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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…

空间相关稳健推断AI 解读

We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a…

具有门限效应的因子增强预测回归AI 解读

Summary This paper introduces a factor-augmented forecasting regression model in the presence of threshold effects. We consider least squares estimation of the regression parameters and establish asymptotic theories for estimators of both slope coefficients and the threshold parameter. Prediction intervals are also constructed for factor-augmented forecasts. Moreover, we develop a likelihood ratio statistic for tests on the threshold parameter and a sup-Wald test statistic…

简约式因子扩展 VAR：利用稀疏性纳入具有经济含义的因子AI 解读

Summary Induced sparsity in the factor loading matrix identifies the factor basis, while rotational identification is obtained ex post by clustering methods closely related to machine learning. We extract meaningful economic concepts from a high‐dimensional data set, which together with observed variables follow an unrestricted, reduced‐form VAR process. Including a comprehensive set of economic concepts allows reliable, fundamental structural analysis, even of the factor…

矩阵补全、反事实与缺失数据的因子分析AI 解读

This paper proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the…

大量时期与大量对照单位下合成控制估计量的性质AI 解读

We consider the asymptotic properties of the synthetic control (SC) estimator when both the number of pretreatment periods and control units are large. If potential outcomes follow a linear factor model, we provide conditions under which the SC unit asymptotically recovers the factor structure of the treated unit, even when the pretreatment fit is imperfect. This happens when there are weights diluted among an increasing number of control units such that a weighted average…

增广合成控制法AI 解读

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The “synthetic control” is a weighted average of control units that balances the treated unit’s pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings…

主成分回归的稳健性AI 解读

Principal component regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structure. However, its ability to handle settings with noisy, missing, and mixed-valued, that is, discrete and continuous, covariates is not understood and remains an important open challenge. As the main contribution of this work, we establish the robustness of PCR, without any change, in this respect…

因果面板数据模型的矩阵补全方法AI 解读

In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations. We propose a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to impute the "missing" elements of the control outcome matrix,…

具有观测驱动动态因子载荷的闭式多因子 Copula 模型AI 解读

We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for straightforward parameter estimation and likelihood inference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001–2014. The proposed multi-factor structure is…

连接稳健主成分分析中的凸与非凸优化：噪声、异常值及缺失数据AI 解读

This paper delivers improved theoretical guarantees for the convex programming approach in low-rank matrix estimation, in the presence of (1) random noise, (2) gross sparse outliers and (3) missing data. This problem, often dubbed as robust principal component analysis (robust PCA), finds applications in various domains. Despite the wide applicability of convex relaxation, the available statistical support (particularly the stability analysis in the presence of random noise)…

大型面板数据模型的递归估计：理论与实践AI 解读

Bai (2009) proposes recursive estimation for panel data models with interactive effects. We study the behaviours of this recursive estimator. The recursive formula is established that shows the behaviours of recursive estimators depend on the initial estimator, the population structure and the iterative steps. Under some general scenarios, we find that the recursive estimator becomes consistent after the first iteration from any initials. We also obtain the optimal number of…

因子模型的极限定理AI 解读

This paper establishes central limit theorems (CLTs) and proposes how to perform valid inference in factor models. We consider a setting where many counties/regions/assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous microparameters estimated separately for each entity. The CLT applies for quantities involving both cross-sectional and time series aggregation, as well as for quadratic…

《高维普通最小二乘投影变量筛选》的引理修正说明AI 解读

The conclusion in Lemma 5 in the supplementary material (SM) of Wang and Leng (2016), on which Theorems 1–3 rely, is incorrect. Here we provide an additional condition under which the conclusion of this lemma holds. We assume that the rows of the matrix X follow a Gaussian distribution with precision matrix Ω = Σ−1 that satisfies c 4 - 1 n - τ / 2 ≤ λ min ( Ω ) ≤ λ max ( Ω ) ≤ c 4 n τ / 2 . This implies the assumption on the condition number of Σ in Assumption A3. We drop…

