PRESENT VALUE RELATIONS, GRANGER NONCAUSALITY, AND VAR STABILITY
现值关系、格兰杰非因果性与VAR稳定性AI 解读 ↓
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AI 根据所提供的论文材料归类:摘要明确研究宏观时间序列中的VAR稳定性及格兰杰非因果限制,直接对应时间序列分析。
现值关系、格兰杰非因果性与VAR稳定性AI 解读 ↓
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When in the class of “exact” present value (PV) relations the decision variables do not Granger cause the explanatory variables, and a vector autoregressive (VAR) process is used to derive the cross-equation restrictions, the system embodies explosive roots, which hardly can be reconciled with the typical features observed in most macroeconomic time series. This paper investigates the issue.I thank Paolo Paruolo and two anonymous referees for helpful comments and suggestions on earlier drafts of the paper. I am solely responsible for all remaining errors.
I study randomization inference for dynamic causal effects in panels with many units and few periods when treatment is endogenous and an instrument is as good as randomly assigned only through the innovations of a first-order autoregressive assignment mechanism with unknown persistence. The persistence is a nuisance parameter in two senses: it determines which weighted average of heterogeneous causal effects the instrumented local projection estimand measures, and it governs…
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…
非线性时间序列中的函数值因果影响
Causal discovery in time series is increasingly performed using nonlinear machine-learning models, yet the resulting causal relationships are almost always summarized by scalar edge scores. We argue that this practice obscures the true object learned by nonlinear autoregressive models: a state-dependent function whose effect varies across regimes, magnitudes, and contexts. We formalize function-valued causal influence for additive, contribution-decomposable architectures and…
通过正交化自适应估计发现存在混杂的时间序列因果滞后结构
Finding which variables cause which others in multivariate time series, and at what lags, is central to science and policy, yet existing methods force a choice between flexible confounder adjustment, data-driven lag selection, and inference that controls the false discovery rate (FDR). ORACLE-VARX does all three in one pipeline. First, double/debiased machine learning (DML) removes nonlinear confounder effects from the outcomes and the lagged series. Second, adaptive causal…
矩阵自回归模型的脉冲响应推断
Matrix autoregressive (MAR) models offer a parsimonious framework for modeling matrix-valued time series, yet tools for estimation and inference for their impulse response functions are lacking. We develop asymptotic and bootstrap-based inference for impulse responses of stable MAR($p$) models. We derive the joint asymptotic distribution of the coefficient and covariance estimators, which permits closed-form delta-method standard errors. To address finite-sample bias, we…
具有个性化多样性的个性化联邦向量自回归
Personalized federated learning can improve estimation for high-dimensional time series while preserving client-specific dynamics. However, parameter heterogeneity creates a fundamental ambiguity between shared and personalized structure. We introduce the principle of personalization diversity, under which genuinely personalized effects recur in only a limited fraction of clients. Based on this principle, we propose PerFeCT-VAR, Personalized Federated Vector Autoregression…