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数据丰富环境中的时变完全子集平均AI 解读

This study proposes two novel time-varying model-averaging methods for time-varying parameter regression models. When the number of predictors is small, we propose a novel time-varying complete subset-averaging (TVCSA) procedure, where the optimal time-varying subset size is obtained by minimizing the local leave- h -out cross-validation criterion. The TVCSA method is asymptotically optimal for achieving the lowest possible local mean squared error. When the number of…

相关时间序列的预测AI 解读

ABSTRACT A collection of time series are “related” if they follow similar stochastic processes and/or they are statistically dependent. This paper proposes a related time series (RTS) forecasting model that exploits these relationships. The model's foundation is a set of univariate Gaussian autoregressions, one for each series, which are then augmented to incorporate stochastic volatility, heavy‐tailed innovations, additive outliers, time‐varying parameters and common…

分布向量自回归：刻画宏观与金融依赖AI 解读

ABSTRACT This paper extends the vector autoregression framework by introducing a flexible distributional regression that models multivariate time series without imposing restrictive parametric distribution assumptions. We develop a distributional impulse response function that captures the future effect of distributional disturbances within the system, providing a more detailed view of dynamic heterogeneity. We propose a straightforward estimation method and establish its…

利用文本数据的因子分位数预测AI 解读

ABSTRACT Words matter for predicting tail risks. We propose an attention mechanism embedded in a quantile factor model, yielding information that is quantile‐specific, target‐specific, and horizon‐specific. We establish new asymptotic results and show empirically that targeted textual data improve quantile forecasts of exchange‐rate returns and industrial production growth relative to strong benchmarks and other conditional‐quantile models. Bigrams and trigrams drive these…

利用混频数据改进密度预测：贝叶斯方法AI 解读

We construct a modeling framework that allows for the flexible contribution of intraday information to predict the conditional distribution of asset returns. A flexible spline specification is proposed to capture the contribution of intraday returns inside GARCH and stochastic volatility specifications for daily returns. The “focused Bayesian” prediction framework is adopted to allow the intraday contribution to the simpler GARCH specification to be trained according to the…

序贯模型置信集AI 解读

Abstract In most prediction and estimation situations, scientists consider various statistical models for the same problem, and naturally want to select amongst the best. Hansen et al. [(2011). The model confidence set. Econometrica: Journal of the Econometric Society, 79(2), 453–497] provide a powerful solution to this problem by the so-called model confidence set, a subset of the original set of available models that contains the best models with a given level of…

高维预测回归中的 LASSO 推断AI 解读

LASSO inflicts shrinkage bias on estimated coefficients, which undermines asymptotic normality and invalidates standard inferential procedures based on the t -statistic. Given cross sectional data, the desparsified LASSO has emerged as a well-known remedy for correcting the shrinkage bias. In the context of high dimensional predictive regression, the desparsified LASSO faces an additional challenge: the Stambaugh bias arising from nonstationary regressors modeled as local…

集合识别 SVAR 预测误差方差分解的估计与推断AI 解读

We study estimation and inference of the Forecast Error Variance Decomposition (FEVD) in Structural Vector Autoregressions (SVARs) that are set-identified by internal and external (proxy) restrictions. The FEVD measures the importance of shocks for macroeconomic fluctuations and is thus key to business cycle analysis. We deliver three practitioner-oriented results. First, we characterize the endpoints of the FEVD as the extreme eigenvalues of a symmetric reduced-form matrix,…

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…

增长风险的稳健计量方法AI 解读

The Growth-at-Risk (GaR) framework has garnered attention in recent econometric literature, yet current approaches implicitly assume a constant Pareto exponent. We introduce novel and robust econometrics to estimate the tails of GaR based on a rigorous theoretical framework and establish validity and effectiveness. Simulations demonstrate consistent outperformance relative to existing alternatives in terms of predictive accuracy. We perform a long-term GaR analysis that…

若干长期宏观预测的实证评价AI 解读

We use long-run annual cross-country data for 10 macroeconomic variables to evaluate the long-horizon forecast distributions of six forecasting models. The variables we use range from ones having little serial correlation to ones having persistence consistent with unit roots. Our forecasting models include simple time series models and frequency domain models developed in Müller and Watson (2016). For plausibly stationary variables, an AR(1) model and a frequency domain…

因子增广矩阵回归AI 解读

We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, using techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm…

严格适当评分规则的局部化AI 解读

When comparing predictive distributions, forecasters are typically not equally interested in all regions of the outcome space. To address the demand for focused forecast evaluation, we propose a procedure to transform strictly proper scoring rules into their localized counterparts while preserving the score divergence and strict propriety. This is accomplished by applying the original scoring rule to a censored distribution. Our procedure nests the censored likelihood score…

