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异方差极值回归模型中的极端期望分位数预测AI 解读

Expectile recently receives much attention for its coherence as a tail risk measure. Estimation of conditional expectile at extremal tails is of great interest in quantitative risk management. Regression analysis is a convenient and useful way to quantify the conditional effect of some predictors or risk factors on an interesting response variable. However, when it comes to the estimation of extremal conditional expectile, the traditional inference methods may suffer from…

函数型线性回归：序列依赖与误差污染AI 解读

Functional linear regression is an important topic in functional data analysis. It is commonly assumed that samples of the functional predictor are independent realizations of an underlying stochastic process, and are observed over a grid of points contaminated by iid measurement errors. In practice, however, the dynamical dependence across different curves may exist and the parametric assumption on the error covariance structure could be unrealistic. In this article, we…

半参数因子模型中的同质性与结构识别AI 解读

Factor modeling is an essential tool for exploring intrinsic dependence structures in financial and economic studies through the construction of common latent variables, including the famous Fama–French three factor models for the description of asset returns in finance. However, most of the existing statistical methods for analyzing latent factors have been developed through a linear approach. In this article, we consider a semiparametric factor model and present a…

依赖过程条件独立检验的投影方法AI 解读

Conditional independence is a fundamental concept in many scientific fields. In this article, we propose a projective approach to measuring and testing departure from conditional independence for dependent processes. Through projecting high-dimensional dependent processes on to low-dimensional subspaces, our proposed projective approach is insensitive to the dimensions of the processes. We show that, under the common β-mixing conditions, our proposed projective test…

用自回归条件帕累托模型刻画动态尾指数AI 解读

We propose an autoregressive conditional Pareto (AcP) model based on the dynamic peaks over threshold method to model a dynamic tail index in the financial markets. Unlike the score-based approach which is widely used in many articles, we use an exponential function to model the tail index process for its intuitiveness and interpretability. Probabilistic properties of the AcP model and the statistical properties of its parameter estimators of maximum likelihood are studied…

基于变化中的变化方法的直接与间接效应AI 解读

We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We identify average and quantile direct and indirect effects for various subgroups under the…

股票收益的非线性可预测性？预测回归中的参数与非参数推断AI 解读

Nonparametric test procedures in predictive regressions have χ2 limiting null distributions under both low and high regressor persistence, but low local power compared to misspecified linear predictive regressions. We argue that IV inference is better suited (in terms of local power) for analyzing additive predictive models with uncertain predictor persistence. Then, a two-step procedure is proposed for out-of-sample predictions. For the current estimation window, one first…

局部平稳时间序列的预测AI 解读

We develop an estimator for the high-dimensional covariance matrix of a locally stationary process with a smoothly varying trend and use this statistic to derive consistent predictors in nonstationary time series. In contrast to the currently available methods for this problem the predictor developed here does not rely on fitting an autoregressive model and does not require a vanishing trend. The finite sample properties of the new methodology are illustrated by means of a…

因子数增长时充分预测的非参数估计与共形推断AI 解读

The sufficient forecasting (SF) provides a nonparametric procedure to estimate forecasting indices from high-dimensional predictors to forecast a single time series, allowing for the possibly nonlinear forecasting function. This article studies the asymptotic theory of the SF with a diverging number of factors and develops its predictive inference. First, we revisit the SF and explore its connections to Fama–MacBeth regression and partial least squares. Second, with a…

利用随机波动识别结构向量自回归AI 解读

We propose to exploit stochastic volatility for statistical identification of structural vector autoregressive models (SV-SVAR). We discuss full and partial identification of the model and develop efficient EM algorithms for maximum likelihood inference. Simulation evidence suggests that the SV-SVAR works well in identifying structural parameters also under misspecification of the variance process, particularly if compared to alternative heteroscedastic SVARs. We apply the…

高维数据下条件平均处理效应的估计AI 解读

Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the…

无矩约束的高维因子分析AI 解读

Large-dimensional factor model has drawn much attention in the big-data era, to reduce the dimensionality and extract underlying features using a few latent common factors. Conventional methods for estimating the factor model typically requires finite fourth moment of the data, which ignores the effect of heavy-tailedness and thus may result in unrobust or even inconsistent estimation of the factor space and common components. In this article, we propose to recover the…

