论文检索

内生转换模型中的异质性与异方差：医生建议对热量摄入的影响AI 解读

ABSTRACT We describe two‐step control function estimation procedures for estimating average treatment effects in constant coefficient endogenous switching models with a binary treatment. We allow for additional heterogeneity by allowing for heteroskedasticity in the latent error in the treatment assignment equation. We apply our estimation procedures to evaluate the causal effect of physician advice on calorie consumption using National Health and Nutritional Examination…

工作场所同伴对未来工资的影响AI 解读

ABSTRACT This paper examines workplace peer effects in two directions, leveraging employer‐employee data for Italy. First, using a novel estimation approach and addressing endogenous worker‐peer sorting, we estimate that a 10% increase in peer quality raises one's wage by 1.8% in the next year. The effect declines to 0.7% after 5 years. Second, in an event study around mobility episodes, we quantify wage changes associated with the entry and leave of high‐quality and…

趋势工资通胀的度量AI 解读

ABSTRACT We extend time‐series models that have so far been used to study price inflation and apply them to a microlevel data set containing worker‐level information on hourly wages. We construct a measure of aggregate nominal wage growth that (i) filters out noise and very transitory movements, (ii) quantifies the importance of idiosyncratic factors for aggregate wage dynamics, and (iii) strongly co‐moves with labor market tightness, unlike existing indicators of wage…

利用动态因子模型为 DSGE 模型提供信息AI 解读

ABSTRACT Structural dynamic factor models (SDFM) represent a reliable tool to inform the construction of dynamic stochastic general equilibrium (DSGE) models. The reason is that the log‐linear solution of a DSGE model has a factor structure which ensures consistency between the representations of the two models. We assess the usefulness of SDFM for DSGE analysis by means of simulations. Using a standard DSGE model as the data generating process, we show that the factor model…

美国最低工资与青少年生育AI 解读

ABSTRACT The minimum wage is increasingly viewed as an important, but often neglected, tool for improving public health outcomes. Using data from the period 2003–2019 and a stacked difference‐in‐differences regression model that accounts for dynamic and heterogeneous treatment effects, we explore the relationship between minimum wages and teenage childbearing in the United States. We find no evidence of a systematic, negative relationship between minimum wages and…

具有得分驱动权重的动态混合向量自回归AI 解读

ABSTRACT We propose a novel dynamic mixture vector autoregressive (VAR) model where the time‐varying mixture weights are driven by the predictive likelihood score. Intuitively, the weight of a component VAR model is increased in the subsequent period if the current observation is more likely to be drawn from this state. The model is not limited to a specific distributional assumption and allows for straightforward likelihood‐based estimation and inference. In a Monte Carlo…

波动率周期建模：MF2-GARCH 模型AI 解读

ABSTRACT We propose a novel multiplicative factor multi‐frequency GARCH (MF2‐GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one‐component GARCH models are predictable by a moving average of past standardized forecast errors. In contrast to other multiplicative component GARCH models, the MF2‐GARCH features stationary returns, and long‐term volatility forecasts are mean‐reverting. When applied to the S&P 500, the new component…

处理交互作用与内生参与AI 解读

ABSTRACT We study causal inference in randomized experiments (or quasi‐experiments) following a factorial design. There are two treatments, denoted and , and units are randomly assigned to one of four categories: treatment alone, treatment alone, joint treatment, or none. Allowing for endogenous non‐compliance with the two binary instruments representing the intended assignment, as well as unrestricted interference across the two treatments, we derive the causal…

不确定性冲击的宏观响应：递归排序的风险AI 解读

ABSTRACT A common practice in empirical macroeconomics is to examine alternative recursive orderings of the variables in structural vector autoregressive (VAR) models. When the implied impulse responses look similar, the estimates are considered trustworthy. When they do not, the estimates are used to bound the true response without directly addressing the identification challenge. A leading example of this practice is the literature on the effects of uncertainty shocks on…

处理状态误分类下的双重差分AI 解读

ABSTRACT This paper studies identification and estimation of the average treatment effect of a latent treated subpopulation in difference‐in‐difference designs when the observed treatment is differentially (or endogenously) mismeasured for the truth. Common examples include misreporting and mistargeting. We propose a two‐step estimator that corrects for the empirically common phenomenon of one‐sided misclassification in the treatment status. The solution uses a single…

持续性冲击的动态效应AI 解读

ABSTRACT We provide evidence that many narrative shocks used by prominent literature display some persistence. We show that the two leading methods to estimate impulse responses to an independently identified shock (local projections and distributed lag models) treat persistence differently, hence identifying different objects. We propose corrections to re‐establish the equivalence between local projections and distributed lag models, providing applied researchers with…

信念冲击与增长风险预期的影响AI 解读

ABSTRACT This paper revisits the question of how shocks to expectations of market participants can cause business cycle fluctuations. We use a vector autoregression to estimate dynamic causal effects of belief shocks which are extracted from nowcast errors about output growth. In a first step, we replicate and corroborate the findings of Enders, Kleemann, and Müller (2021). The second step computes nowcast errors about growth‐at‐risk at various quantiles. This involves both…

