Periodically expanding discounted debt: a threat to fiscal policy sustainability?
周期性扩张的贴现债务是否威胁财政政策可持续性?AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要明确使用 Markov 转换时间序列模型分析美国贴现债务及长期财政可持续性。
周期性扩张的贴现债务是否威胁财政政策可持续性?AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
Abstract This paper models the behaviour of discounted US debt using a Markov‐switching time series model. The significance of modelling fiscal policy within this framework derives from the implications it has for long‐term sustainability. The two‐regime framework used in this paper identifies periods where the present value of US Federal debt is expanding versus periods when it is collapsing. Using an updated data series from Hamilton and Flavin ( 1986 ), a test is conducted to establish if the expanding periods pose a threat to the long‐run sustainability of fiscal policy. For the USA, it is found that they do not. Copyright © 2005 John Wiley & Sons, Ltd.
高维近似因子时间序列模型的主成分分析:假设、渐近理论与识别
We consider estimation of large approximate factor models in high-dimensional panels of stationary time series using Principal Component Analysis (PCA). We review the key results establishing the necessary and sufficient conditions for consistency and asymptotic normality of the estimators, which hold when both the cross-sectional dimension $n$ and the sample size $T$ tend to infinity. Special emphasis is placed on identification. First, we show that the common and…
有意过度识别的局部投影
Local projections (LPs) have become a standard tool for estimating dynamic effects in macroeconomics and are typically implemented using ordinary least squares (OLS) for a given lag order. We propose an alternative approach that exploits additional lags as excluded instrumental variables (IVs). Under a finite-order model with martingale-difference shocks, the resulting (optimally weighted) IV estimator is never asymptotically less efficient than OLS and, as we show, can…
部分观测时间序列的潜变量驱动插补与预测:LIFTS
Partially observed multivariate time series pose two coupled challenges: recovering missing measurements and forecasting future trajectories when observation patterns are informative. Existing methods often assume ignorable missingness or use the mask only as an auxiliary input. We propose LIFTS (Latent-driven Imputation and Forecasting for partly observed Time Series), a probabilistic framework in which a latent process jointly governs the complete measurement trajectory…
时间序列基础模型的潜在推理时引导
Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on in-context learning to make their predictions, which makes the quality of their performance highly sensitive to the user-selected lookback, covariates, horizon and training data distributions. In practise, the quality of the forecasts are variable but complementary, which highlights the need for a principled ensembling approach,…
紧识别SVAR中基于“软”符号限制的快速后验抽样
We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on ‘soft’ sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on…
大冲击宏观模型的快速高效估计
We propose a new approach for efficiently estimating and analyzing macroeconomic models subject to large shocks. We apply the methodology to a two-sector model that captures the heterogeneous exposure of different sectors to the COVID-19 pandemic. We solve the model nonlinearly and propose a new nonlinear, non-Gaussian filter designed to handle large shocks and identify their source and time location. Monte Carlo experiments show that the estimation and identification of…