论文检索

利用冲击方差变化识别因子增强向量自回归模型AI 解读

Summary This study proposes a method to identify factor‐augmented vector autoregression models without imposing uncorrelatedness or any timing restrictions among observed and unobserved factors in the vector autoregression system. To this end, we utilize changes in unconditional shock variances following Rigobon (2003). The proposed method can incorporate both observed and unobserved factors in the structural vector autoregression system and allows the contemporaneous matrix…

早年饥荒暴露、饥饿回忆与晚年健康AI 解读

Abstract We use newly collected individual‐level hunger recall information from the China Family Panel Survey to estimate the causal effect of undernourishment on later‐life health. We develop a two‐sample instrumental variable (TSIV) estimator that can deal with heterogeneous samples. We find a nonlinear relationship between mortality rates, a commonly used famine indicator, and the individual hunger experience. The nonlinearity in famine exposure may explain the variation…

通胀波动的全球成分AI 解读

Summary Global developments play an important role for domestic inflation rates. Earlier literature has found that a substantial amount of the variation in a large set of national inflation rates can be explained by a single global factor. However, inflation volatility has been typically neglected, although it is clearly relevant both from a policy point of view and for structural analysis and forecasting. We study the evolution of inflation rates in several countries, using…

2020年3月之后如何估计向量自回归模型AI 解读

Summary This paper illustrates how to handle a sequence of extreme observations—such as those recorded during the COVID‐19 pandemic—when estimating a vector autoregression, which is the most popular time‐series model in macroeconomics. Our results show that the ad hoc strategy of dropping these observations may be acceptable for the purpose of parameter estimation. However, disregarding these recent data is inappropriate for forecasting the future evolution of the economy,…

重新评估瑞典综合学校改革：教育与收入效应AI 解读

Summary We revisit a Swedish comprehensive school reform first evaluated by Meghir and Palme (2005). This reform increased years of schooling and abolished tracking. We extend the original analysis to the full population and introduce an improved education measure. Our results confirm the original overall finding of small average earnings effects. However, we find considerably larger increases in educational attainment and no evidence of decreased labor earnings for students…

随机前沿模型中尾部行为的非参数检验AI 解读

Summary This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests for thin‐tailed distributional assumptions imposed on these two components. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A…

贝叶斯模型比较中的自动化先验稳健性分析AI 解读

Summary It is well‐known that the marginal likelihood, the gold standard for Bayesian model comparison, can be sensitive to prior hyperparameter choices. However, most models require computationally intense simulation‐based methods to evaluate the typically high‐dimensional integral of the marginal likelihood expression. Hence, despite the recognition that prior sensitivity analysis is important in this context, it is rarely done in practice. We develop efficient and…

模型复杂度能否提升资产配置表现？机器学习预测模型的证据AI 解读

Summary This study evaluates the benefits of integrating return forecasts from a variety of machine learning and forecast combination methods into an out‐of‐sample asset allocation framework. The economic evaluation of the forecasts shows that model complexity translates to improved results in the majority of cases considered, with shrinkage methods and shallow neural networks generating the highest individual performance. Overall, an investor would consistently realize…

个体预测者对GDP冲击持续性的认知AI 解读

Summary We analyse individual professional forecasters' beliefs concerning the persistence of GDP shocks. Despite substantial apparent heterogeneity in perceptions, with around one half of the sample of professional forecasters believing shocks do not have permanent effects, we show that these apparent differences may be largely due to short samples and survey respondents being active at different times. When we control for these effects, using a bootstrap, we formally do…

初级医疗与急诊二级医疗需求的动态相互依赖：隐马尔可夫方法AI 解读

Summary This paper develops an extension of the class of finite mixture models for longitudinal count data to the bivariate case by using a hidden Markov chain approach. The model allows for disentangling unobservable time‐varying heterogeneity from the dynamic effect of utilisation of primary and secondary care and measuring their potential substitution effect. Three points of supports adequately describe the distribution of the latent states suggesting the existence of…

商品价格与通胀风险AI 解读

Summary This paper investigates the role of commodity price information when evaluating inflation risk. Using a model averaging approach, we provide strong evidence of in‐sample and out‐of‐sample predictive ability from commodity prices and convenience yields to inflation, establishing clear point and density forecast performance gains when incorporating disaggregated commodities price information. The resulting forecast densities are used to calculate the (ex‐ante) risk of…

职业培训的动态处理效应AI 解读

Summary This paper estimates the dynamic returns to job training. We posit a model of sequential training participation, where decisions and outcomes depend on observed and unobserved characteristics. We analyze different treatment effects, including policy relevant parameters, and link them to continuation values and latent skills. The empirical analysis exploits administrative data combining job training records, matched employee‐employer information, and pre‐labor market…