离散程度、偏度与峰度回归：美国工资结构应用AI 解读

ABSTRACT Quantile regression provides a powerful tool for investigating the effects of covariates on key quantiles of a conditional distribution. However, we often lack a general picture of how covariates affect the overall shape of the conditional distribution. Using quantile regression estimation and quantile‐based measures of spread, skewness, and kurtosis, we propose spread regression, skewness regression, and kurtosis regression as empirical tools to quantify the…

汇率、无抛补利率平价与时变 Fama 回归AI 解读

ABSTRACT This paper studies the forward premium puzzle, which signals a violation of the uncovered interest parity (UIP) hypothesis. We test this hypothesis with Fama‐style regressions with time‐varying parameters (TVPs) and stochastic volatility (SV) on six major currencies relative to the US dollar on monthly samples from 1993 to 2018. TVP‐SV regressions are also employed to examine the opposing predictions of the forward premium and excess volatility puzzles often found…

双重差分回归的标准误AI 解读

ABSTRACT This paper makes a case for the use of jackknife methods for standard error, value, and confidence interval construction for difference‐in‐difference (DiD) regression. We review cluster‐robust, bootstrap, and jackknife standard error methods and show that standard methods can substantially underperform in conventional settings. In contrast, our proposed jackknife inference methods work well in broad contexts. We illustrate the relevance by replicating several…

利用替代性高频数据追踪经济活动AI 解读

ABSTRACT Monthly macroeconomic series captured the sharp fluctuations during the COVID‐19 pandemic only with a lag. The use of alternative high‐frequency data is promising for crisis periods, but it is difficult to extract relevant business cycle information from them. We present a Bayesian mixed‐frequency dynamic factor model with stochastic volatility for measuring GDP growth at high‐frequency intervals. Its novelty is an additional state‐space block, in which the sparse…

模型平均与双重机器学习AI 解读

ABSTRACT This paper discusses pairing double/debiased machine learning (DDML) with stacking , a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: Short‐stacking exploits the cross‐fitting step of DDML to substantially reduce the computational burden, and pooled stacking enforces common stacking weights over cross‐fitting folds. Using…

具有大量弱工具变量的二元响应模型AI 解读

ABSTRACT This paper considers an endogenous binary response model with many weak instruments. We employ a control function approach and a regularization scheme to obtain better estimation results for the endogenous binary response model in the presence of many weak instruments. Two consistent and asymptotically normally distributed estimators are provided, each of which is called a regularized conditional maximum likelihood estimator (RCMLE) and a regularized nonlinear least…

美国货币政策与均衡不确定性AI 解读

ABSTRACT In this paper, I investigate the stance of the US monetary policy in the postwar period. To this end, I show that two features are key: a rich structural model and a parameterization of the indeterminate solution that ensures a sufficient exploration of the indeterminacy region during the estimation procedure. Therefore, I estimate a medium‐scale model on the US macroeconomic data using a novel solution method to allow for indeterminacy. The evidence of a passive…

识别经济增长放缓的来源：需求与供给AI 解读

ABSTRACT Long‐run GDP growth has declined in the United States over the past two decades. Two competing views take centre stage in accounting for this slowdown: the demand‐side view and the supply‐side view. I empirically quantify their relative importance in a Bayesian SVAR with common trends identified using sign restrictions. While supply‐side factors were the main driver of long‐run GDP growth prior to 2000, demand‐side factors explain half of its slowdown afterwards.…

重新评估稀疏网格积分的似然近似AI 解读

ABSTRACT This paper revisits sparse grid integration proposed in the literature for approximating integrals that occur as choice probabilities in random coefficient discrete choice models. First, we successfully replicate their main findings for the panel mixed logit. Second, for higher variances and for a different structure of the variances of the random coefficients, in certain cases, we fail to replicate the original results. Third, for the important special case of…

重新评估收益率利差对美国衰退的预测能力AI 解读

ABSTRACT Rudebusch and Williams (2009) predict recessions in the United States utilising a probit model with the lagged yield spread as a real‐time predictor. Mindful of the importance of recent yield curve movements, we update their analysis and evaluate quarterly forecasts from their probit model up to the end of 2023. We also analyze lagged financial conditions as an alternative real‐time predictor. We find that both the yield spread and financial conditions perform…

菲利普斯曲线的断点：来自面板数据的证据AI 解读

ABSTRACT We revisit the Phillips curve, applying new Bayesian panel methods with structural breaks to US and EU disaggregate data. Our approach lets us estimate both the number and timing of breaks and to determine the existence of clusters of industries, cities, or countries whose Phillips curves display similar patterns. We find evidence of a flattening for US sectoral data and among EU countries, particularly poorer ones. Evidence of flattening is weaker for MSA‐level…

以大型因子增广 VAR 分析企业上下游与共同冲击AI 解读

ABSTRACT We evaluate the roles of upstream (supplier‐to‐user), downstream (user‐to‐supplier), and common factor shock transmission across firms by deriving interfirm networks and common factors from US equities over 1989–2017. We overcome the econometric challenges of estimating the large factor‐augmented vector autoregressive (FAVAR) system by developing two alternative approaches: one prioritizing computational efficiency and the other providing the full posterior…