Joint Forecasts of U.S. Marital Fertility, Nuptiality, Births, and Marriages Using Time Series Models
利用时间序列模型联合预测美国婚内生育、婚姻、出生与结婚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:摘要明确采用 Box–Jenkins 时间序列方法进行人口总量与发生率预测,对应时间序列和预测方向。
利用时间序列模型联合预测美国婚内生育、婚姻、出生与结婚AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
This article presents a new approach to forecasting U.S. marital fertility, nuptiality, births, and marriages. The analysis represents a wedding of demographic and statistical time series in models amenable to Box-Jenkins techniques of model identification, estimation, diagnosis, and forecasting. The models demonstrate the advantages in this approach in forecasting both rates and events as opposed to the common practice of simply forecasting events. Using the best models of indexes of fertility and nuptiality, forecasts of births and first marriages are made for the U.S. for the years 1983–2000. Analyses of these forecasts are made with discussions of their demographic realism in terms of their forecast confidence intervals.
藤Copula VAR:从递归边际估计到联合预测推断
Joint-event forecasts often combine a dependence estimate based on past forecast errors with newly estimated marginal distributions. When each historical error retains the marginal fit available at its issue date, inference must account for an overlapping sequence of estimation errors. We derive their joint influence with the terminal forecast estimates in a stable Vine Copula VAR with normal innovation margins and a fixed, correctly specified Gaussian or positive Clayton…
基于分位数的 Kumaraswamy–Teissier 自回归移动平均模型
This paper introduces a quantile-based Kumaraswamy-Teissier autoregressive moving average (KTARMA) model for positive-valued time series. Leveraging the flexibility of the extended Kumaraswamy-Teissier distribution within an observation-driven framework, the random component of the distribution is conditioned on the historical process and time-varying covariates, and is parameterized explicitly via its $ρ$-th conditional quantile, where $ρ\in (0,1)$. To capture temporal…
具有分组边效应的降秩网络自回归
We propose the Edge-Grouped Reduced-Rank Network Autoregressive model (Edge-Grouped RRNAR) for multivariate time series observed over a network. The model allows transmission effects to vary across prespecified and economically interpretable groups of edges, while using a low-rank structure to capture cross-variable dynamics. This structure separates where network transmission occurs from which variables transmit and respond. We develop a topology-aware and block-specific…
WIRED:结构化依赖下的加权自适应多序列概率预测
This paper presents WIRED, an R package algorithm for joint probabilistic forecasting of multiple related time series. WIRED combines a library of simple marginal predictive distributions, CRPS-based adaptive mixture weights, and a Gaussian or Student t copula for cross-series simulation. We evaluate the implementation in a benchmark with four synthetic data-generating processes (DGPs), three forecast horizons, 30 replicates per DGP-horizon pair, nine ablations and external…
可扩展的 VARMA 模型估计
Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identified only up to equivalence, and every evaluation costs a pass over the entire series. Yet their moving-average term captures with a few parameters what a pure autoregression matches only with many lags. We introduce an estimation framework that removes this computational barrier: each optimization…
离散卡方方法与厄尔尼诺时间序列中的太阳强迫:太平洋辐射计解释
Discrete Chi-square Method (DCM) can detect multiple signals superimposed on an arbitrary trend. DCM's backbone is Gauss-Markov theorem that Least Squares (LS) is the best unbiased estimator for linear regression models. DCM is robust because it computes numerous linear model LS fits. Discrete Fourier Transform and other frequency-domain methods have many application limitations. None of those limitations constrains DCM. Fisher-test provides signal significances and…