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含非参数内生效应的横截面互动模型之筛工具变量估计AI 解读

In this study, we consider cross-sectional interaction models including spatial autoregressive models and peer effects models as special cases. Our model allows the endogenous effect – the effect of others’ outcomes on one’s own outcome – to be nonlinear and nonparametric. For the model estimation, we propose a sieve instrumental variable estimator and establish both its consistency and asymptotic normality. Furthermore, we propose a nonparametric specification test for the…

模型平均能否改进模型选择？AI 解读

In regression, model averaging (MA) provides an alternative to model selection (MS), and asymptotic efficiency theories have been derived for both MS and MA. Basically, under sensible conditions, MS asymptotically achieves the smallest estimation loss/risk among the candidate models, and MA does so among averaged estimators from the models with convex weights. Clearly, MA can beat MS by any extent in rate of convergence when all the candidate models have large biases that…

二元响应的半参数模型平均预测AI 解读

Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on either parametric models or nonparametric models with a continuous response. In this paper, we propose a semiparametric model averaging prediction (SMAP) method for a dichotomous response. The idea is to approximate the unknown score function by a…

股东积极主义的价值创造AI 解读

We measure value creation by activist investors via structural estimation of a model of the choice between passive investment and activism. Our estimates imply that average returns following activist intent announcements consist of 74.8% expected value creation, or treatment, 13.4% stock picking, and 11.8% sample selection effects. Higher treatment values predict improvements in firm performance and lower proxy contest probabilities, whereas abnormal announcements returns do…

分位数双重自回归模型AI 解读

Many financial time series have varying structures at different quantile levels, and also exhibit the phenomenon of conditional heteroskedasticity at the same time. However, there is presently no time series model that accommodates both of these features. This paper fills the gap by proposing a novel conditional heteroskedastic model called “quantile double autoregression”. The strict stationarity of the new model is derived, and self-weighted conditional quantile estimation…

时间序列多个变点的自助法推断AI 解读

In this paper, we propose two bootstrap procedures, namely parametric and block bootstrap, to approximate the finite sample distribution of change-point estimators for piecewise stationary time series. The bootstrap procedures are then used to develop a generalized likelihood ratio scan method (GLRSM) for multiple change-point inference in piecewise stationary time series, which estimates the number and locations of change-points and provides a confidence interval for each…

相关随机系数变换模型的识别与估计AI 解读

This study examines identification and estimation in a correlated random coefficients (CRC) model with an unknown transformation of the dependent variable, namely $\lambda \left (Y^{*}\right)=B_{0}+X^{\prime }B$ , where the latent outcome $Y^{*}$ may be subject to a certain kind of censoring mechanism, $\lambda (\cdot)$ is an unknown, one-to-one monotone function, and the random coefficients $\left (B_{0},B\right)$ are allowed to be correlated with one or several components…

广义频带谱估计及其在新凯恩斯菲利普斯曲线中的应用AI 解读

Summary This paper proposes a new method for estimating linear dynamic structural models. The proposed generalized band spectrum estimator (GBSE) generalizes band spectrum regression to simultaneously account for endogeneity and nonlinearities of unknown form in the first stage, while having a computationally convenient closed‐form expression in the time domain. We apply the GBSE to the hybrid New Keynesian Phillips curve (NKPC) with U.S. postwar data. We find a stable…

机器学习如何帮助宏观经济预测？AI 解读

Summary We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by adding the how . The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. To the contrary, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so‐called features (nonlinearities, regularization, cross‐validation, and alternative loss function)…

让文本发挥作用：利用报纸文本进行经济预测AI 解读

Summary This paper examines several ways to extract timely economic signals from newspaper text and shows that such information can materially improve forecasts of macroeconomic variables including GDP, inflation and unemployment. Our text is drawn from three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. Exploiting newspaper text can improve economic forecasts both unconditionally and when…

以周频率即时预测经济活动的尾部风险AI 解读

Summary This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information used to produce nowcasts on a weekly basis. We consider Bayesian mixed frequency regressions with stochastic volatility and Bayesian quantile regressions. Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making…

