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时间序列回归的可行广义最小二乘AI 解读

We consider GLS and OLS estimation in a linear regression model with serially correlated errors, and we provide the following contributions. First, we clarify when OLS is consistent or not. Second, we give sufficient conditions such that GLS is valid without the assumption of exogenous regressors (uncorrelated with past innovations). Third, we devise a feasible GLS procedure valid whether or not the regressors are exogenous, and which achieves a MSE close to that of the…

交互固定效应面板数据模型与（相对）小 TAI 解读

This paper studies the estimation of a linear panel data model with interactive fixed effects. A transformation is introduced which, after having been applied, renders the least squares (LS) estimator of Bai (2009) consistent and asymptotically unbiased when n is large and T is fixed. This is termed the transformed least squares (TLS) estimator. Going further, these properties are shown to also carry over to the large n, large T setting, provided T /n → 0. This contrasts…

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…

高维线性因子定价模型的 Lq 范数 alpha 检验AI 解读

We consider testing zero pricing errors in high-dimensional linear factor pricing models. Existing methods are mainly based on either an $L_2$ statistic, which is effective under dense alternatives, or an $L_\infty$ statistic, which is powerful under very sparse alternatives. To bridge these two regimes, we develop a class of $L_q$-based tests for finite $q$, including the practically useful $L_4$ and $L_6$ cases. We show that larger $q$ leads to greater sensitivity to…

高维矩阵值时间序列的时空自回归模型AI 解读

Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily trading volume curve of one asset, and each row captures synchronized 5-minute volume intervals acrossmultiple assets. While traditional matrix time series focus mainly on temporal evolution, our approach incorporates both spatial and temporal dynamics, enabling simultaneous analysis of interactions…

具有动态异质溢出的矩阵值空间自回归AI 解读

We introduce a new time-varying parameter spatial matrix autoregressive model that integrates matrix-valued time series, heterogeneous spillover effects, outlier robustness, and time-varying parameters in one unified framework. The model allows for separate dynamic spatial spillover effects across both the row and column dimensions of the matrix-valued observations. Robustness can be introduced via a matrix Student’s t distribution. The model can still be estimated…

基于离散傅里叶变换检验交互固定效应面板模型的结构变化AI 解读

We propose a discrete Fourier transform (DFT)-based test for detecting structural changes in panel data models with interactive fixed effects (IFE). When structural changes are present but ignored, the conventional asymptotic distribution of the least squares (LS) estimator of Bai (2009) may no longer provide reliable inference, and the resulting residuals contain signals of parameter instability. Our test exploits this feature by comparing the DFT of weighted residuals to…

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…

混合平稳与非平稳变量的主成分分析AI 解读

This paper develops a procedure for uncovering the common cyclical factors that drive a mix of stationary and nonstationary variables. The method does not require knowing which variables are nonstationary or the nature of the nonstationarity. An application to the FRED-MD macroeconomic dataset demonstrates that the approach offers similar benefits to those of traditional principal component analysis with some added advantages.

动态系统性风险测度的多元推断AI 解读

This paper provides statistical inference for marginal expected shortfall (MES) and delta conditional value-at-risk (ΔCoVaR) measures, which are semiparametrically estimated by a two-step procedure in a multivariate GARCH-type framework. We establish the asymptotic properties of corresponding estimators and illustrate how the estimation uncertainty can be decomposed into dynamic univariate marginal and time-varying dependence components. Our methodology reveals good finite…

时变向量自回归的模型平均AI 解读

This paper proposes a novel time-varying model averaging (TVMA) approach to enhancing forecast accuracy for multivariate time series subject to structural changes. The TVMA method averages predictions from a set of time-varying vector autoregressive models using optimal time-varying combination weights selected by minimizing a penalized local criterion. This allows the relative importance of different models to adaptively evolve over time in response to structural shifts. We…

由因果 GARCH 波动驱动的非因果自回归过程AI 解读

This paper studies the introduction of causal conditional heteroskedasticity in noncausal autoregressive (AR) models. We demonstrate that, in this framework, large shocks to the innovation process driving the GARCH error term of a noncausal AR model result in heightened volatility immediately before and after a bubble crash. The non-coincidence of the σ-fields generated by past observations and past values of the GARCH process makes statistical inference nonstandard. In…

MCA：面向条件众数的高维众数成分分析AI 解读

The modal factor model represents a novel statistical framework for dimension reduction in high dimensional panel data analysis. It is specifically designed to capture modal factors which exert a significant influence on the conditional mode of the distribution of the observables. Statistical inference for this model is developed based on a newly proposed modal component analysis, where modal factors and their corresponding loadings are estimated through maximizing a…