具有共同冲击的动态空间面板数据模型AI 解读

This paper studies dynamic spatial panel data models with common shocks to deal with both weak and strong cross-sectional correlations. Weak correlations are captured by a spatial structure and strong correlations are captured by a factor structure. The proposed quasi-maximum likelihood estimator (QMLE) is capable of handling both types of cross sectional dependence. We provide a rigorous analysis for the asymptotic theory of the QMLE, demonstrating its desirable properties.…

通过预测变量增广确定降维阶数AI 解读

Summary In many dimension reduction problems in statistics and machine learning, such as in principal component analysis, canonical correlation analysis, independent component analysis and sufficient dimension reduction, it is important to determine the dimension of the reduced predictor, which often amounts to estimating the rank of a matrix. This problem is called order determination. In this article, we propose a novel and highly effective order-determination method based…

国际个股收益中的因子与风险溢价AI 解读

We propose an estimation methodology tailored for large unbalanced panels of individual stock returns to study the factor structure and expected returns in international stock markets. We show that the local market is necessary to capture the factor structure in both developed and emerging markets. Neither the presence of multiple world or regional risk factors, systematic currency risk factors, nor a country-specific currency subsumes the importance of the local market…

具有因子结构的多任务分位数回归AI 解读

A multivariate quantile regression model with a factor structure is proposed to study data with multivariate responses with covariates. The factor structure is allowed to vary with the quantile levels, which is more flexible than the classical factor models. Assuming the number of factors is small, and the number of responses and the input variables are growing with the sample size, the model is estimated with the nuclear norm regularization. The incurred optimization…

因子强度的测量：理论与实践AI 解读

Abstract This paper proposes an estimator of factor strength and establishes its consistency and asymptotic distribution. The estimator is based on the number of statistically significant factor loadings, taking multiple testing into account. Both cases of observed and unobserved factors are considered. The small sample properties of the proposed estimator are investigated using Monte Carlo experiments. It is shown that the proposed estimation and inference procedures…

无套利先验、时变波动率与利率期限结构AI 解读

Abstract We use a Bayesian vector autoregression with stochastic volatility to forecast government bond yields. We form the conjugate prior from a no‐arbitrage affine term structure model. The model improves on the accuracy of point and density forecasts from a no‐change random walk and an affine term structure model with stochastic volatility. Our proposed approach may succeed by relaxing the no‐arbitrage affine term structure model's requirements that yields obey a factor…

基于边际化脆弱性的疾病—死亡模型：英国生物样本库应用AI 解读

The UK Biobank is a large-scale health resource comprising genetic, environmental, and medical information on approximately 500,000 volunteer participants in the United Kingdom, recruited at ages 40–69 during the years 2006–2010. The project monitors the health and well-being of its participants. This work demonstrates how these data can be used to yield the building blocks for an interpretable risk-prediction model, in a semiparametric fashion, based on known genetic and…

高维季节性天气预测的多元后处理方法AI 解读

–Seasonal weather forecasts are crucial for long-term planning in many practical situations and skillful forecasts may have substantial economic and humanitarian implications. Current seasonal forecasting models require statistical postprocessing of the output to correct systematic biases and unrealistic uncertainty assessments. We propose a multivariate postprocessing approach using covariance tapering, combined with a dimension reduction step based on principal component…

用于交互作用推断的贝叶斯因子分析AI 解读

This article is motivated by the problem of inference on interactions among chemical exposures impacting human health outcomes. Chemicals often co-occur in the environment or in synthetic mixtures and as a result exposure levels can be highly correlated. We propose a latent factor joint model, which includes shared factors in both the predictor and response components while assuming conditional independence. By including a quadratic regression in the latent variables in the…

空间索引函数的主成分分析AI 解读

We develop an expansion, similar in some respects to the Karhunen–Loève expansion, but which is more suitable for functional data indexed by spatial locations on a grid. Unlike the traditional Karhunen–Loève expansion, it takes into account the spatial dependence between the functions. By doing so, it provides a more efficient dimension reduction tool, both theoretically and in finite samples, for functional data with moderate spatial dependence. For such data, it also…