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分数高斯过程的复合似然推断与序贯最优子集选择AI 解读

The composite likelihood method reduces the computational cost of parameter estimation in time series by considering several subsets of observations instead of all observations at once. The asymptotic properties of this method are related to the Godambe information, an extension of the Fisher information that accounts for the dependence between subsets of observations. We aim to apply this method to linear Gaussian models, in particular fractional Brownian motion and…

因子载荷的解释与检验AI 解读

Dynamic Factor Models (DFMs) are popular to reduce dimensionality being customary in the empirical analysis of large systems of macroeconomic and/or financial variables. In this context, the common underlying factors and their loadings are often extracted using Principal Components (PC), which are consistent and asymptotically normal under very general conditions. Consequently, inference on the factor loadings, which is crucial for the correct interpretation of the…

分组数据下动态收入分布的贝叶斯状态空间建模与反事实分析AI 解读

Grouped income data contain only limited information about the evolution of income distributions over time. This paper develops a Bayesian state-space model for the generalized beta distribution of the second kind (GB2) to estimate dynamic income distributions using repeated grouped income data. By borrowing information across adjacent periods through the latent GB2 parameters, the proposed framework improves estimation precision relative to independent cross-sectional…

马尔可夫交替实验中经校准的期限加权局部投影设计AI 解读

We study temporal assignment design for Markov switchback experiments when the reported object is a dynamic local-projection target. We develop a calibrated selector that chooses the feasible persistence minimizing the covariance, HAC, residual-bootstrap, or realized-schedule risk of the estimator and reporting object specified before the experiment. A balanced homoskedastic Markov benchmark yields a closed form because the lagged-assignment information matrix is…

非参数时间序列回归中跳跃的高维推断AI 解读

We study simultaneous inference on jumps in the conditional mean functions of a high-dimensional collection of heterogeneous nonparametric time series, where the number of series may exceed the sample size and the data may exhibit strong cross-sectional dependence. The jump depends on one specific covariate, and we allow the regression function to vary with additional latent variables. We propose two uniform tests: one for the existence of jumps and one for their homogeneity…

无分位数交叉的分位数向量自回归AI 解读

This paper considers estimation and model selection of quantile vector autoregression (QVAR). Conventional quantile regression often yields undesirable crossing quantile curves, violating the monotonicity of quantiles. To address this issue, we propose a simplex quantile vector autoregression (SQVAR) framework, which transforms the autoregressive (AR) structure of the original QVAR model into a simplex, ensuring that the estimated quantile curves remain monotonic across all…

高维函数型时间序列预测的可解释模型AI 解读

We study the modeling and forecasting of high-dimensional functional time series, which can be temporally dependent and cross-sectionally correlated. Central to our implementation is a functional analysis of variance by decomposing high-dimensional functional time series, such as subnational age- and sex-specific mortality observed over years, into two distinct components: a deterministic mean structure and a residual process varying over time. Unlike purely statistical…

斯大林是否必要？苏联经济增长的反事实证据AI 解读

This article evaluates whether the exceptionally rapid growth of the Russian and Soviet economy after 1928 requires a specifically Stalinist explanation. The analysis treats this historical question as a sequence of increasingly demanding counterfactual comparisons rather than a binary comparison between observed growth and a single alternative. Long run output series from the Global Macro Database and the Maddison Project are combined with synthetic control, augmented…

未知聚类下的组内等预测能力检验AI 解读

We develop tests of clustered equal predictive ability (C-EPA) in panels where the clusters are unknown and estimated by the Panel Kmeans algorithm. To address the challenge of testing hypotheses that depend on data-driven clusters, we adopt a selective conditional inference framework. Specifically, we first derive a Wald-type test for pairwise equality and show that the limiting distribution of its square root conditional on the estimated clusters is that of a truncated $χ$…

遍历自回归中的函数依赖与同步耦合AI 解读

Functional dependence measures have become an important tool in the analysis of nonlinear time series and are typically formulated with respect to a given innovation representation of the process. This note points out that the probability space on which such representations yield the expected memory loss properties may not always coincide with the natural dynamical probability space of the model. We exhibit classes of uniformly ergodic autoregressive processes for which the…

平稳随机动力系统中因果效应符号的可识别性AI 解读

We study identifiability in continuous-time linear stationary stochastic differential equations with a known causal structure. Unlike existing approaches, we relax the assumption of a known diffusion matrix, thereby respecting the model's intrinsic scale invariance. Therefore, rather than recovering drift coefficients themselves, we introduce edge-sign identifiability: for a given causal structure, we ask whether the sign of a given drift entry is uniquely determined across…

非高斯输入下 Volterra 互相关识别的闭式偏度惩罚AI 解读

The monomial parameterization of finite-memory Volterra identification is ill-conditioned under non-Gaussian input, and the Wiener--Hermite expansion removes this ill-conditioning only for Gaussian white-noise input. We construct the distribution-matched Volterra--Wiener--Kunchenko (VWK) basis by oriented Gram--Schmidt orthogonalization of monomials in $L^2(P)$ and use it as an arbitrary-polynomial-chaos coordinate system for finite-memory Volterra identification from data,…

