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非平稳波动环境下股票收益可预测性的实时监测AI 解读

Stock return predictability, should it exist, is likely to be episodic in nature. In order to exploit such pockets of predictability it is essential that they are rapidly detected, in real-time, as the nascent predictive regime emerges. This will typically entail the repeated (sequential) application of one-shot end-of-sample predictability statistics, updated as new data become available. Consequently, in addition to dealing with the usual inference problems caused by…

时变可预测性的稳健推断：Sieve-IVX 方法AI 解读

Predictive regression models are often used to evaluate the predictive capability of economic fundamentals on bond and equity returns. Inferential procedures in these regressions typically employ parameter constancy or piecewise constancy in slope coefficients. Such formulations are prone to misspecification, more especially during periods of disturbance or evolution in prevailing economic and financial conditions, which can lead to size distortion and spurious evidence of…

联合尾部风险监测：增长与通胀的应用AI 解读

This paper develops the concept of Growth and Inflation at Risk frontier (GIaR). This is a bivariate generalisation of the concepts of Growth-at-Risk (GaR) and Inflation-at-Risk (IaR). We propose a novel approach to identify and estimate GIaR and provide uniformly valid upper and lower confidence bands. We first apply our procedure to predict the conditional probability of stagflation. Second, we compute worst-case scenarios for a policy maker who is concerned about the…

复杂时间动态下单调且平滑时变函数的同时推断AI 解读

We propose a new framework for the simultaneous inference of monotone and smoothly time-varying functions under complex temporal dynamics. This will be done utilizing the monotone rearrangement and the nonparametric estimation. We capitalize the Gaussian approximation for the nonparametric monotone estimator and construct the asymptotically correct simultaneous confidence bands (SCBs) using designed bootstrap methods. We investigate two general and practical scenarios. The…

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谱密度函数的实用区间估计方法AI 解读

The spectral density function can play a key role in time series analysis, where nonparametric interval estimation of the spectral density is a fundamental issue. However, the prevailing pointwise interval methods for spectral densities, including chi-square approximation and frequency domain bootstrap (FDB), can be misleading in practice, perhaps more so than appreciated, as confidence intervals often exhibit low coverage accuracy as well as high sensitivity to tuning…

非线性 DSGE 模型的有限样本识别稳健推断AI 解读

ABSTRACT We develop identification‐robust likelihood‐free simultaneous confidence sets for dynamic stochastic general equilibrium models, without relying on linear approximations. Our methodology integrates simulation estimation methods using auxiliary statistics with Monte Carlo test principles. Results cover deep parameters and impulse responses. Auxiliary statistics include coefficients of linear and nonlinear vector autoregressions and local projections. Proposed…

大型向量自回归模型中的网络格兰杰因果关系发现AI 解读

This article proposes novel inferential procedures for discovering the network Granger causality in highdimensional vector autoregressive models.In particular, we mainly offer two multiple testing procedures designed to control the false discovery rate (FDR).The first procedure is based on the limiting normal distribution of the t-statistics with the debiased lasso estimator.The second procedure is its bootstrap version.We also provide a robustification of the first…

精度矩阵最小二乘：高维估计与推断AI 解读

The least squares estimator can be cast as depending only on the precision matrix of the data, similar to the weights of a global minimum variance portfolio. We give conditions under which any plug-in precision matrix estimator produces an unbiased and consistent least squares estimator for stationary time series regressions, in both low- and high-dimensional settings. Such conditions define a class of “Precision Least Squares” (PrLS) estimators, which are shown to be…

高维谱密度矩阵的统计推断AI 解读

The spectral density matrix is a fundamental object of interest in time series analysis, and it encodes both contemporary and dynamic linear relationships between component processes of the multivariate system. In this paper we develop novel inference procedures for the spectral density matrix in the high-dimensional setting. Specifically, we introduce a new global testing procedure to test the nullity of the cross-spectral density for a given set of frequencies and across…

高维时间序列的频域统计推断AI 解读

Analyzing time series in the frequency domain enables the development of powerful tools for investigating the second-order characteristics of multivariate processes. Parameters like the spectral density matrix and its inverse, the coherence or the partial coherence, encode comprehensively the complex linear relations between the component processes of the multivariate system. In this paper, we develop inference procedures for such parameters in a high-dimensional, time…

时间序列数据的高维 Knockoffs 推断AI 解读

We make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time series knockoffs inference (TSKI) by exploiting the ideas of subsampling and e-values to address the difficulty caused by the serial dependence. We also generalize the robust knockoffs inference in Barber, Candès, and Samworth to the time series setting to relax the assumption of known covariate…

