每日简报

强化学习变量选择的序贯 Knockoffs 方法AI 解读

In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge. Consequently, it is common practice to construct a state larger than necessary, e.g., by concatenating measurements over contiguous time points. However, needlessly increasing the dimension of the state may slow learning and obfuscate the learned policy. We introduce the notion of a…

个体数据与汇总统计量的半参数有效融合AI 解读

Suppose we have individual data from an internal study and various summary statistics from relevant external studies. External summary statistics have the potential to improve statistical inference for the internal population; however, it may lead to efficiency loss or bias if not used properly. We study the fusion of individual data and summary statistics in a semiparametric framework to investigate the efficient use of external summary statistics. Under a weak…

生成式贝叶斯推断中的树型多臂老虎机方法AI 解读

In generative models with intractable likelihood, Approximate Bayesian Computation (ABC) is often the tool of last resort for inference. However, ABC demands many prior parameter proposals to keep only the small fraction of them that meet the acceptance criterion. To accelerate ABC rejection sampling, this paper develops a self-aware framework that learns from past proposals: both acceptances and rejections. We apply recursive partitioning classifiers on the ABC lookup table…

多元分类响应回归的条件概率张量分解AI 解读

In many modern regression applications, the response consists of multiple categorical random variables whose probability mass is a function of a common set of predictors. In this article, we propose a new method for modeling such a probability mass function in settings where the number of response variables, the number of categories per response, and the dimension of the predictor are large. Our method relies on a functional probability tensor decomposition: a decomposition…

具有共同随机趋势的高维门限因子模型AI 解读

We study inference for threshold regression in the context of a large factor model with common stochastic trends. We develop a Least Squares estimator for the threshold level, deriving almost sure rates of convergence and proposing a novel way of constructing confidence intervals based on a randomized test. Our confidence intervals are constructed using the rates of convergence of the estimated threshold level, and no limiting distribution is required. We also develop a…

动态因子相关性AI 解读

ABSTRACT We introduce a dynamic factor correlation model whose core methodological innovation is a variation‐free parametrization of dynamic factor loadings, inspired by the generalized Fisher transformation. The model accommodates time‐varying correlations, heterogeneous heavy tails, and dependent idiosyncratic shocks. Applied to a Small Universe of 12 assets and a Large Universe of 323 stocks, the factor structure induces a sparse idiosyncratic correlation matrix with…

利用混频数据改进密度预测：贝叶斯方法AI 解读

We construct a modeling framework that allows for the flexible contribution of intraday information to predict the conditional distribution of asset returns. A flexible spline specification is proposed to capture the contribution of intraday returns inside GARCH and stochastic volatility specifications for daily returns. The “focused Bayesian” prediction framework is adopted to allow the intraday contribution to the simpler GARCH specification to be trained according to the…

跨异质地点的因果效应迁移估计AI 解读

We consider the problem of extrapolating treatment effects across heterogeneous populations (“sites”/“contexts”). We consider an idealized scenario in which the researcher observes cross-sectional data for a large number of units across several “experimental” sites in which an intervention has already been implemented to a new “target” site for which a baseline survey of unit-specific, pre-treatment outcomes and relevant attributes is available. Our approach treats the…

内生进入与可能有偏信念下第一价格拍卖的识别AI 解读

This paper studies identification of first-price auctions where bidders enter auctions endogenously and may have biased beliefs about the valuation distribution. We analyze the symmetric independent private setup under three commonly used entry models. In the Levin-Smith entry model, the primitives are point-identified. In the Samuelson entry model, the primitives are identified only on a subset of the support of valuations. In the arbitrarily selective entry model, the…

短面板中的潜在因子分析AI 解读

We develop a pseudo maximum likelihood method for latent factor analysis in short panels without imposing sphericity nor Gaussianity. We derive an asymptotically uniformly most powerful invariant test for the number of factors. On a large panel of monthly U.S. stock returns, we separate month after month systematic and idiosyncratic risks in short subperiods of bear vs. bull market. We observe an uptrend in the paths of total and idiosyncratic volatilities. The systematic…

大规模网络的降秩多元空间自回归模型AI 解读

In spatial and social network analysis, multivariate spatial autoregressive (MSAR) models are effective tools for analyzing network data with multivariate responses. When the dimension of the response is divergent, however, the number of unknown parameters in an MSAR increases at a rate that is proportional to the square of the dimensionality, which poses significant challenges to the model estimation process. To address this issue, we propose a novel reduced-rank MSAR model…

混合矩阵值自回归模型AI 解读

Time series of matrix-valued data are increasingly available in various areas including economics, finance, social science, among others. These data may shed light on the inter-dynamical relationships between two sets of attributes, for instance, countries and economic indices. The matrix autoregressive (MAR) model provides a parsimonious approach for analyzing such data. However, the MAR model, being a linear model with parametric constraints, cannot capture the nonlinear…

