arXiv (OAI Expanded)
open access
Principal Component Analysis for High-Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification
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 ho
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Principal Component Analysis for High-Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification#923981
- Main Effect Factor Models in High-Dimensional Matrix Time Series: Identification and Sparsity#652088
- Main Effect Factor Models in High-Dimensional Matrix Time Series: Identification and Sparsity#650635
- Optimal Estimation of Large-Dimensional Nonlinear Factor Models#1143559
- IV Estimation of Heterogeneous Spatial Dynamic Panel Models with Interactive Effects#1061588
Record · ID 919752
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive