论文检索

大学中的学业与非学业投入：预期、偏好和约束的作用AI 解读

This paper estimates a discrete choice model of time allocation decisions made by university students. We consider investments in academic and non-academic activities, such as job placements or volunteering. Identification is achieved using data collected through a recent survey of UK university students on subjective expectations about the returns to these activities, and the enjoyment students derive from them. Unobserved heterogeneity in the choice set is addressed using…

婚姻、子女与劳动供给：信念和实际结果AI 解读

While a large literature is interested in the relationship between family and labor supply outcomes, little is known about the expectations of these objects at earlier stages. We examine these expectations, taking advantage of unique data from the Berea Panel Study. In addition to characterizing expectations, starting during college, the data details outcomes for ten years after graduation. Methodological contributions come from approaches to validate quality of survey…

健康与退休研究中概率预期的尾部与中心舍入AI 解读

We study rounding of numerical expectations in the Health and Retirement Study (HRS) between 2002 and 2014. We document that respondent-specific rounding patterns across questions in individual waves are quite stable across waves. We discover a tendency by about half of the respondents to provide more refined responses in the tails of the 0–100 scale than the center. In contrast, only about five percent of the respondents give more refined responses in the center than the…

企业不确定性的调查测量AI 解读

We elicit subjective probability distributions from business executives about their own-firm outcomes at a one-year look-ahead horizon. In terms of question design, our key innovation is to let survey respondents freely select support points and probabilities in five-point distributions over future sales growth, employment, and investment. In terms of data collection, we develop and field a new monthly panel Survey of Business Uncertainty. The SBU began in 2014 and now…

激励驱动的注意力不足AI 解读

“Rational inattention” is becoming increasingly prominent in economic modeling, but there is little empirical evidence for its central premise-that the choice of attention results from a cost-benefit optimization. Observational data typically do not allow researchers to infer attention choices from observables. We fill this gap in the literature by exploiting a unique dataset of professional forecasters who update their inflation forecasts at days of their choice. In the…

参数估计应如何适配使用目标？AI 解读

We study parameter estimation from the sample X, when the objective is to maximize the expected value of a criterion function, Q, for a distinct sample, Y. This is the situation that arises when a model is estimated for the purpose of describing other data than those used for estimation, such as in forecasting problems. A natural candidate for solving maxT∈σ(X)EQ(Y,T) is the innate estimator, θˆ=argmaxθQ(X,θ). While the innate estimator has certain advantages, we show that…

局部错误定价与市场微观结构噪声：参数化视角AI 解读

We extend the classic ”martingale-plus-noise” model for high-frequency returns to accommodate an error correction mechanism and endogenous pricing errors. It is motivated by (i) novel empirical evidence documenting that microstructure noise exhibits frequently changing patterns of serial dependence which are interwoven with innovations to the efficient price; (ii) building a bridge between high-frequency econometrics and market microstructure models. We identify temporal…

跳跃扩散模型中跳跃成分的存在性检验AI 解读

In this paper we propose a nonparametric test to determine whether an underlying jump diffusion process indeed contains jump component, or equivalently, is indeed a diffusion. Our test is based upon a robust threshold estimation of diffusive volatility and the kernel estimation of the conditional moment function of the squared instantaneous increments of the underlying process. We show that our test statistic has asymptotic standard normal distribution under the null…

含平稳协变量的分位数协整非参数推断AI 解读

This paper considers the inference problems in nonlinear quantile regressions with both stationary and nonstationary covariates. The nonparametric local constant quantile estimator is proposed to estimate the unknown quantile regression function, whose asymptotic properties are established under quite general conditions. Specification testing of the quantile regression function is further considered through a statistic constructed based on the integrated squared distance…

分位数回归的广义矩估计AI 解读

This paper develops generalized method of moments (GMM) estimation and inference procedures for quantile regression models. We propose a GMM estimator for simultaneous estimation across multiple quantiles. This estimator allows us to model quantile regression coefficients using flexible parametric restrictions across quantiles. The restrictions and simultaneous estimation lead to efficiency gains compared to standard methods. We establish the asymptotic properties of the GMM…

厚尾、非对称或异方差误差下高维均值回归的稳健选择后推断AI 解读

We propose a robust post-selection inference method based on the Huber loss for the regression coefficients, when the error distribution is heavy-tailed and asymmetric in a high-dimensional linear model with an intercept term. The asymptotic properties of the resulting estimators are established under mild conditions. We also extend the proposed method to accommodate heteroscedasticity assuming the error terms are symmetric and other suitable conditions. Statistical tests…

存在测量误差的变系数模型估计AI 解读

We propose a semiparametric estimator for varying coefficient models when the regressors in the nonparametric components are measured with error. Varying coefficient models are an extension of other popular semiparametric models , including partially linear and nonparametric additive models, and deliver an attractive solution to the curse-of-dimensionality. We use deconvolution kernel estimation in a two-step procedure and show that the estimator is consistent and…