高维因子随机波动模型中的杠杆效应、非对称性与厚尾AI 解读

We develop a factor stochastic volatility model that incorporates leverage effects, return asymmetry, and heavy tails across all systematic and idiosyncratic model components. Our model leads to a flexible high-dimensional dependence structure that allows for time-varying correlations, tail dependence, and volatility response to both systematic and idiosyncratic return shocks. We develop an efficient Markov chain Monte Carlo algorithm for posterior estimation based on the…

动态宏观经济模型矩结构中的参数信息来源AI 解读

What features of the data are the key sources of information about the parameters in structural macroeconomic models? As such models grow in size and complexity, the answer to this question has become increasingly difficult. This article shows how to identify the main sources of parameter information across different parts of the moment structure of macroeconomic models. In particular, we propose a measure of the relative contribution of information by a given subset of…

分类调查回答的潜在狄利克雷分析AI 解读

Beliefs are important determinants of an individual’s choices and economic outcomes, so understanding how they comove and differ across individuals is of considerable interest. Researchers often rely on surveys that report individual beliefs as qualitative data. We propose using a Bayesian hierarchical latent class model to analyze the comovements and observed heterogeneity in categorical survey responses. We show that the statistical model corresponds to an economic…

反事实处理效应：估计与推断AI 解读

This article proposes statistical methods to evaluate the quantile counterfactual treatment effect (QCTE) if one were to change the composition of the population targeted by a status quo program. QCTE enables a researcher to carry out an ex-ante assessment of the distributional impact of certain policy interventions or to investigate the possible explanations for treatment effect heterogeneity. Assuming unconfoundedness and invariance of the conditional distributions of the…

非平稳数据下的反事实分析与推断AI 解读

Recently, there has been growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial/synthetic counterfactual from a pool of “untreated” peers, organized in a panel data structure. In this article, we investigate the consequences of…

均值结构与自相关稳健的协方差矩阵估计AI 解读

We consider estimation of the asymptotic covariance matrix in nonstationary time series. A nonparametric estimator that is robust against unknown forms of trends and possibly a divergent number of change points (CPs) is proposed. It is algorithmically fast because neither a search for CPs, estimation of trends, nor cross-validation is required. Together with our proposed automatic optimal bandwidth selector, the resulting estimator is both statistically and computationally…

偏相关网络模型中的社区发现AI 解读

We introduce a class of partial correlation network models with a community structure for large panels of time series. In the model, the series are partitioned into latent groups such that correlation is higher within groups than between them. We then propose an algorithm that allows one to detect the communities using the eigenvectors of the sample covariance matrix. We study the properties of the procedure and establish its consistency. The methodology is used to study…

寻找工作：利用Google Trends预测就业增长AI 解读

We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the United States over the period 2004–2019 at both short and long horizons. Starting from an initial search term “jobs,” we construct a large panel of 172 variables using Google’s own algorithms to find semantically related search queries. The best Google Trends model achieves an out-of-sample R2 between 29% and 62% at horizons spanning from one month to…

非参数样本选择下删失回归模型的半参数估计AI 解读

This study proposes a semiparametric estimation method for a censored regression model subject to nonparametric sample selection without the exclusion restriction. Consistency and asymptotic normality of the proposed estimator are established under mild regularity conditions. A Monte Carlo simulation study indicates that the estimator performs well in various designs and outperforms parametric maximum likelihood estimators. An empirical application to female smoking is…

“胡萝卜与大棒”政策的治疗效应与制度效应AI 解读

Public employment service (PES) agencies and caseworkers (CWs) often have substantial leeway in the design and implementation of active labor market policies for the unemployed, and they use policies to a varying extent. We estimate regime effects which capture how CW and PES affect outcomes through different policy intensities. These operate potentially on all forward-looking job seekers regardless of actual treatment exposure. We consider regime effects for two sets of…

基于改进 Hamilton 滤波器的可靠实时产出缺口估计AI 解读

We propose a simple modification of Hamilton’s time series filter that yields reliable and economically meaningful real-time output gap estimates. The original filter relies on 8 quarter ahead forecast errors of a simple autoregression of real GDP. While this approach yields a cyclical component that is hardly revised with new incoming data due to the one-sided filtering approach, it does not cover typical business cycle frequencies evenly, but mutes short and amplifies…

非参数非经典测量误差方法下的代际流动弹性估计AI 解读

This article provides a framework for estimating intergenerational mobility elasticities (IGEs) of children’s income with respect to parental income. We allow the IGEs to be heterogeneous, by leaving the relationship of parental and child incomes unspecified, while acknowledging and addressing the latent nature of both child and parental permanent incomes and the resulted life-cycle bias. Our framework enables us to test the widely imposed assumption that the…