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非参数时变工具变量结构 VAR：估计与推断AI 解读

Abstract This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even…

错位处理采用下合成控制的不确定性量化AI 解读

Abstract We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of different causal quantities to be predicted, which we call causal predictands, allowing for multiple treated units with treatment adoption at possibly…

局部加权共形预测：随机化带来稳健保证AI 解读

Abstract In this work, we consider the problem of building distribution-free prediction intervals with finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increasingly popular framework for building such intervals with distribution-free guarantees, but these guarantees only ensure marginal coverage: the probability of coverage is averaged over both the training and test data, meaning that there might be substantial undercoverage within certain…

网络时间序列中的欧氏镜像与动态变化AI 解读

Analyzing changes in network evolution is central to statistical network inference. We consider a dynamic network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the evolution of latent vectors exhibits low-dimensional manifold structure under a suitable distance. This distance can be approximated by a measure of separation between the…

用于边界层速度预测的物理约束深度双储备池网络AI 解读

When a fluid flows over a solid surface, it creates a thin boundary layer where the flow velocity is influenced by the surface through viscosity, and can transition from laminar to turbulent at sufficiently high speeds. Understanding and forecasting the fluid dynamics under these conditions is one of the most challenging scientific problems in fluid dynamics. It is therefore of high interest to formulate models able to capture the nonlinear spatio-temporal velocity structure…

线性工具变量回归的稳健置换检验AI 解读

This paper develops permutation versions of identification-robust tests in linear instrumental variables (IV) regression. Unlike the existing randomization and rank-based tests in which independence between the instruments and the error terms is assumed, the permutation Anderson- Rubin (AR), Lagrange Multiplier (LM) and Conditional Likelihood Ratio (CLR) tests are asymptotically similar and robust to conditional heteroskedasticity under standard exclusion restriction i.e.…

异方差高维分位数回归的多重变点检测与定位AI 解读

Data heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this article, we consider potential structural changes and complicated tail distributions. There are various existing methods proposed to handle either structural changes or heteroscedasticity. However, it is difficult to handle them simultaneously. To overcome this limitation, we consider statistically and computationally efficient…

监督式动态主成分分析：多预测变量下的线性动态预测AI 解读

This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an effective way to bridge the gap between predictors and the target variable of interest by scaling and combining the predictors and their lagged values, resulting in an effective dynamic forecasting. Unlike the traditional diffusion-index approach, which does not learn the…

独立冲击的核最大似然统计识别及全球原油市场应用AI 解读

Independent component analysis has emerged as a promising approach for revealing structural relationships in multivariate dynamic systems, particularly in scenarios with limited knowledge of causal patterns. This article introduces a robust kernel-based maximum likelihood (KML) estimation method that accommodates the distributional characteristics of the structural sources of data variation. Our Monte Carlo study demonstrates the superior performance of the KML estimator…

融合不同信息来源的贝叶斯最优投资组合选择AI 解读

This article introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolio weights. This approach not only enables direct inference about the weights but also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and a market condition metric (MCM), exemplified by the CBOE Volatility Index (VIX) and Economic Policy…

多期动态投资组合选择：高维资产与收益可预测性AI 解读

Multiperiod portfolio choice is the central problem in active asset management. Multiperiod dynamic portfolios are notoriously difficult to solve, especially when there are hundreds of tradable assets as well as a large number of state variables. In this article, we develop a novel two-step methodology to solve the multiperiod dynamic portfolio choice problem with high dimensional assets in the presence of return predictability conditional on a large number of state…

零利率下限下的平滑影子利率动态 Nelson–Siegel 模型AI 解读

We propose a smooth shadow-rate version of the dynamic Nelson-Siegel (DNS) model to analyze the term structure of interest rates during a zero lower bound (ZLB) period.By relaxing the no-arbitrage restriction, our shadow-rate model becomes highly tractable with a closed-form yield curve expression.This permits the implementation of readily available DNS extensions such as allowing for time-varying parameters and the integration of macroeconomic variables.Using U.S. Treasury…

