论文检索

因子增强回归预测准确性的稳健检验：新欧元区 EA-MD-QD 数据集应用AI 解读

ABSTRACT We present four novel tests of equal predictive accuracy and encompassing á Pitarakis (2023, 2025) for factor‐augmented regressions. Factors are estimated using cross‐section averages (CAs) of grouped series and our theoretical findings are empirically relevant: asymptotic normality, robustness to an overspecification of the number of factors, tractability of different degrees of predictor persistence, and invariance to the location of structural breaks in the…

可靠性随时间变化的高频工具变量：理解宏观经济识别AI 解读

ABSTRACT The effects of monetary policy shocks are regularly estimated using high‐frequency surprises in asset prices around central bank meetings as an instrument. These studies, insofar as they explicitly model the relationship between instrument and structural shock, assume a constant relationship between the instrument and the monetary policy shock. By allowing for time variation in this relationship, we show that only a few distinct periods are informative about…

高频信息组合优化中重审 EWMA：比较评估AI 解读

ABSTRACT This paper compares the statistical and economic performance of state‐of‐the‐art high‐frequency (HF) based multivariate volatility models with a simpler, widely used alternative, the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002–2023), we assess model performance through a Global Minimum Variance portfolio optimization exercise, with and without short‐selling restrictions across multiple forecast…

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…

相关时间序列的预测AI 解读

ABSTRACT A collection of time series are “related” if they follow similar stochastic processes and/or they are statistically dependent. This paper proposes a related time series (RTS) forecasting model that exploits these relationships. The model's foundation is a set of univariate Gaussian autoregressions, one for each series, which are then augmented to incorporate stochastic volatility, heavy‐tailed innovations, additive outliers, time‐varying parameters and common…

没有工具变量时的边际处理效应AI 解读

ABSTRACT We propose a method for defining, identifying, and estimating the marginal treatment effect (MTE) without imposing the instrumental variable (IV) assumptions of independence, exclusion, and separability (or monotonicity). Under a new definition of the MTE based on reduced‐form treatment error that is statistically independent of the covariates, we find that the relationship between the MTE and standard treatment parameters holds in the absence of IVs. We provide a…

存在非均衡行为的不完全信息离散博弈的非参数识别AI 解读

ABSTRACT This paper studies empirical discrete‐choice games with incomplete information under nonparametric specifications of both the payoff function and the distribution of private information. It relaxes the standard global equilibrium assumption —under which players follow equilibrium strategies for all realizations of the control variables—and introduces a weaker local equilibrium assumption that requires the equilibrium restriction only for some, but not all,…

缓冲储蓄行为的统计推断AI 解读

ABSTRACT There is an ongoing debate about the buffer‐stock saving hypothesis. This paper develops a formal test of the hypothesis by examining the sub‐unity of the marginal propensity to consume out of permanent income (MPCP). Our test relies on a novel estimator of the average derivative of a nonparametric consumption model, using constructed measures of latent permanent income shocks that accommodate nonparametric income distributions. Following Li and Vuong (1998), we use…

分布向量自回归：刻画宏观与金融依赖AI 解读

ABSTRACT This paper extends the vector autoregression framework by introducing a flexible distributional regression that models multivariate time series without imposing restrictive parametric distribution assumptions. We develop a distributional impulse response function that captures the future effect of distributional disturbances within the system, providing a more detailed view of dynamic heterogeneity. We propose a straightforward estimation method and establish its…

利用文本数据的因子分位数预测AI 解读

ABSTRACT Words matter for predicting tail risks. We propose an attention mechanism embedded in a quantile factor model, yielding information that is quantile‐specific, target‐specific, and horizon‐specific. We establish new asymptotic results and show empirically that targeted textual data improve quantile forecasts of exchange‐rate returns and industrial production growth relative to strong benchmarks and other conditional‐quantile models. Bigrams and trigrams drive these…

