论文检索

零收益过程非平稳时的波动率估计AI 解读

Financial returns are frequently nonstationary due to the nonstationary distribution of zeros. In daily stock returns, for example, the nonstationarity can be due to an upwards trend in liquidity over time, which may lead to a downwards trend in the zero-probability. In intraday returns, the zero-probability may be periodic: It is lower in periods where the opening hours of the main financial centers overlap, and higher otherwise. A nonstationary zero-process invalidates…

部分观测汇总数据下的时变 Markov 过程：冠状病毒应用AI 解读

A major difficulty in the analysis of Covid-19 transmission is that many infected individuals are asymptomatic. For this reason, the total counts of infected individuals and of recovered immunized individuals are unknown, especially during the early phase of the epidemic. In this paper, we consider a parametric time varying Markov process of Coronavirus transmission and show how to estimate the model parameters and approximate the unobserved counts from daily data on…

COVID-19 感染曲线的时间序列分析：变点视角AI 解读

In this paper, we model the trajectory of the cumulative confirmed cases and deaths of COVID-19 (in log scale) via a piecewise linear trend model. The model naturally captures the phase transitions of the epidemic growth rate via change-points and further enjoys great interpretability due to its semiparametric nature. On the methodological front, we advance the nascent self-normalization (SN) technique (Shao, 2010) to testing and estimation of a single change-point in the…

寻找遗失的高频预测：美联储预测效率的新数据与证据AI 解读

Summary We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produce for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these data to study whether the staff forecasts efficiently. Prespecified…

用于政府债券收益率建模的广义贝叶斯时变参数VARAI 解读

Summary US yield curve dynamics are subject to time‐variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time‐varying parameters and stochastic volatility, which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the data and to carry out…

三个及以上时期的固定效应二元选择模型AI 解读

We consider fixed‐effects binary choice models with a fixed number of periods T and regressors without a large support. If the time‐varying unobserved terms are i.i.d. with known distribution F , Chamberlain (2010) shows that the common slope parameter is point identified if and only if F is logistic. However, he only considers in his proof T = 2. We show that the result does not generalize to T ≥ 3: the common slope parameter can be identified when F belongs to a family…

高维关联度表估计：从部门溢出理解大缓和AI 解读

We estimate sectoral spillovers around the Great Moderation with the help of forecast error variance decomposition tables. Obtaining such tables in high dimensions is challenging because they are functions of the estimated vector autoregressive coefficients and the residual covariance matrix. In a simulation study, we compare various regularization methods on both and conduct a comprehensive analysis of their performance. We show that standard estimators of large…

具有偶尔约束生效机制的结构VARAI 解读

We develop a structural VAR in which an occasionally-binding constraint generates censoring of one of the dependent variables. Once the censoring mechanism is triggered, we allow some of the coefficients for the remaining variables to change. We show that a necessary condition for a unique reduced form is that regression functions for the non-censored variables are continuous at the censoring point and that parameters satisfy some mild restrictions. In our application the…

持续性预测回归中的参数不稳定与结构变化检验AI 解读

This paper develops parameter instability and structural change tests within predictive regressions for economic systems governed by persistent vector autoregressive dynamics. Specifically, in a setting where all – or a subset – of the variables may be fractionally integrated and the predictive relation may feature cointegration, we provide sup-Wald break tests that are constructed using the Local speCtruM (LCM) approach. The new tests cover both parameter variation and…

预测模型之间的条件轮换AI 解读

We establish conditions under which forecasting performance can be improved by rotating between a set of underlying forecasts whose predictive accuracy is tracked using a set of time-varying monitoring instruments. We characterize the properties that the monitoring instruments must possess to be useful for identifying, at each point in time, the best forecast and show that these reflect both the accuracy of the predictors used by the underlying forecasting models and the…

通过自归一化进行时间序列分段AI 解读

Abstract We propose a novel and unified framework for change-point estimation in multivariate time series. The proposed method is fully non-parametric, robust to temporal dependence and avoids the demanding consistent estimation of long-run variance. One salient and distinct feature of the proposed method is its versatility, where it allows change-point detection for a broad class of parameters (such as mean, variance, correlation and quantile) in a unified fashion. At the…

COVID-19 感染轨迹建模：分段线性分位数趋势模型AI 解读

Abstract We propose a piecewise linear quantile trend model to analyse the trajectory of the COVID-19 daily new cases (i.e. the infection curve) simultaneously across multiple quantiles. The model is intuitive, interpretable and naturally captures the phase transitions of the epidemic growth rate via change-points. Unlike the mean trend model and least squares estimation, our quantile-based approach is robust to outliers, captures heteroscedasticity (commonly exhibited by…