任意带宽选择下的预测模型权重形状优化AI 解读

Parameter instability has been widely documented to lead to poor predictive accuracy for economic forecasting models. Adaptive schemes for data weighting, such as rolling-window estimation, only involve a single tuning parameter (akin to a bandwidth) and have been widely used to address this issue. The importance of the shape of the weighting scheme (kernel) has received far less attention in the forecasting literature. In this paper we show that in practice there can be a…

未知形式异方差下非基本 ARMA 模型的时域估计AI 解读

This paper considers time domain estimation of possibly non-fundamental (that is, non-causal and/or non-invertible) non-Gaussian linear ARMA models with martingale difference innovations that may display conditional heteroskedasticity of unknown form. Instead of explicitly parametrizing the underlying volatility process (the higher order dependence) and employing maximum likelihood procedures, we propose a time domain minimum distance objective function based on innovations…

奇异向量自回归AI 解读

This paper develops methods for the empirical analysis of singular processes, particularly in macroeconomics. A strong rationale, a well-developed theoretical framework and, as we show, empirical support exist for multivariate time series with a singular spectral density. A singular spectral density is consistent with economic theory underlying, for example, DSGE models in which the number of variables is greater than the number of structural shocks. This assumption…

非对称风险下的宏观审慎政策：贝叶斯结构分位数 VAR 方法AI 解读

Macro-prudential policymakers face a risk-management problem: they must assess medium-term downside risks to the real economy stemming from financial imbalances and the associated threat of future financial crises, while accounting for the cost of policy interventions in terms of foregone upside potential. This trade-off between limiting downside risks and preserving upside growth is inherently asymmetric and depends on the distribution of future macroeconomic outcomes. We…

基于方差分解检验高频网络连通性的差异AI 解读

We develop a Bayesian Wald-type test for detecting temporal differences in connectedness from variance decompositions, and establish the asymptotic normality of impulse response functions and variance decomposition matrices at fixed local time points. The resulting test statistic is asymptotically χ 2 distributed under the null hypothesis and performs well in Monte Carlo simulations. Applying our methodology to high-frequency options data on sectoral exchange-traded funds…

非中心化随机波动率下结构向量自回归的部分识别AI 解读

We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails—unlike the common centred parameterization. This feature makes it well suited for…

疫情后大型贝叶斯 VAR 的贝叶斯模型比较AI 解读

There is an increasing interest in applying variational Bayes techniques to estimating large Bayesian vector autoregressions (VARs) with stochastic volatility. However, less attention has been paid to the development of appropriate tools for comparing these high-dimensional models, especially among those designed to address COVID-19 outliers. This paper develops a marginal likelihood estimator that combines importance sampling and variational approximation for comparing…

结构突变、模型不确定性与因子选择AI 解读

This paper addresses the non-standard problem of detecting multiple structural breaks when the data-generating model in each regime is uncertain, with an application to factor selection in empirical asset pricing. Detection is based on the marginal likelihood of break points, obtained by a novel integration over all possible pairings of models across regimes, from all possible models within regimes. The optimal break points maximize this marginal likelihood. Applying this…

经济增长建模的贝叶斯方法：变量选择与截面依赖AI 解读

This paper introduces a Bayesian framework designed to address two critical econometric challenges in empirical growth studies: variable selection and cross-sectional dependence. We first propose a growth regression model that incorporates cross-sectional dependence through a multi-factor structure and accounts for variable selection with sparse coefficients. We then introduce a new Bayesian Adaptive LASSO estimator specifically designed for this model, capable of performing…

大型贝叶斯矩阵自回归模型AI 解读

High-dimensional matrix-valued time-series are increasingly common in economics and finance. Prominent examples include large cross-region panels and dynamic economic networks. As the dimensions of the matrix grow, conventional approaches based on vector autoregressions—implemented by vectoring the matrix-valued data—become computationally infeasible. We introduce a class of large Bayesian matrix autoregressions (BMARs) that can accommodate time-varying volatility,…

混合矩阵值自回归模型AI 解读

Time series of matrix-valued data are increasingly available in various areas including economics, finance, social science, among others. These data may shed light on the inter-dynamical relationships between two sets of attributes, for instance, countries and economic indices. The matrix autoregressive (MAR) model provides a parsimonious approach for analyzing such data. However, the MAR model, being a linear model with parametric constraints, cannot capture the nonlinear…