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动态分位数的函数系数 VAR 模型及非参数金融网络构建AI 解读

In this article, we investigate a functional coefficient vector autoregressive model for conditional quantiles, in which the interdependences among tail risks such as Value-at-Risk are allowed to vary smoothly with a variable of general economy. Methodologically, we develop an easy-to-implement two-stage procedure to estimate functionals in the dynamic network system based on the deep learning method of neural networks and the local linear smoothing technique. We establish…

时间聚合对 MIDAS 回归的影响AI 解读

Time series used in practice are often temporal aggregates. A MIDAS regression model may be fitted wrongly assuming the independent variables are not aggregated when in fact they are. We derive the correct model’s specification under temporal aggregation of the independent variables, thus identifying the correct number of dynamic terms and the order of the MA component. We also show that these depend on the frequency of the variables and the aggregation order used. As a…

估计美式期权隐含的状态价格密度AI 解读

We propose a new method to estimate state price densities implicit in American-style options. The method involves estimating the parameters of a Gauss-Hermite series expansion and solving a sequence of recursive equations for the early exercise premium. The resulting estimator can capture sudden shifts in density that may occur during financial crises or in response to significant policy events. It also provides an estimate of the early exercise premium that is of…

高维函数时间序列的因子建模AI 解读

Many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables p diverges as the number of serially dependent observations n increases. In this paper, we present a novel functional factor model for high-dimensional functional time series that maintains and makes use of the functional and dynamic structure to achieve great dimension reduction and find the latent factor structure. To estimate the…

具有不可观测分组异质性的面板自回归模型统一推断AI 解读

This paper considers unified estimation and inference in panel autoregressive (PAR) models. The PAR coefficients are assumed to contain a latent group structure that allows the degree of persistence for each time series to be heterogeneous and unknown. We propose a novel penalized weighted least squares approach to simultaneously identify the unknown group membership and consistently estimate the PAR coefficients, regardless of whether the underlying PAR process is…

共同成分结构 VARAI 解读

Structural VAR models (SVAR) produce results that can vary dramatically with the choice of variables, because information is deficient. We argue that if the variables of interest belong to a High-Dimensional Factor Model and are replaced in the SVAR by their common components, the information issue finds a solution, provided that the number of common components is larger than the number of structural shocks, so that the SVAR is dynamically singular. This is the Common…

气候敏感度是否因地区而异？变系数方法AI 解读

Global mean surface temperature has been increasing in response to growing greenhouse gas concentrations (IPCC, 2021). While Earth is getting warmer overall, regions that have different local geographical features experience unequal increases in temperature. In this paper, we develop a varying-coefficient dynamic panel data model and use it to measure local climate sensitivity, defined as the increase in temperature in a given location following a doubling of CO2…

隐含波动率预测中的神经切线核：非线性函数自回归方法AI 解读

Forecasting implied volatility across different levels of moneyness and maturity is crucial yet challenging due to the high dimensionality of the Implied Volatility Surface (IVS) and the nonlinearity that characterizes its temporal dependence. We adopt a Nonlinear Functional Autoregressive (NFAR) framework to a sequence of IVS and employ neural networks that admit a Neural Tangent Kernel (NTK) parameterization to capture nonlinear interactions between surfaces. We illustrate…

高斯自回归中充分统计量的有限样本密度及相关检验AI 解读

A first-order Gaussian autoregressive model is considered. The exact finite-sample joint density of the minimal sufficient statistic is derived, for any value of the autoregressive parameter. This allows us to derive explicitly the exact density of the autocorrelation coefficient and its Studentized t-ratio, whose densities were available only in the asymptotic case and not for all values of the parameter and the statistic. This article also demonstrates how to solve a…

具有网络关联固定效应的动态面板分位数回归AI 解读

This paper introduces a dynamic panel data quantile regression model with network-linked fixed effects, named DQR-NFE, in which unobserved individual heterogeneity is structured through an underlying network. The corresponding estimator is derived by incorporating a quantile network cohesion (QNC) penalty into the dynamic panel quantile regression framework. This penalty encourages connected units within the network to exhibit similar conditional quantiles, with a…

局部投影的 GLS 估计：稳健性与效率的权衡AI 解读

Local projections (LPs) are widely used for estimating impulse responses (IRs) as they are considered more robust to model misspecification than forward-iterated IRs from dynamic models such as VARs. However, this robustness comes at the cost of higher variance, particularly at longer horizons. To mitigate this trade-off, several GLS transformations of LPs have been proposed. This paper analyzes two broad strands of GLS-type LP estimators: those that condition on residuals…

广义共同相关效应估计AI 解读

The widely-used common correlated effects (CCE) estimator, pioneered by Pesaran (2006), is computed using least squares applied to auxiliary regressions where the observed regressors are augmented with cross-sectional averages of the dependent variable and regressors. However, the CCE estimator requires a crucial rank condition and becomes inconsistent when this condition is violated and the factor loadings of the x- and y -equations are correlated, causing an endogeneity…

