in aggregate data because investment is unresponsive to…

高维多项 Logit 模型AI 解读

Summary The number of parameters in a standard multinomial logit model increases linearly with the number of choice alternatives and number of explanatory variables. Because many modern applications involve large choice sets with categorical explanatory variables, which enter the model as large sets of binary dummies, the number of parameters in a multinomial logit model is often large. This paper proposes a new method for data‐driven two‐way parameter clustering over…

提前裁员通知与总体就业损失AI 解读

Summary We collect data from Worker Adjustment and Retraining Notification (WARN) Act notices and establish their usefulness as an indicator of aggregate job loss. The number of workers affected by WARN notices (“WARN layoffs”) leads state‐level initial unemployment insurance claims and unemployment rate (UR) and private employment changes. WARN layoffs comove with aggregate layoffs from Mass Layoff Statistics and the Job Openings and Labor Turnover Survey but are timelier…

允许偏斜厚尾误差的统计识别结构 VARAI 解读

Abstract We introduce a structural vector autoregressive model in which the mutually independent errors follow skewed generalized t ‐distributions, whose flexibility compared with commonly considered Student's t ‐distributions diminishes the risk of misspecification and strengthens identification. Because of statistical identification due to non‐Gaussianity, the plausibility of economic identifying restrictions can be formally assessed. In an empirical application, the data…

将宏观经济建模为随机森林AI 解读

Summary I develop the macroeconomic random forest (MRF), an algorithm adapting the canonical machine learning (ML) tool, to flexibly model evolving parameters in a linear macro equation. Its main output, generalized time‐varying parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, and structural breaks/change) and allowing for sophisticated new ones. The approach delivers clear forecasting gains over numerous…

纠正机器学习方法中的样本选择偏误AI 解读

Summary We study approaches for adjusting machine learning methods when the training sample differs from the prediction sample on unobserved dimensions. The machine learning literature predominately assumes selection only on observed dimensions. Common approaches are to weight or include variables that influence selection as solutions to selection on observables. Simulation results show that selection on unobservables increases mean squared prediction error using popular…

强制安全带法规与交通死亡：重新评估AI 解读

Abstract Using data from the Fatality Analysis Reporting System for the period 1983–1997, Cohen and Einav ( Review of Economics and Statistics 2003; 85[4]: 828–843) found that mandatory seatbelt laws were associated with a 4–6% reduction in traffic fatalities among motor vehicle occupants. After successfully replicating their two‐way fixed effects estimates, we (1) add 22 years of data (1998–2019) to capture additional seatbelt policy variation and observe a longer…

随机前沿模型中的内生二元处理：萨尔瓦多土壤保护应用AI 解读

Summary Numerous programs exist to promote productivity, alleviate poverty, and enhance food security in developing countries. Stochastic frontier analysis can be helpful to assess their effectiveness. However, challenges can arise when accounting for treatment endogeneity, often intrinsic to these interventions. We study maximum likelihood estimation of stochastic frontier models when both the frontier and inefficiency depend on a potentially endogenous binary treatment. We…

利用文本数据预测欧洲 GDPAI 解读

Summary We evaluate the informational content of news‐based sentiment indicators for forecasting gross domestic product (GDP) and other macroeconomic variables of the five major European economies. Our dataset includes over 27 million articles for 26 major newspapers in five different languages. The evidence indicates that these sentiment indicators are significant predictors to forecast macroeconomic variables and their predictive content is robust to controlling for other…

内生删失持续期模型的部分识别与推断AI 解读

Abstract This paper studies identification and inference in transformation models with endogenous censoring. Many kinds of duration models, such as the accelerated failure time model, proportional hazard model, and mixed proportional hazard model, can be viewed as transformation models. We allow the censoring of a duration outcome to be arbitrarily correlated with observed covariates and unobserved heterogeneity. We impose no parametric restrictions on either the…

带薪育儿假影响儿童学业表现吗？复现Danzer与Lavy（2018）AI 解读

Summary Danzer and Lavy (2018) study how the duration of paid parental leave affects children's educational performance using data from PISA. An extension of the maximum duration from 12 to 24 months in Austria had no statistically significant effect on average, but the authors highlight the existence of large and statistically significant heterogenous effects that vary in sign depending on the education of mothers and children's gender. The policy increased the scores…

面板数据即时预测：市盈率案例AI 解读

Abstract The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross‐section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse‐group LASSO regularization which can take advantage of the mixed‐frequency time series panel data structures. Our empirical…

通过大数据分位数回归研究宏观尾部风险的非线性AI 解读

Summary Modeling and predicting extreme movements in GDP is notoriously difficult, and the selection of appropriate covariates and/or possible forms of nonlinearities are key in obtaining precise forecasts. In this paper, our focus is on using large datasets in quantile regression models to forecast the conditional distribution of US GDP growth. To capture possible nonlinearities, we include several nonlinear specifications. The resulting models will be huge dimensional, and…

存在样本流失时的配对与分层实验分析再审视AI 解读

Abstract In this paper, we revisit some common recommendations regarding the analysis of matched‐pair and stratified experimental designs in the presence of attrition. Our main objective is to clarify a number of well‐known claims about the practice of dropping pairs with an attrited unit when analyzing matched‐pair designs. Contradictory advice appears in the literature about whether or not dropping pairs is beneficial or harmful, and stratifying into larger groups has been…