时变波动下的稳健推断：专业预测者的实时评估AI 解读

Summary In many forecast evaluation applications, standard tests as well as tests allowing for time‐variation in relative forecast ability build on heteroskedasticity‐and‐autocorrelation consistent (HAC) covariance estimators. Yet, the finite‐sample performance of these asymptotics is often poor. “Fixed‐ ” asymptotics, used to account for long‐run variance estimation, improve finite‐sample performance under homoskedasticity, but lose asymptotic pivotality under time‐varying…

结构突变下的最优预测AI 解读

Summary This paper develops an optimal combined estimator to forecast out‐of‐sample under structural breaks. When it comes to forecasting, using only the postbreak observations after the most recent break point may not be optimal. In this paper, we propose a new estimation method that exploits the prebreak information. In particular, we show how to combine the estimator using the full‐sample (i.e., both the prebreak and postbreak data) and the estimator using only the…

对冲基金的极端关联性AI 解读

Abstract We propose a dynamic measure of extremal connectedness tailored to the short reporting period and unbalanced nature of hedge funds data. Using multivariate extreme value regression techniques, we estimate this measure conditional on factors reflecting the economic uncertainty and the state of the financial markets, and derive risk indicators reflecting the likelihood of extreme spillovers. Empirically, we study the dynamics of tail dependencies between hedge funds…

非参数经验贝叶斯预测AI 解读

Henceforth, the Journal of the American Statistical Association 2022 Theory and Methods Invited Paper by Nikolaos Ignatiadis and Stefan Wager, “Nonparametric Empirical Bayes Analysis,” will be refe...

对《非参数经验贝叶斯分析的置信区间》的讨论AI 解读

We would like to start with congratulating the authors. Empirical Bayes estimation is a very old, well studied problem. However, construction of confidence intervals in empirical Bayes setting has ...

作者回复：非参数经验贝叶斯分析的置信区间AI 解读

In our article, we introduced two general methods for conducting inference for empirical Bayes estimands. The discussants have contributed to our work by clarifying and raising important issues reg...

经验贝叶斯区间的覆盖性质：对非参数经验贝叶斯置信区间研究的讨论AI 解读

Empirical Bayes methods are commonly used in the analysis of data coming from multiple related populations or groups, where often the goal is to obtain a parameter estimate for each individual grou...

关于“非参数经验贝叶斯分析的置信区间”的讨论AI 解读

We congratulate Ignatiadis and Wager (IW) on this significant step forward, incorporating uncertainty in the estimated prior into empirical Bayes (EB) analysis. EB analyses have traditionally ignor...

关于非参数经验贝叶斯分析置信区间的讨论AI 解读

I congratulate the authors for a fantastic piece of work about confidence intervals in an inverse problem involving a mixing distribution under an empirical Bayes setup. We can regard the problem a...

高维中介分析：筛选连接童年创伤与皮质醇应激反应的 DNA 甲基化位点AI 解读

Childhood trauma tends to influence cortisol stress reactivity through the mediating effects of DNA methylation. Houtepen et al. conducted a study to investigate the role of DNA methylation in cortisol stress reactivity and its relationship with childhood trauma. The study collected a dataset consisting of 385,882 DNA methylation loci, cortisol stress reactivity, one-dimensional score on a childhood trauma questionnaire and several covariates for 85 healthy individuals. Of…

非参数经验贝叶斯分析的置信区间AI 解读

In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results provide a comprehensive characterization of when and why empirical Bayes point estimates accurately recover oracle Bayes behavior. In this paper, we develop flexible and practical confidence intervals that provide asymptotic frequentist coverage of empirical Bayes estimands, such as…

多元点模式数据的半参数多项 logistic 回归AI 解读

We propose a new method for analysis of multivariate point pattern data observed in a heterogeneous environment and with complex intensity functions. We suggest semiparametric models for the intensity functions that depend on an unspecified factor common to all types of points. This is for example well suited for analyzing spatial covariate effects on events such as street crime activities that occur in a complex urban environment. A multinomial conditional composite…

极值均值回归（Extremile Regression）AI 解读

Regression extremiles define a least squares analogue of regression quantiles. They are determined by weighted expectations rather than tail probabilities. Of special interest is their intuitive meaning in terms of expected minima and maxima. Their use appears naturally in risk management where, in contrast to quantiles, they fulfill the coherency axiom and take the severity of tail losses into account. In addition, they are comonotonically additive and belong to both the…