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结合极端天气协变量与时空条件 Copula 的作物产量概率预测AI 解读

We introduce a novel forecasting model for crop yields that explicitly accounts for spatio-temporal dependence and the influence of extreme weather and climatic events. Our approach combines Bayesian Structural Time Series for modeling marginal crop yields, ensuring a more robust quantification of uncertainty given the typically short historical records. To capture dynamic dependencies between regions, we develop a time-varying conditional copula model, where the copula…

利用 Google Trends 构建私人消费趋势指数AI 解读

In recent years, the use of databases that analyze trends, sentiments or news to make economic projections or create indicators has gained significant popularity, particularly with the Google Trends platform. This article explores the potential of Google search data to develop a new index that improves economic forecasts, with a particular focus on one of the key components of economic activity: private consumption (64\% of GDP in Peru). By selecting and estimating…

劳动需求预测：利用深度学习预测JOLTS职位空缺AI 解读

This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job Openings and Labor Turnover Survey data in the United States. Drawing on multiple economic indicators from various sources, the data are fed directly into LSTM model to predict JOLT job openings in subsequent periods. The performance of the LSTM model is compared with conventional autoregressive approaches, including ARIMA, SARIMA, and Holt-Winters. Findings suggest that the LSTM…

贸易政策对亚洲制造业中心的时空影响：贝叶斯全球 VAR 分析AI 解读

A novel spatiotemporal framework using diverse econometric approaches is proposed in this research to analyze relationships among eight economy-wide variables in varying market conditions. Employing Vector Autoregression (VAR) and Granger causality, we explore trade policy effects on emerging manufacturing hubs in China, India, Malaysia, Singapore, and Vietnam. A Bayesian Global Vector Autoregression (BGVAR) model also assesses interaction of cross unit and perform…

基于市场分类与波动率的动态投资策略：机器学习方法AI 解读

This study introduces a dynamic investment framework to enhance portfolio management in volatile markets, offering clear advantages over traditional static strategies. Evaluates four conventional approaches : equal weighted, minimum variance, maximum diversification, and equal risk contribution under dynamic conditions. Using K means clustering, the market is segmented into ten volatility-based states, with transitions forecasted by a Bayesian Markov switching model…

极短层级时间序列预测协调的迭代迹最小化方法AI 解读

Time series often appear in an additive hierarchical structure. In such cases, time series on higher levels are the sums of their subordinate time series. This hierarchical structure places a natural constraint on forecasts. However, univariate forecasting techniques are incapable of ensuring this forecast coherence. An obvious solution is to forecast only bottom time series and obtain higher level forecasts through aggregation. This approach is also known as the bottom-up…

Minnesota BART：面向宏观预测的稀疏与滞后收缩树先验AI 解读

Vector autoregression (VAR) models are widely used for forecasting and macroeconomic analysis, yet they remain limited by their reliance on a linear parameterization. Recent research has introduced nonparametric alternatives, such as Bayesian additive regression trees (BART), which provide flexibility without strong parametric assumptions. However, existing BART-based frameworks do not account for time dependency or allow for sparse estimation in the construction of…

追踪老挝 2022 年汇率危机与国际收支不稳定背后的潜在因素AI 解读

This working paper uses a Dynamic Factor Model ('the model') to identify underlying factors contributing to the debt-induced economic crisis in the People's Democratic Republic of Laos ('Laos'). The analysis aims to use the latent macroeconomic insights to propose ways forward for forecasting. We focus on Laos's historic structural weaknesses to identify when a balance of payments crisis with either a persistent current account imbalance or rapid capital outflows would…

避孕产品需求预测的混合方法：结合点预测与概率分布AI 解读

Accurate demand forecasting is vital for ensuring reliable access to contraceptive products, supporting key processes like procurement, inventory, and distribution. However, forecasting contraceptive demand in developing countries presents challenges, including incomplete data, poor data quality, and the need to account for multiple geographical and product factors. Current methods often rely on simple forecasting techniques, which fail to capture demand uncertainties…

高斯方差混合噪声下多变量门限自回归模型的贝叶斯估计AI 解读

A threshold autoregressive (TAR) model is a powerful tool for analyzing nonlinear multivariate time series, which includes special cases like self-exciting threshold autoregressive (SETAR) models and vector autoregressive (VAR) models. In this paper, estimation, inference, and forecasting using the Bayesian approach are developed for multivariate TAR (MTAR) models considering a flexible setup, under which the noise process behavior can be described using not only the…

FNETS：高维时间序列的因子调整网络估计与预测AI 解读

We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by {\it common} factors, models the remaining {\it idiosyncratic} dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS consists of three…

外推电价长期季节成分以改进日前市场预测AI 解读

Recent studies provide evidence that decomposing the electricity price into the long-term seasonal component (LTSC) and the remaining part, predicting both separately, and then combining their forecasts can bring significant accuracy gains in day-ahead electricity price forecasting. However, not much attention has been paid to predicting the LTSC, and the last 24 hourly values of the estimated pattern are typically copied for the target day. To address this gap, we introduce…