CP 张量因子模型的估计与推断AI 解读

High-dimensional tensor-valued data have recently gained attention from researchers in economics and finance. We consider the estimation and inference of high-dimensional tensor factor models, where each dimension of the tensor diverges. Our focus is on a factor model that admits CP-type tensor decomposition, which allows for non-orthogonal loading vectors. Based on the contemporary covariance matrix, we propose an iterative simultaneous projection estimation method. Our…

局部平稳条件下时变因子模型的推断AI 解读

This paper considers estimation of and testing for a class of locally stationary time series factor models with evolutionary dynamics, where the entries and dimension of the factor loading matrix are allowed to vary with time while the factors and idiosyncratic components are locally stationary. We propose an adaptive sieve estimator for the span of the time-varying loading matrix of a locally stationary factor process. A uniformly consistent estimator of the effective…

未观测成分模型、近似滤波与动态自适应混合模型AI 解读

State estimation in unobserved component models with parameter uncertainty is traditionally performed through approximate filters, where Gaussian distributions with given moments are employed to replace otherwise intractable conditional densities. This paper re-examines signal-plus-noise models where parameter uncertainty is induced by a latent variable that may assume a fixed number of states. First, it is shown that, for these models, the approximate filters commonly…

非平稳与周期时间序列的函数型半参数建模AI 解读

Inspired by a real data example illustrating the periodicity in hog price data, this study aims to analyze time series that exhibit an unknown period alongside complex covariate effects. To address these complexities and effectively handle the data structures, we incorporate the partial functional varying-coefficient single-index model into the classical time series decomposition model. We propose a two-stage estimation procedure designed to accurately estimate the unknown…

利用快速模型置信集开展大规模模型比较AI 解读

The paper proposes a new algorithm for finding the confidence set of a collection of forecasts or prediction models. Existing numerical implementations use an elimination approach, where one starts with the full collection of models and successively eliminates the worst performing until the null of equal predictive ability is no longer rejected at a given confidence level. The intuition behind the proposed implementation lies in reversing the process, i.e. starting with a…

高频委托报价中的跳跃检测AI 解读

We propose methods to infer jumps of a semi-martingale, which describes long-term price dynamics, based on discrete, noisy, high-frequency observations. Different to the classical model of additive, centered market microstructure noise, we consider one-sided microstructure noise for order prices in a limit order book. We develop methods to estimate, locate and test for jumps using local minima of best ask quotes. We provide a local jump test and show that we can consistently…

检验预测效率的工具变量方法AI 解读

ABSTRACT We study a specific form of forecast efficiency that requires forecast errors to be unpredictable from forecast revisions. One approach aggregates forecasts and estimates an aggregated efficiency regression, while another estimates the relationship by running separate regressions for each individual and then aggregating. We demonstrate that both estimators can be asymptotically biased in the presence of public noise. To address these biases, we propose instrumenting…

不确定的短期约束与统计识别的结构向量自回归AI 解读

ABSTRACT This study proposes a combination of a statistical identification approach with potentially invalid short‐run zero restrictions. The estimator shrinks towards imposed restrictions and stops shrinkage when the data provide evidence against a restriction. We demonstrate that incorporating valid restrictions through the shrinkage approach enhances the efficiency of the statistically identified estimator, and the impact of invalid restrictions vanishes as the sample…

局部投影的双重稳健性与 VAR 推断的隐患AI 解读

We consider impulse response inference in a locally misspecified vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected with probability approaching 1. This result follows from a “double robustness” property analogous to that of popular partially linear regression estimators. By contrast, the conventional VAR confidence interval with…

信念更新中的推断与预测差距AI 解读

Evidence from the laboratory and the field has uncovered both underreaction and overreaction to new information. We provide new experimental evidence on the underlying mechanisms of under‐ and overreaction by comparing how people make inferences and revise forecasts in the same information environment. Participants underreact to signals when inferring about underlying states, but overreact to the same signals when revising forecasts about future outcomes—a phenomenon we term…

线性面板模型中固定效应的最优收缩估计AI 解读

Shrinkage methods are frequently used to improve the precision of least squares estimators of fixed effects. However, widely used shrinkage estimators guarantee improved precision only under strong distributional assumptions. I develop an estimator for the fixed effects that obtains the best possible mean squared error within a class of shrinkage estimators. This class includes conventional shrinkage estimators and the optimality does not require distributional assumptions.…

带多重符号与排序限制的大型结构 VARAI 解读

Large VARs are increasingly used in structural analysis as a unified framework to study the impacts of multiple structural shocks simultaneously. However, the concurrent identification of multiple shocks using sign and ranking restrictions poses significant practical challenges to the point where existing algorithms cannot be used with such large VARs. To address this, we introduce a new numerically efficient algorithm that facilitates the estimation of impulse responses and…