协整向量自回归过程的统一推断AI 解读

Uniformly valid inference for cointegrated vector autoregressive processes has so far proven difficult due to certain discontinuities arising in the asymptotic distribution of the least squares estimator. We extend asymptotic results from the univariate case to multiple dimensions and show how inference can be based on these results. Furthermore, we show that lag augmentation and a recent instrumental variable procedure can also yield uniformly valid tests and confidence…

参数不稳定下高维线性回归的变量选择AI 解读

This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigate the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating…

非平稳时间序列的最优速率高斯逼近与显式构造AI 解读

Statistical inference for time series such as curve estimation for time-varying models or testing for existence of change-point have garnered significant attention. However, these works are generally restricted to the assumption of independence and/or stationarity at its best. The main obstacle is that the existing Gaussian approximation results for non-stationary processes only provide an existential proof and thus they are difficult to apply. In this paper, we provide two…

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调查密度预期差异检验：组成数据方法AI 解读

Summary We propose to treat survey‐based density expectations as compositional data when testing either for heterogeneity in density forecasts across different groups of agents or for changes over time. Monte Carlo simulations show that the proposed test has more power relative to both a bootstrap approach based on the KLIC and an approach that involves multiple testing for differences of individual parts of the density. In addition, the test is computationally much faster…

空间依赖面板数据的一致非参数推断AI 解读

This article proposes a uniform functional inference method for nonparametric regressions in a panel-data setting that features general unknown forms of spatio-temporal dependence. The method requires a long time span, but does not impose any restriction on the size of the cross section or the strength of spatial correlation. The uniform inference is justified via a new growing-dimensional Gaussian coupling theory for spatio-temporally dependent panels. We apply the method…

结构变化检测中控制错误发现率的广义Knockoff程序AI 解读

Controlling false discovery rate (FDR) is crucial for variable selection, multiple testing, among other signal detection problems. In literature, there is certainly no shortage of FDR control strategies when selecting individual features, but the relevant works for structural change detection, such as profile analysis for piecewise constant coefficients and integration analysis with multiple data sources, are limited. In this paper, we propose a generalized knockoff…

线性时变系数模型的筛自助法推断AI 解读

We propose a sieve bootstrap framework to conduct pointwise and simultaneous inference for time-varying coefficient regression models based on a local linear estimator. The asymptotic validity of the sieve bootstrap in the presence of autocorrelation is established. The bootstrap automatically produces a consistent estimate of nuisance parameters, both at the interior and boundary points. In addition, we develop a bootstrap-based test for parameter constancy and examine its…

含个体、时间及交互效应的短期动态面板模型AI 解读

Summary This paper proposes a transformed quasi‐maximum likelihood (TQML) estimator for short dynamic fixed effects panel data models allowing for interactive effects through a multifactor error structure. The proposed estimator is robust to the heterogeneity of the initial values and common unobserved effects, while at the same time allowing for standard fixed and time effects. It is applicable to both stationary and unit root cases. The order condition for identification…

滚动回归估计量的分布AI 解读

We establish the asymptotic distribution for rolling linear regression models using various window widths. The limiting distribution depends on the width of the rolling window and on a “bias process” that is typically ignored in practice. Based on the asymptotic distribution, we tabulate critical values used to find uniform confidence intervals for the average values of regression parameters over the windows. We propose a corrected rolling regression technique that removes…

高维面板函数型时间序列的统计推断AI 解读

Abstract In this paper, we develop statistical inference tools for high-dimensional functional time series. We introduce a new concept of physical dependent processes in the space of square integrable functions, which adopts the idea of basis decomposition of functional data in these spaces, and derive Gaussian and multiplier bootstrap approximations for sums of high-dimensional functional time series. These results have numerous important statistical consequences.…

通过错误率控制数据驱动地选择变点数量AI 解读

In multiple change-point analysis, one of the main difficulties is to determine the number of change-points. Various consistent selection methods, including the use of Schwarz information criterion and cross-validation, have been proposed to balance the model fitting and complexity. However, there is lack of systematic approaches to provide theoretical guarantee of significance in determining the number of changes. In this paper, we introduce a data-adaptive selection…

稀疏性诱导的弱因子模型推断AI 解读

In this article, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sectional dimension, N. We propose a novel inferential procedure to decide whether each component of the factor loadings is zero or not, and prove that this controls the false discovery rate (FDR) below a preassigned level, while…

利用高频数据预测低频宏观事件AI 解读

Summary High‐frequency financial and economic indicators are usually time‐aggregated before computing forecasts of macroeconomic events, such as recessions. We propose a mixed‐frequency alternative that delivers high‐frequency probability forecasts (including their confidence bands) for low‐frequency events. The new approach is compared with single‐frequency alternatives using loss functions for rare‐event forecasting. We find (i) the weekly‐sampled term spread improves over…