每日简报

结合 NUTS 与 ADVI：探索 Prophet 的贝叶斯推断方法AI 解读

Since its introduction, Facebook Prophet has attracted positive attention from both classical statisticians and the Bayesian statistics community. The model provides two built-in inference methods: maximum a posteriori estimation using the L-BFGS-B algorithm, and Markov Chain Monte Carlo (MCMC) sampling via the No-U-Turn Sampler (NUTS). While exploring various time-series forecasting problems using Bayesian inference with Prophet, we encountered limitations stemming from the…

是否采用欧元：欧元对贸易影响的异质性AI 解读

Two decades of research on the euro's trade effects have produced estimates ranging from 4% to 30%, with no consensus on the magnitude. We find evidence that this divergence may reflect genuine heterogeneity in the euro's trade effect across country pairs rather than methodological differences alone. Using Eurostat data on 15 EU countries (12 eurozone members plus Denmark, Sweden, and the UK as controls) from 1995-2015, we estimate that euro adoption increased bilateral…

服务于货币政策的预测AI 解读

This paper discusses three key themes in forecasting for monetary policy highlighted in the Bernanke (2024) review: the challenges in economic forecasting, the conditional nature of central bank forecasts, and the importance of forecast evaluation. In addition, a formal evaluation of the Bank of England's inflation forecasts indicates that, despite the large forecast errors in recent years, they were still accurate relative to common benchmarks.

复杂非平稳噪声下同步压缩变换的不确定性量化AI 解读

We propose a bootstrapping framework to quantify uncertainty in time-frequency representations (TFRs) generated by the short-time Fourier transform (STFT) and the STFT-based synchrosqueezing transform (SST) for oscillatory signals with time-varying amplitude and frequency contaminated by complex nonstationary noise. To this end, we leverage a recent high-dimensional Gaussian approximation technique to establish a sequential Gaussian approximation for nonstationary processes…

高维时变模型的广义自适应联合学习框架AI 解读

In modern biomedical and econometric studies, longitudinal processes are often characterized by complex time-varying associations and abrupt regime shifts that are shared across correlated outcomes. Standard functional data analysis (FDA) methods, which prioritize smoothness, often fail to capture these dynamic structural features, particularly in high-dimensional settings. This article introduces Adaptive Joint Learning (AJL), a hierarchical regularization framework…

基于距离协方差的长记忆时间序列独立性检验AI 解读

We apply the concept of distance covariance for testing independence of two long-range dependent time series. As test statistic we propose a linear combination of empirical distance cross-covariances. We derive the asymptotic distribution of the test statistic, and we show consistency against a very general class of alternatives. The asymptotic theory developed in this paper is based on a novel non-central limit theorem for stochastic processes with values in an $L^2$-Hilbert…