多交易场所资产的积分方差估计AI 解读

In this paper, we show that the availability of multiple price series for the same asset can be exploited to estimate its integrated variance. We use a vector error correction model for those prices and its common trend representation to estimate the efficient price of the asset. Because the estimated efficient price satisfies the martingale property, we can then compute the sum of its squared intra-day returns to obtain an unbiased and efficient realized variance estimator.…

面板回归模型的核范数正则化估计AI 解读

In this paper we investigate panel regression models with interactive fixed effects. We propose two new estimation methods that are based on minimizing convex objective functions. The first method minimizes the sum of squared residuals with a nuclear (trace) norm regularization. The second method minimizes the nuclear norm of the residuals. We establish the consistency of the two resulting estimators. Those estimators have a very important computational advantage compared to…

利用空间建模处理协变量测量误差AI 解读

We propose a new estimation methodology to address the presence of covariate measurement error by exploiting the availability of spatial data.The approach uses neighboring observations as repeated measurements, after suitably controlling for the random distance between the observations in a way that allows the use of operator diagonalization methods to establish identification.The method is applicable to general nonlinear models with potentially nonclassical errors and does…

得分驱动模型时变参数的一致性、分布收敛与最优性AI 解读

Observation-driven models for time series have a long history in statistics and econometrics, but are typically studied under the assumption of correct dynamic specification. We develop an in-fill asymptotic framework to study the limiting behavior of the estimated (i.e., filtered) time-varying parameter paths obtained with such models in (severely) mis-specified settings. We show that despite such mis-specification, the filtered paths, particularly those from the class of…

高斯混合模型的信息矩阵检验AI 解读

We obtain the Information Matrix test for finite Gaussian mixtures exploiting the EM principle, which implies the moments tested are the expectation given the data of the moments one would assess if one knew the latent component each observation belongs to. Simulations indicate analytical expressions for the asymptotic covariance matrix of those moments adjusted for sampling variability in parameter estimators provide reliable finite sample sizes and good power against…

高维预测回归中的 LASSO 推断AI 解读

LASSO inflicts shrinkage bias on estimated coefficients, which undermines asymptotic normality and invalidates standard inferential procedures based on the t -statistic. Given cross sectional data, the desparsified LASSO has emerged as a well-known remedy for correcting the shrinkage bias. In the context of high dimensional predictive regression, the desparsified LASSO faces an additional challenge: the Stambaugh bias arising from nonstationary regressors modeled as local…

分数阶单整模型的修正条件平方和估计量AI 解读

In this paper, we analyse the influence of estimating a constant term on the bias of the conditional sum-of-squares (CSS) estimator in a stationary or non-stationary type-II ARFIMA (p1, d, p2) model. We derive expressions for the estimator’s bias and show that the leading term can be easily removed by a simple modification of the CSS objective function. We call this new estimator the modified conditional sum-of-squares (MCSS) estimator. We show theoretically and by means of…

是否应利用辅助网络信息改进大型协方差矩阵估计？AI 解读

This paper uses the auxiliary network information, observed in addition to the original sample, to infer latent network structures in the population correlation matrix and thus improve high-dimensional covariance matrix estimation. Building on estimated Location Indicator and Relative Importance matrices, we propose two Network-Guided estimators. Network-Guided Thresholding uses auxiliary network data to regularize the large and small elements in the sample covariance…

集合识别 SVAR 预测误差方差分解的估计与推断AI 解读

We study estimation and inference of the Forecast Error Variance Decomposition (FEVD) in Structural Vector Autoregressions (SVARs) that are set-identified by internal and external (proxy) restrictions. The FEVD measures the importance of shocks for macroeconomic fluctuations and is thus key to business cycle analysis. We deliver three practitioner-oriented results. First, we characterize the endpoints of the FEVD as the extreme eigenvalues of a symmetric reduced-form matrix,…

联合尾部风险监测：增长与通胀的应用AI 解读

This paper develops the concept of Growth and Inflation at Risk frontier (GIaR). This is a bivariate generalisation of the concepts of Growth-at-Risk (GaR) and Inflation-at-Risk (IaR). We propose a novel approach to identify and estimate GIaR and provide uniformly valid upper and lower confidence bands. We first apply our procedure to predict the conditional probability of stagflation. Second, we compute worst-case scenarios for a policy maker who is concerned about the…

增长风险的稳健计量方法AI 解读

The Growth-at-Risk (GaR) framework has garnered attention in recent econometric literature, yet current approaches implicitly assume a constant Pareto exponent. We introduce novel and robust econometrics to estimate the tails of GaR based on a rigorous theoretical framework and establish validity and effectiveness. Simulations demonstrate consistent outperformance relative to existing alternatives in terms of predictive accuracy. We perform a long-term GaR analysis that…

工具变量外生性的一致有效检验AI 解读

This paper studies the limiting behavior of the test for instrument exogeneity in linear models when there is uncertainty about the strength of the identification signal. We consider the test for conditional moment restrictions with an expanding set of constructed instruments. We establish the uniform validity of the standard normal asymptotic approximation, under the null, of this specification test over all possible degrees of model identification. As a result, this allows…