利用高频数据中的波动聚集提高局部投影估计效率AI 解读

This paper advances the local projections (LP) method by addressing its inefficiency in high-frequency economic and financial data with volatility clustering. We incorporate a generalized autoregressive conditional heteroskedasticity (GARCH) process to resolve serial correlation issues and extend the model with GARCH-X and GARCH-HAR structures. Monte Carlo simulations show that exploiting serial dependence in LP error structures improves efficiency across forecast horizons,…

消费者如何选择：用混合专家模型揭示潜在偏好AI 解读

Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as multinomial logit (MNL) and mixed logit models, impose rigid parametric assumptions that limit their ability to capture the complexity of consumer decision-making. This study introduces the Mixture of Experts (MoE) framework as a machine learning-driven…

资产定价中机器学习预测的不确定性AI 解读

Machine learning in asset pricing typically predicts expected returns as point estimates, ignoring uncertainty. We develop new methods to construct forecast confidence intervals for expected returns obtained from neural networks. We show that neural network forecasts of expected returns share the same asymptotic distribution as classic nonparametric methods, enabling a closed-form expression for their standard errors. We also propose a computationally feasible bootstrap to…

用贝叶斯 VAR 预测宏观数据：稀疏还是稠密？取决于情境AI 解读

Vector autogressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods, more concretely shrinkage priors, have shown to be successful in improving prediction performance. In the present paper, we introduce the semi-global framework, in which we replace the traditional global shrinkage parameter with group-specific shrinkage parameters. We show how this…

用于水文预测的 Matsuoka 型 GARMA 模型AI 解读

Time series in natural sciences, such as hydrology and climatology, and other environmental applications, often consist of continuous observations constrained to the unit interval (0,1). Traditional Gaussian-based models fail to capture these bounds, requiring more flexible approaches. This paper introduces the Matsuoka Autoregressive Moving Average (MARMA) model, extending the GARMA framework by assuming a Matsuoka-distributed random component taking values in (0,1) and an…

货币政策中的决策综合AI 解读

The macroeconomy is a sophisticated dynamic system involving significant uncertainties that complicate modelling. In response, decision-makers consider multiple models that provide different predictions and policy recommendations which are then synthesized into a policy decision. In this setting, we develop Bayesian predictive decision synthesis (BPDS) to formalize monetary policy decision processes. BPDS draws on recent developments in model combination and statistical…

战略行为主体下的实用表现性政策学习AI 解读

This paper studies the performative policy learning problem, where agents adjust their features in response to a released policy to improve their potential outcomes, inducing an endogenous distribution shift. There has been growing interest in training machine learning models in strategic environments, including strategic classification and performative prediction. However, existing approaches often rely on restrictive parametric assumptions: micro-level utility models in…

用于非高斯多元时间序列概率预测的动态 Copula 模型AI 解读

Multivariate time series (MTS) data often include a heterogeneous mix of non-Gaussian distributional features (asymmetry, multimodality, heavy tails) and data types (continuous and discrete variables). Traditional MTS methods based on convenient parametric distributions are typically ill-equipped to model this heterogeneity. Copula models provide an appealing alternative, but present significant obstacles for fully Bayesian inference and probabilistic forecasting. To…

通过潜在不变因果机制实现时间序列领域适应AI 解读

Time series domain adaptation aims to transfer the complex temporal dependence from the labeled source domain to the unlabeled target domain. Recent advances leverage the stable causal mechanism over observed variables to model the domain-invariant temporal dependence. However, modeling precise causal structures in high-dimensional data, such as videos, remains challenging. Additionally, direct causal edges may not exist among observed variables (e.g., pixels). These…

极端协变量下的二元结果模型：估计与预测AI 解读

This paper presents a novel semiparametric method to study the effects of extreme events on binary outcomes and subsequently forecast future outcomes. Our approach, based on Bayes' theorem and regularly varying (RV) functions, facilitates a Pareto approximation in the tail without imposing parametric assumptions beyond the tail. We analyze cross-sectional as well as static and dynamic panel data models, incorporate additional covariates, and accommodate the unobserved…

用于时间序列预测的监督筛选与正则化因子方法AI 解读

Factor-based forecasting using Principal Component Analysis (PCA) is an effective machine learning tool for dimension reduction with many applications in statistics, economics, and finance. This paper introduces a Supervised Screening and Regularized Factor-based (SSRF) framework that systematically addresses high-dimensional predictor sets through a structured four-step procedure integrating both static and dynamic forecasting mechanisms. The static approach selects…

全球神经网络与金融时间序列预测中的数据规模效应AI 解读

Neural networks have revolutionized many empirical fields, yet their application to financial time series forecasting remains controversial. In this study, we demonstrate that the conventional practice of estimating models locally in data-scarce environments may underlie the mixed empirical performance observed in prior work. By focusing on volatility forecasting, we employ a dataset comprising over 10,000 global stocks and implement a global estimation strategy that pools…