布伦特原油与尼泊尔股市的动态关系：计量与可解释机器学习AI 解读

This study examines the dynamic relationship between the global oil prices and Nepal Stock Exchange (NEPSE) using an integrated approach which combines traditional econometric techniques with machine learning and explainable AI techniques. For this, Daily data of International Oil prices and NEPSE index is analyzed from approximately thirteen years (June 2013 to June 2026) using Granger causality, EGARCH(1,1), and DCC-GARCH models to examine different properties like…

中断时间序列中具有自回归误差的贝塔回归AI 解读

Interrupted time series analyses (ITSA) of proportion and rate outcomes are frequently estimated using ordinary least squares regression despite the bounded nature of these outcomes. When methods appropriate for bounded outcomes are used, the standard approach is a quasi-likelihood generalized linear model (GLM) with heteroskedasticity- and autocorrelation-consistent (HAC) standard errors. However, no existing estimator jointly models the beta-distributed conditional density…

圆柱型数据的无限隐马尔可夫模型AI 解读

We propose an infinite hidden Markov model for cylindrical time series with von Mises-Gamma emissions. Posterior inference is performed using a beam sampler combining conjugate updates and approximate sampling schemes. Simulation studies and two real data applications demonstrate the effectiveness of the proposed methodology.

面向时间序列的双重机器学习AI 解读

We modify the Double Machine Learning estimator to broaden its applicability to macroeconomic time-series settings. A deterministic cross-fitting step, termed Reverse Cross-Fitting, leverages the time-reversibility of stationary series to improve sample utilization and efficiency. We detail and prove the conditions under which the estimator is asymptotically valid. We then demonstrate, through simulations, that its performance remains valid in realistic finite samples and is…

变分贝叶斯变量选择后的潜在结构恢复：部分探索性因子分析的拟合评估与因子数选择AI 解读

In partially exploratory factor analysis (PEFA), the loading structure and factor numbers are weakly specified. The regularized variational approximation for partially confirmatory factor analysis (PCFA VA) recovers this structure via Bayesian variable selection, using spike and slab priors to assign inclusion probabilities to unspecified loadings. This research introduces a post selection assessment framework for this approach. We convert converged solutions into covariance…

谱密度矩阵的多元贝叶斯 P 样条估计及 LISA TDI 噪声应用AI 解读

We present a Bayesian P-spline method for estimating the frequency-dependent cross-spectral density matrix of stationary multivariate time series. The inverse spectral matrix is parametrised through its frequency-varying Cholesky decomposition, which guarantees Hermitian positive definiteness at every frequency. Each real log-diagonal entry and each real and imaginary off-diagonal entry is given an independent penalised B-spline prior that controls smoothness. Inference uses…

tsbootstrap：时间序列的分布无关不确定性量化与保形预测AI 解读

Finance, sensing, and demand streams violate the exchangeability that IID conformal prediction and the IID bootstrap assume, and existing libraries implement either a general resampling engine or conformal calibration without the other. tsbootstrap provides block, residual, sieve, and wild resampling, classical bootstrap confidence intervals, and adaptive conformal calibrators (EnbPI, ACI, NexCP, AgACI) through a single typed API in which a specification object selects each…

因子增强的机器学习面板回归AI 解读

This paper develops the asymptotic theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks. We consider a factor-augmented sparse-group LASSO estimator that combines MIDAS aggregation with latent factors. The estimator can take advantage of the mixed-frequency group structure in the time-series dimension. Theory shows that it can outperform the standard LASSO estimator both for prediction and estimation…

结构向量自回归的全局识别AI 解读

In a landmark contribution to the structural vector autoregression (SVARs) literature, Rubio-Ramirez, Waggoner, and Zha (2010, `Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,' Review of Economic Studies) shows a necessary and sufficient condition for equality restrictions to globally identify the structural parameters of a SVAR. The simplest form of the necessary and sufficient condition shown in Theorem 7 of Rubio-Ramirez et al…

从纪录值生成随机轨迹：以冰碛记录重建冰川前缘位置波动AI 解读

The record values theory study elements of a time series that exceed all previous observations, which are of particular interest in fields such as sports or climate science. In this paper, we propose a statistical method based on the construction of a Brownian stochastic simulator to reconstruct entire time series solely from such record values, even in a non-stationary case. We then implement a procedure, which can be compared to a Neural-Based Inference (NBI) procedure, to…

矩阵值时间序列共同变动的分解：拟结构降秩方法AI 解读

A pseudo-structural framework is proposed for analyzing contemporaneous co-movements in stationary reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector autoregressive (VAR) models that discard the matrix structure, the formulation preserves it, enabling a decomposition of co-movements into three interpretable components: row-specific, column-specific, and joint (row--column) interactions across the matrix-valued time series. The estimator admits…

平稳化变换真的改善时间序列预测吗？受控实验评估AI 解读

Stationarity transformations, such as differencing, are a common preprocessing step in forecasting, motivated by the idea that modifying a series to achieve stationarity improves accuracy. Whether this is true, and for which processes, has rarely been evaluated in controlled experiments. We study the decision to transform as the object of inquiry. We cross eighteen synthetic data-generating processes, most of them stochastic-trend processes spanning exact and near unit…