时变随机对象与动态网络的建模AI 解读

Samples of dynamic or time-varying networks and other random object data such as time-varying probability distributions are increasingly encountered in modern data analysis. Common methods for time-varying data such as functional data analysis are infeasible when observations are time courses of networks or other complex non-Euclidean random objects that are elements of general metric spaces. In such spaces, only pairwise distances between the data objects are available and…

高维时间序列的断点推断AI 解读

For multiple change-points detection of high-dimensional time series, we provide asymptotic theory concerning the consistency and the asymptotic distribution of the breakpoint statistics and estimated break sizes. The theory backs up a simple two-step procedure for detecting and estimating multiple change-points. The proposed two-step procedure involves the maximum of a MOSUM (moving sum) type statistics in the first step and a CUSUM (cumulative sum) refinement step on an…

分位数回归中的单指标门限模型AI 解读

Threshold regression models are useful for identifying subgroups with heterogeneous parameters. The conventional threshold regression models split the sample based on a single and observed threshold variable, which enforces the threshold point to be equal for all subgroups of the population. In this article, we consider a more flexible single-index threshold model in the quantile regression setup, in which the sample is split based on a linear combination of predictors. We…

网络函数型变系数模型AI 解读

We consider functional responses with network dependence observed for each individual at irregular time points. To model both the interindividual dependence and within-individual dynamic correlation, we propose a network functional varying coefficient (NFVC) model. The response of each individual is characterized by a linear combination of responses from its connected nodes and its exogenous covariates. All the model coefficients are allowed to be time dependent. The NFVC…

高维数据变点的自适应推断AI 解读

In this article, we propose a class of test statistics for a change point in the mean of high-dimensional independent data. Our test integrates the U-statistic based approach in a recent work by Wang et al. and the Lq-norm based high-dimensional test in a recent work by He et al., and inherits several appealing features such as being tuning parameter free and asymptotic independence for test statistics corresponding to even q’s. A simple combination of test statistics…

时变参数回归模型中快速且灵活的贝叶斯推断AI 解读

In this article, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, a hierarchical mixture model on the TVPs is introduced. The resulting model closely mimics a random coefficients specification which groups the TVPs into several…

含固定效应与时变系数的半参数空间自回归面板模型AI 解读

This article considers a semiparametric spatial autoregressive (SAR) panel data model with fixed effects and time-varying coefficients. The time-varying coefficients are allowed to follow unknown functions of time, while the other parameters are assumed to be unknown constants. We propose a local linear quasi-maximum likelihood estimation method to obtain consistent estimators for the SAR coefficient, the variance of the error term, and the nonparametric time-varying…

变系数中介模型及其在行为经济学数据分析中的应用AI 解读

This paper is concerned with causal mediation analysis with varying indirect and direct effects. We propose a varying coefficient mediation model, which can also be viewed as an extension of moderation analysis on a causal diagram. We develop a new estimation procedure for the direct and indirect effects based on B-splines. Under mild conditions, rates of convergence and asymptotic distributions of the resulting estimates are established. We further propose a F-type test for…

可能存在多次周期结构变化的非平稳时间序列稳健推断AI 解读

Motivated by two examples concerning global warming and monthly total import and export by China, we study time series that contain a nonparametric periodic component with an unknown period, a nonparametric trending behavior and also additive covariate effects. Further, as the amplitude function may change at some known or unknown change-point(s), we extend our model to take this dynamical periodicity into account and introduce two change-point estimators. To the best of…

利用双重边缘化子抽样进行海量数据微观计量模型的贝叶斯推断AI 解读

Bayesian inference with a large dataset is computationally intensive, as Markov chain Monte Carlo simulation requires a complete scan of the dataset for each proposed parameter update. To reduce the number of data points evaluated at each iteration of posterior simulation, we develop a double marginalized subsampling method, which is applicable to a wide array of microeconometric models including Tobit, Probit, regressions with non-Gaussian errors, heteroscedasticity and…

干预特定平均结局的连续时间目标最小损失估计AI 解读

This paper generalizes the targeted minimum loss-based estimation (TMLE) framework to allow for estimating the effects of time-varying interventions in settings where both interventions, covariates, and outcome can happen at subject-specific time-points on an arbitrarily fine time-scale. TMLE is a general template for constructing asymptotically linear substitution estimators for smooth low-dimensional parameters in infinite-dimensional models. Existing longitudinal TMLE…

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…