随机游走预测平稳过程的样本平均误差较小AI 解读

We study the use of a misspecified overdifferenced model to forecast the level of a stationary scalar time series. Let xt be the series, and let bias be the sample average of a series of forecast errors. Then, the bias of forecasts of xt generated by a misspecified overdifferenced ARMA model for Δxt will tend to be smaller in magnitude than the bias of forecasts of xt generated by a correctly specified model for xt. Formally, let P be the number of forecasts. The bias from…

高维时间序列预测回归中有效协整秩的确定AI 解读

This article proposes a new approach to identifying the effective cointegration rank in high-dimensional unit-root (HDUR) time series from a prediction perspective using reduced-rank regression. For a HDUR process xt∈RN and a stationary series yt∈Rp of interest, our goal is to predict future values of yt using xt and lagged values of yt. The proposed framework consists of a two-step estimation procedure. First, the Principal Component Analysis is used to identify all…

不稳定环境中的预测选择AI 解读

This paper leverages the time-series properties of forecast loss differences for out-of-sample forecast selection. Our framework predicts the conditional distribution of future loss differences while accommodating for time-contingent unstable forecasting environments. We establish distributional theory to quantify the sampling uncertainty of our predictions, enabling the development of advanced selection rules. Through simulations and an empirical application to inflation…

预期分歧的因子结构AI 解读

We estimate a Bayesian three-dimensional dynamic factor model on the individual forecasts in the Survey of Professional Forecasters. The factors extract the most important dimensions along which disagreement comoves across variables. We interpret our results through a generic dispersed information model. The two most important factors in the data describe disagreement about aggregate supply and demand, respectively. Up until the Great Moderation, supply disagreement was…

时变参数 VAR 中与经济理论一致的收缩AI 解读

This article introduces a theory coherent shrinkage prior for Time-Varying Parameter VARs (TVP-VARs). The prior centers the time-varying parameters on a path implied a priori by an underlying economic theory, chosen to describe the dynamics of the macroeconomic variables in system. Leveraging information from conventional economic theory using this prior significantly improves inference precision and forecast accuracy compared to the standard TVP-VAR. In an application, I…

包络矩阵自回归模型AI 解读

Matrix-valued data is commonly collected over time in many scientific fields. However, existing methods for handling such data are limited and often suffer from overparametrization. In response, Chen et al. (2021) introduced the matrix autoregressive (MAR) model as an alternative to traditional time series analysis, which relies on vectorization and vector autoregression frameworks. By preserving the original structure of matrices, the MAR model avoids the loss of valuable…

GARCH-MIDAS 模型的自适应组 LASSOAI 解读

This article investigates the variable selection problem in the generalized autoregressive conditional heteroscedasticity-mixed-data sampling (GARCH-MIDAS) model of Engle, Ghysels, and Sohn (Citation2013), and establishes the corresponding theoretical properties along with a feasible computational algorithm. The proposed method is based on a penalized log-partial likelihood with an adaptively group-weighted L2 penalty on the long-term component coefficients. The variable…

半球神经网络：从被动响应到前瞻性波动建模AI 解读

ABSTRACT We revisit maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network, which accommodates various forms of time variation in the error variance. Second, we introduce a volatility emphasis constraint that…

资产收益与期权需求的条件密度比模型估计AI 解读

Option-implied risk-neutral densities are widely used for constructing forward-looking risk measures. Meanwhile, risk aversion introduces a multiplicative pricing kernel between the risk-neutral and true conditional densities of the underlying asset’s return. This paper proposes a simple local estimator of the pricing kernel based on inverse density weighting. We characterize the asymptotic bias and variance of the estimator and its multiplicatively corrected density…

张量数据的扩散指数预测AI 解读

In this paper, we consider diffusion index forecasting with both tensor and non-tensor predictors, where the tensor structure is preserved with a Canonical Polyadic (CP) tensor factor model. When the number of non-tensor predictors is small, we study the asymptotic properties of the least squares estimator in this tensor factor-augmented regression, allowing for factors with different strengths. We derive an analytical formula for prediction intervals that accounts for the…

时间变换扩散模型中已实现方差估计的高效抽样AI 解读

This paper analyzes the benefits of sampling intraday returns in intrinsic time for the realized variance (RV) estimator. We theoretically show in finite samples that depending on the permitted sampling information, the RV estimator is most efficient under either hitting time sampling that samples whenever the price changes by a pre-determined threshold, or under the new concept of realized business time that samples according to a combination of observed trades and…