Modelling multiple time series via common factors

Jiazhu Pan · Qiwei Yao

128 citations0/0 files storedBiometrika

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Risk and Volatility Modeling Statistical Methods and Applications Spatial and Panel Data Analysis

Abstract

We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to some nonstationary time series. The unobservable, nonstationary factors are identified by expanding the white noise space step by step, thereby solving a high-dimensional optimization problem by several low-dimensional sub-problems. Asymptotic properties of the estimation are investigated. The proposed methodology is illustrated with both simulated and real datasets.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 128 citations exist upstream.

References

2002Determining the Number of Factors in Approximate Factor Models 2002Forecasting Using Principal Components From a Large Number of Predictors 2000The Generalized Dynamic-Factor Model: Identification and Estimation 1997Estimating continuous-time stochastic volatility models of the short-term interest rate 2005Diagnostic Checking in ARMA Models With Uncorrelated Errors 1981Maximum Likelihood "Confirmatory" Factor Analysis of Economic Time Series

Cited by

2011Large Volatility Matrix Inference via Combining Low-Frequency and High-Frequency Approaches 2014Consistently determining the number of factors in multivariate volatility modelling 2021Factor extraction using Kalman filter and smoothing: This is not just another survey 2018Estimating Non-stationary Common Factors: Implications for Risk Sharing 2013Dynamic Factor Models: A Review of the Literature 2023Multi-view metro station clustering based on passenger flows: a functional data-edged network community detection approach

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.