尾部删失数据下尾部特征的估计与推断AI 解读

This paper considers estimation and inference about tail features such as tail index and extreme quantile when the observations beyond some threshold are censored. Ignoring such tail censoring could lead to substantial bias and size distortion, even if the censored probability is tiny. We first propose a new maximum likelihood estimator (MLE) based on the Pareto tail approximation and derive its asymptotic properties. Then, we propose an alternative method of constructing…

协方差—均值回归的统计推断AI 解读

In this article, we introduce a covariance-mean regression model with heterogeneous similarity matrices. It not only links the covariance of responses to heterogeneous similarity matrices induced by auxiliary information, but also establishes the relationship between the mean of responses and covariates. Under this new model setting, however, two statistical inference challenges are encountered. The first challenge is that the consistency of the covariance estimator based on…

多潜变量模型的快速精确变分推断AI 解读

Models with a large number of latent variables are often used to utilize the information in big or complex data, but can be difficult to estimate. Variational inference methods provide an attractive solution. These methods use an approximation to the posterior density, yet for large latent variable models existing choices can be inaccurate or slow to calibrate. Here, we propose a family of tractable variational approximations that are more accurate and faster to calibrate…

贝叶斯后验均值的抽样性质及其在 WALS 估计中的应用AI 解读

Many statistical and econometric learning methods rely on Bayesian ideas. When applied in a frequentist setting, their precision is often assessed using the posterior variance. This is permissible asymptotically, but not necessarily in finite samples. We explore this issue focusing on weighted-average least squares (WALS), a Bayesian-frequentist ‘fusion’. Exploiting the sampling properties of the posterior mean in the normal location model, we derive estimators of the…

具有内生同步效应的马尔可夫转换面板模型AI 解读

This paper introduces a new dynamic panel model with multi-layer network effects. Series-specific latent Markov chain processes drive the dynamics of the observable processes, and several types of interaction effects among the hidden chains allow for various degrees of endogenous synchronization of both latent and observable processes. The interaction is driven by a multi-layer network with exogenous and endogenous connectivity layers. We provide some theoretical properties…

基于期权的非参数跳跃变差度量AI 解读

This paper proposes a novel nonparametric method for estimating tail jump variation measures from short-dated options, which can achieve rate-efficiency and works in a general infinite jump activity setting, avoiding parametric or semiparametric assumptions for the jump measure. The method is based on expressing the measures of interest as integrals of the Laplace transforms of the jump compensator and developing methods for recovering nonparametrically the latter from the…

全球气温与温室气体：共同特征方法AI 解读

We propose a common features approach for testing for common trends and estimating long-run relationships between variables with complex trends. Using this approach, we establish that global temperatures and the concentration of greenhouse gases share a common trend and we estimate their long-run relationship without conditioning on the exact nature of this trend.

具有递增半参数单指标交互项的预测型函数线性模型AI 解读

When predicting crop yield using both functional and multivariate predictors, the prediction performances benefit from the inclusion of the interactions between the two sets of predictors. We assume the interaction depends on a nonparametric, single-index structure of the multivariate predictor and reduce each functional predictor’s dimension using functional principal component analysis (FPCA). Allowing the number of FPCA scores to diverge to infinity, we consider a…

条件离散—连续分布的自适应贝叶斯估计及股票交易活动应用AI 解读

We consider Bayesian nonparametric estimation of conditional discrete-continuous distributions. Our model is based on a mixture of normal distributions with covariate dependent mixing probabilities. We use continuous latent variables for modeling the discrete part of the distribution. The marginal distribution of covariates is not modeled. Under anisotropic smoothness conditions on the data generating conditional distribution and a possibly increasing number of the support…

促进大规模状态空间模型简约性的先验AI 解读

State–space models are commonly used in the engineering, economic, and statistical literature. They are flexible and encompass many well-known statistical models, including random coefficient autoregressive models and dynamic factor models. Bayesian analysis of state–space models has attracted much interest in recent years. However, for large scale models, prior specification becomes a challenging issue in Bayesian inference. In this paper, we propose a flexible prior for…

仿射无套利收益率网络模型及欧债危机应用AI 解读

We develop a parsimonious class of affine arbitrage-free yield net models for consistent bond pricing across maturities and issuers of different risk levels. Containing a core curve and multiple peripheral curves, the yield net is spanned by three layers of factors: base factors spanning all curves, and common and individual spread factors. Under the arbitrage-free assumption, we prove a parsimonious solution to the risk-neutral process that guarantees joint identification…

基于后验分布的 Wald 型假设检验统计量AI 解读

A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions under the correct model specification. The new statistic can be explained as a posterior version of the Wald statistic and has several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids Jeffreys–Lindley–Bartlett’s paradox. Third, under the null hypothesis and repeated sampling, it follows a χ2 distribution asymptotically,…