适应趋势与方差变化的贝叶斯变点分析及局部异常值评分AI 解读

We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We use a state-space approach to identify a dynamic signal in the presence of outliers and measurement error with stochastic volatility. We find that global state equation parameters are inadequate for most real applications and we include local parameters to…

局部平稳过程与二维高斯白噪声的渐近等价性AI 解读

We consider a general class of statistical experiments, in which an n-dimensional centered Gaussian random variable is observed and its covariance matrix is the parameter of interest. The covariance matrix is assumed to be well-approximable in a linear space of lower dimension Kn with eigenvalues uniformly bounded away from zero and infinity. We prove asymptotic equivalence of this experiment and a class of Kn-dimensional Gaussian models with informative expectation in Le…

随机几何对象时间序列的形状分析统计框架AI 解读

We introduce a new framework to analyze shape descriptors that capture the geometric features of an ensemble of point clouds. At the core of our approach is the point of view that the data arises as sampled recordings from a metric space-valued stochastic process, possibly of nonstationary nature, thereby integrating geometric data analysis into the realm of functional time series analysis. Our framework allows for natural incorporation of spatial-temporal dynamics,…

具有一般因子与解释变量的交互效应面板模型AI 解读

This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough,…

极端系统性风险预测中的估计风险AI 解读

Systemic risk measures have been shown to be predictive of financial crises and declines in real activity. Thus, forecasting them is of major importance in finance and economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (MES). It is based on first de-volatilizing the observations and, then, calculating systemic risk for the residuals using an estimator based on extreme value theory. We show the…

信念冲击与增长风险预期的影响AI 解读

ABSTRACT This paper revisits the question of how shocks to expectations of market participants can cause business cycle fluctuations. We use a vector autoregression to estimate dynamic causal effects of belief shocks which are extracted from nowcast errors about output growth. In a first step, we replicate and corroborate the findings of Enders, Kleemann, and Müller (2021). The second step computes nowcast errors about growth‐at‐risk at various quantiles. This involves both…

汇率、无抛补利率平价与时变 Fama 回归AI 解读

ABSTRACT This paper studies the forward premium puzzle, which signals a violation of the uncovered interest parity (UIP) hypothesis. We test this hypothesis with Fama‐style regressions with time‐varying parameters (TVPs) and stochastic volatility (SV) on six major currencies relative to the US dollar on monthly samples from 1993 to 2018. TVP‐SV regressions are also employed to examine the opposing predictions of the forward premium and excess volatility puzzles often found…

利用替代性高频数据追踪经济活动AI 解读

ABSTRACT Monthly macroeconomic series captured the sharp fluctuations during the COVID‐19 pandemic only with a lag. The use of alternative high‐frequency data is promising for crisis periods, but it is difficult to extract relevant business cycle information from them. We present a Bayesian mixed‐frequency dynamic factor model with stochastic volatility for measuring GDP growth at high‐frequency intervals. Its novelty is an additional state‐space block, in which the sparse…

利用联合特征函数对多元时间序列进行非参数分段AI 解读

Summary Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial…

连续时间局部平稳小波过程AI 解读

Abstract This article introduces the class of continuous-time locally stationary wavelet processes. Continuous-time models enable us to properly provide scale-based time series models for irregularly spaced observations for the first time, while also permitting a spectral representation of the process over a continuous range of scales. We derive results for both the theoretical setting, where we assume access to the entire process sample path, and a more practical one, which…

样本分割与时间序列拟合优度评估AI 解读

Summary A fundamental and often final step in time series modelling is to assess the quality of fit of a proposed model to the data. Since the underlying distribution of the innovations that generate a model is often not prescribed, goodness-of-fit tests typically take the form of testing the fitted residuals for serial independence. However, these fitted residuals are intrinsically dependent since they are based on the same parameter estimates, and thus standard tests of…

局部平稳时间序列的偏自相关函数：刻画、估计与推断AI 解读

SUMMARY For stationary time series, it is common to use plots of the partial autocorrelation function (PACF) or PACF-based tests to explore the temporal dependence structure of the process. To the best of our knowledge, analogues for nonstationary time series have not yet been fully developed. This article aims to fill this gap for locally stationary time series with short-range dependence. First, we characterize the PACF locally in the time domain and show that the jth PACF…