误分类下流行率与趋势估计的贝叶斯方法AI 解读

Abstract In this paper, we present a Bayesian approach to estimate the mean of a binary variable and changes in the mean over time, when the variable is subject to misclassification error. These parameters are partially identified, and we derive identified sets under various assumptions about the misclassification rates. We apply our method to estimating the prevalence and trend of prescription opioid misuse, using data from the 2002–2014 National Survey on Drug Use and…

求职援助项目的动态评估AI 解读

Summary This paper evaluates a job search assistance program for unemployed teachers where the assignment to the program is dynamic. We discuss the methodology of estimating dynamic treatment effects and identification conditions. In the empirical analysis, we use administrative data from a unique institutional environment in which we observe all variables determining assignment to the job search assistance program. This allows us to compare results from a dynamic…

利用短期期权测量与预测波动率的信息增益AI 解读

Summary We study the gains from using short‐dated options for volatility measurement and forecasting. Using option portfolios, we estimate nonparametrically spot volatility under weak assumptions for the underlying asset. This volatility estimator complements existing ones constructed from high‐frequency returns. We show empirically, using the market index and Dow 30 stocks, that combining optimally return and option data can lead to nontrivial gains for volatility…

基于重抽样统计量的依赖稳健推断AI 解读

Summary We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well‐known forms of weak dependence and…

状态空间模型的自助法推断与诊断：动态宏观模型应用AI 解读

Abstract This paper investigates the potentials of the bootstrap as a tool for inference on the parameters of macroeconometric models which admit a state space representation. We consider a bootstrap estimator of the parameters of state space models and show that the bootstrap realizations of this estimator, usually employed to approximate asymptotic confidence intervals, p ‐values, and critical values of tests, can be also constructively used to build a test for forms of…

新冠疫情期间严厉封锁的经济影响：中国湖北的经验AI 解读

Summary This paper uses a panel data approach to assess the evolution of economic consequences of the drastic lockdown policy in the epicenter of COVID‐19—the Hubei Province of China during worldwide curbs on economic activity. We find that the drastic 76‐day COVID‐19 lockdown policy brought huge negative impacts on Hubei's economy. In 2020:q1, the lockdown quarter, the treatment effect on GDP was about 37% of the counterfactual. However, the drastic lockdown also brought…

国家分裂的经济学：评估分离独立的经济影响AI 解读

Abstract This paper presents estimates of the economic effects of secession for a large panel of countries that gained independence between 1940 and 2016. It relies on a semi‐parametric identification strategy that controls for the confounding effects of past gross domestic product (GDP) dynamics, anticipation effects, unobserved heterogeneity, model uncertainty, and effect heterogeneity. Our findings indicate that secession lowered per capita GDP, on average by around 24%…

不确定的王国：国内生产总值及其修订的即时预测AI 解读

Summary We propose a release‐augmented dynamic factor model (RA‐DFM) that allows to quantify the role of a country's data flow in nowcasting both early Gross Domestic Product (GDP) releases and subsequent revisions of official estimates. We use the RA‐DFM to study UK GDP early revision rounds and assemble a comprehensive and novel mixed‐frequency dataset that features 12 years of real‐time data vintages. The RA‐DFM improves over the standard DFM in real time when forecasting…

新闻媒体与FRED-MD的宏观经济预测比较AI 解读

Summary Using a unique dataset of 22.5 million news articles from the Dow Jones Newswires Archive , we perform an in depth real‐time out‐of‐sample forecasting comparison study with one of the most widely used data sets in the newer forecasting literature, namely the FRED‐MD dataset. Focusing on US GDP, consumption and investment growth, our results suggest that the news data contains information not captured by the hard economic indicators, and that the news‐based data are…

预测不确定性、预测分歧与线性池AI 解读

Summary The linear pool is the most popular method for combining density forecasts. We analyze its implications concerning forecast uncertainty, using a new framework that focuses on the means and variances of the individual and combined forecasts. Our results show that, if the variance predictions of the individual forecasts are unbiased, the well‐known “disagreement” component of the linear pool exacerbates the upward bias of its variance prediction. This finding suggests…

单指数 copula 模型的半参数估计与变量选择AI 解读

Summary A copula with a flexibly dependence structure can capture complexity and heterogeneity in economic and financial time series. Based on the recently proposed single‐index copula, we propose a simultaneous variable selection and estimation procedure. This method allows for choosing the most relevant state variables by using a penalized estimation with large sample properties derived. Simulation results demonstrate the good performance of the method in selecting…

时间使用调查能与不能揭示的劳动供给信息AI 解读

Summary The American Time Use Survey (ATUS) accurately measures hours worked on a single day. We propose several estimators of elasticities of weekly labor supply in a linear regression model, despite certain impossibility results due to the time specific feature of the ATUS. We recommend the impute estimator , a simple modification of the standard two stage least squares estimator, that imputes the dependent variable using daily subsamples, based on our careful…