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多期限预测误差的时变不确定性建模AI 解读

We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to simple variance approaches, our stochastic…

稀疏匹配数据下双向固定效应线性模型的估计与推断AI 解读

Models with multiway fixed effects are frequently used to address selection on unobservables. The data used for estimating these models often contain few observations per value of either indexing variable (sparsely matched data). I show that this sparsity has important implications for inference and propose an asymptotically valid inference method based on subsetting. Sparsity also has important implications for point estimation when covariates or instrumental variables are…

从附加福利到核心报酬：工会与员工薪酬AI 解读

Conventional wisdom suggests that labor unions raise worker wages, while the newer empirical literature finds only negligible earnings effects. I reconcile this apparent contradiction by arguing that collective bargaining targets fringe benefits. Using U.S. firm-level data from the Bureau of Economic Analysis (BEA) Multinational Enterprise Survey and Compustat, I exploit a regression discontinuity in majority rule union elections to compare changes in employee compensation…

英国区域产出增长：始于1970年、更及时且频率更高的估计AI 解读

Summary Output growth estimates for regions of the UK are currently published at an annual frequency only, released with a long delay, and offer limited historical coverage. To improve the regional database this paper develops a mixed‐frequency multivariate model and uses it to produce consistent estimates of quarterly regional output growth dating back to 1970. We describe how these estimates are updated and evaluated on an ongoing, quarterly basis to publish online (at…

页岩革命与原油价格动态的转变AI 解读

Summary Oil price fluctuates in response to both demand and supply shocks. This paper proposes a new methodology that allows for timely identification of the shifting contribution from the two types of shock through a joint analysis of crude futures options and stock index options. Historical analysis shows that crude oil price movements are dominated by supply shocks from 2004 to 2008, but demand shocks have become much more dominant since then. The large demand shock…

动态随机前沿模型的似然估计方法AI 解读

Summary This paper considers a panel stochastic production frontier model that allows the dynamic adjustment of technical inefficiency. In particular, we assume that inefficiency follows an AR(1) process. That is, the current year's inefficiency for a firm depends on its past inefficiency plus a transient inefficiency incurred in the current year. Interfirm variations in the transient inefficiency are explained by some firm‐specific covariates. We consider four…

多元动态强度超阈值模型AI 解读

Summary We propose a multivariate dynamic intensity peaks‐over‐threshold model to capture extremes in multivariate return processes. The random occurrence of extremes is modeled by a multivariate dynamic intensity model, while temporal clustering of their size is captured by an autoregressive multiplicative error model. Applying the model to daily returns of three major stock indexes yields strong empirical support for a temporal clustering of both the occurrence and the…

具有固定效应的区间删失回归AI 解读

Summary This paper considers identification and estimation of a fixed‐effects model with an interval‐censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered: a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the…

石油供给冲击对美国经济活动的影响：我们学到了什么？AI 解读

Summary Estimated responses of real oil prices and US gross domestic product (GDP) to oil supply disruptions vary widely. We show that most variation is attributable to differences in identification assumptions and in the model specification. Models that allow for a large short‐run price elasticity of oil supply imply a larger response of oil prices and a larger, longer lived contraction in US real GDP. We find that, if we condition on a range of supply elasticity values…

基于随机扰动的因子个数秩估计AI 解读

Summary We introduce a random-perturbation-based rank estimator of the number of factors of a large-dimensional approximate factor model. An expansion of the rank estimator demonstrates that the random perturbation reduces the biases due to the persistence of the factor series and the dependence between the factor and error series. A central limit theorem for the rank estimator with convergence rate higher than root $n$ gives a new hypothesis-testing procedure for both…

利用个性化PageRank对巨型分块模型图进行定向抽样AI 解读

Summary The paper provides statistical theory and intuition for personalized PageRank (called ‘PPR’): a popular technique that samples a small community from a massive network. We study a setting where the entire network is expensive to obtain thoroughly or to maintain, but we can start from a seed node of interest and ‘crawl’ the network to find other nodes through their connections. By crawling the graph in a designed way, the PPR vector can be approximated without…

再随机化与回归调整AI 解读

Summary Randomization is a basis for the statistical inference of treatment effects without strong assumptions on the outcome-generating process. Appropriately using covariates further yields more precise estimators in randomized experiments. R. A. Fisher suggested blocking on discrete covariates in the design stage or conducting analysis of covariance in the analysis stage. We can embed blocking in a wider class of experimental design called rerandomization, and extend the…