利用面板数据估计交互效应AI 解读

ABSTRACT This paper analyzes how interaction effects can be consistently estimated under economically plausible assumptions in linear panel models with a fixed ‐dimension. We advocate for a correlated interaction term effects (CITE) estimator and show that it is consistent under conditions that are not sufficient for consistency of the interaction term effect estimator that is most common in applied econometric work. Our paper discusses the empirical content of these…

双重 LASSO：复现与实践启示AI 解读

ABSTRACT The rise of machine learning (ML) is one of the most prominent developments in applied econometrics in the past decade. The focus of much economic analysis is causal, rather than prediction, and Belloni et al. (2014) demonstrate how ML methods can be used in causal inference. This paper undertakes a narrow and wide replication of the Monte Carlo and empirical examples presented by Belloni et al. (2014). We discuss practical implications of this replication for the…

什么解释了国际利率的共同变动？AI 解读

ABSTRACT The international co‐movement of interest rates reflects correlated business‐cycle fluctuations, largely driven by demand shocks. Monetary policy in advanced economies follows domestic mandates—inflation and the output gap—and does not respond to foreign policy shocks. We derive this result from a Bayesian structural panel vector autoregression with informative priors, homogeneity restrictions on contemporaneous relations, a hierarchical Minnesota prior with…

符号约束 VAR 模型中的常规冲击响应先验AI 解读

ABSTRACT Some studies have expressed concern that the Gaussian‐inverse Wishart–Haar prior typically employed in estimating sign‐identified VAR models may be unintentionally informative about the prior for the structural responses. We discuss what features to look for in this prior in the absence of specific prior information about the responses, building on the notion of weakly informative priors in Gelman et al. (2013), and in the presence of such information. Empirical…

理性预期下具有异质同伴效应的计数数据模型AI 解读

ABSTRACT This paper develops a peer effect model for count responses under rational expectations. The model accounts for heterogeneity in peer effects across groups based on observed characteristics. Identification is based on the linear model condition that requires the presence of friends of friends who are not direct friends. I show that this identification condition extends to a broad class of nonlinear models. Parameters are estimated using a nested pseudo‐likelihood…

资源错配下的生产函数估计AI 解读

ABSTRACT We show that the proxy variable method fails to yield consistent estimates of production function coefficients in the presence of resource misallocation. This failure arises because unobserved firm‐specific distortions violate the assumptions of scalar unobservability and strict monotonicity. We propose a novel identification strategy that does not rely on these assumptions. Our method is robust to various distortions and production function specifications, and can…

半球神经网络：从被动响应到前瞻性波动建模AI 解读

ABSTRACT We revisit maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network, which accommodates various forms of time variation in the error variance. Second, we introduce a volatility emphasis constraint that…

不完美的合成控制AI 解读

ABSTRACT The synthetic control method assumes the existence of a perfect synthetic control, which cannot exist if the outcomes are functions of transitory shocks with nonzero asymptotic variance and may not exist even in expectation for the treated unit. This paper first shows the benefits of estimating synthetic controls for all units. If the treated unit composes part of the synthetic control for any untreated unit, the treatment effect is independently identified by the…

半参数模型的一致、异方差稳健 LM 型设定检验AI 解读

ABSTRACT This article develops a heteroskedasticity‐robust Lagrange Multiplier‐type specification test for semiparametric regression models. The test is able to detect a wide class of deviations from the null hypothesis. The test statistic is based on the estimates from the restricted semiparametric model, can be computed in a regression‐based way, and is asymptotically standard normal under the null. I apply the test to Environmental Engel Curves and find that while the…

部分参数模型中的联合估计与带宽选择AI 解读

ABSTRACT We propose a single‐step approach to estimating a model with both a known nonlinear parametric component and an unknown nonparametric component. We study the large sample behavior of a simultaneous optimization routine that estimates both the parameter vector of the parametric component and the bandwidth vector used to smooth the unknown function. In our application, we estimate a partially constant elasticity of substitution production function and uncover results…