流式数据广义线性模型的可更新估计与增量推断AI 解读

Summary The paper presents an incremental updating algorithm to analyse streaming data sets using generalized linear models. The method proposed is formulated within a new framework of renewable estimation and incremental inference, in which the maximum likelihood estimator is renewed with current data and summary statistics of historical data. Our framework can be implemented within a popular distributed computing environment, known as Apache Spark, to scale up computation.…

基于随机偏微分方程的多元 G 型 Matérn 随机场AI 解读

Summary For many applications with multivariate data, random-field models capturing departures from Gaussianity within realizations are appropriate. For this reason, we formulate a new class of multivariate non-Gaussian models based on systems of stochastic partial differential equations with additive type G noise whose marginal covariance functions are of Matérn type. We consider four increasingly flexible constructions of the noise, where the first two are similar to…

理解敏感性：扩展遗漏变量偏误分析AI 解读

Summary We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in regression models that does not require assumptions on the functional form of the treatment assignment mechanism nor on the distribution of the unobserved confounders, naturally handles multiple confounders, possibly acting non-linearly, exploits expert knowledge to bound sensitivity parameters and can be easily computed by using only standard regression results. In…

时间序列误差下非参数回归的多尺度推断与长期方差估计AI 解读

Summary We develop new multiscale methods to test qualitative hypotheses about the function m in the non-parametric regression model Yt, T = m(t/T) + ɛt with time series errors ɛt. In time series applications, m represents a non-parametric time trend. Practitioners are often interested in whether the trend m has certain shape properties. For example, they would like to know whether m is constant or whether it is increasing or decreasing in certain time intervals. Our…

基于Pólya树的相关密度贝叶斯层级模型AI 解读

Summary Bayesian hierarchical models are used to share information between related samples and to obtain more accurate estimates of sample level parameters, common structure and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. Various such models have been described in the literature using extensions of the Dirichlet process and related processes,…

总体间接因果效应的稳健推断：广义前门准则AI 解读

Summary Standard methods for inference about direct and indirect effects require stringent no-unmeasured-confounding assumptions which often fail to hold in practice, particularly in observational studies. The goal of the paper is to introduce a new form of indirect effect, the population intervention indirect effect, that can be non-parametrically identified in the presence of an unmeasured common cause of exposure and outcome. This new type of indirect effect captures the…

控制混杂因素的条件独立性置换检验AI 解读

Summary We propose a general new method, the conditional permutation test, for testing the conditional independence of variables X and Y given a potentially high dimensional random vector Z that may contain confounding factors. The test permutes entries of X non-uniformly, to respect the existing dependence between X and Z and thus to account for the presence of these confounders. Like the conditional randomization test of Candès and co-workers in 2018, our test relies on…

复杂调查数据的贝叶斯经验似然推断AI 解读

Summary We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non-differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically…

删失数据下估计方程数量增长的惩罚广义经验似然AI 解读

This article considers simultaneous variable selection and parameter estimation as well as hypothesis testing in censored survival models where a parametric likelihood is not available. For the problem, we utilize certain growing dimensional general estimating equations and propose a penalized generalized empirical likelihood, where the general estimating equations are constructed based on the semiparametric efficiency bound of estimation with given moment conditions. The…

We study weighted particle systems in which new generations are resampled from current particles with probabilities proportional to their weights. This covers a broad class of sequential Monte Carlo (SMC) methods, widely-used in applied statistics and cognate disciplines. We consider the genealogical tree embedded into such particle systems, and identify conditions, as well as an appropriate time-scaling, under which they converge to the Kingman $n$-coalescent in the…

线性变换尖峰模型中的最优预测AI 解读

We consider the linearly transformed spiked model, where the observations $Y_{i}$ are noisy linear transforms of unobserved signals of interest $X_{i}$: \begin{equation*}Y_{i}=A_{i}X_{i}+\varepsilon_{i},\end{equation*} for $i=1,\ldots ,n$. The transform matrices $A_{i}$ are also observed. We model the unobserved signals (or regression coefficients) $X_{i}$ as vectors lying on an unknown low-dimensional space. Given only $Y_{i}$ and $A_{i}$ how should we predict or recover…

主成分线性泛函的有效估计AI 解读

We study principal component analysis (PCA) for mean zero i.i.d. Gaussian observations $X_{1},\dots,X_{n}$ in a separable Hilbert space $\mathbb{H}$ with unknown covariance operator $\Sigma $. The complexity of the problem is characterized by its effective rank $\mathbf{r}(\Sigma):=\frac{\operatorname{tr}(\Sigma)}{\|\Sigma \|}$, where $\mathrm{tr}(\Sigma)$ denotes the trace of $\Sigma $ and $\|\Sigma\|$ denotes its operator norm. We develop a method of bias reduction in the…