Identifying a Simplifying Structure in Time Series
From MaRDI portal
Publication:3761507
DOI10.2307/2288794zbMath0623.62081OpenAlexW4248078258MaRDI QIDQ3761507
Publication date: 1987
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.2307/2288794
identificationcanonical transformationeigenvectorsmultivariate time seriesmultiple time seriesdynamic factor analysisnumber of factorshidden factorsinfinite moving average representationrank of the covariance matricessimplifying transformationvector autoregressive integrated moving average model
Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)
Related Items (48)
Simultaneous Statistical Inference in Dynamic Factor Models ⋮ Generalized principal component analysis for moderately non-stationary vector time series ⋮ Forecasting Multiple Time Series With One-Sided Dynamic Principal Components ⋮ Extracting a low-dimensional predictable time series ⋮ Statistical Tests and Estimators of the Rank of a Matrix and Their Applications in Econometric Modelling ⋮ Wavelet estimation for factor models with time-varying loadings ⋮ Martingale Difference Divergence Matrix and Its Application to Dimension Reduction for Stationary Multivariate Time Series ⋮ Testing multiple equation systems for common nonlinear components ⋮ Moving dynamic principal component analysis for non-stationary multivariate time series ⋮ A fragmented-periodogram approach for clustering big data time series ⋮ COINTEGRATION AND COMMON FACTORS ⋮ Identifying the finite dimensionality of curve time series ⋮ Statistical inference in factor analysis for diffusion processes from discrete observations ⋮ Measuring the Advantages of Multivariate vs. Univariate Forecasts ⋮ Testing for symmetric correlation matrices with applications to factor models ⋮ Factor modeling of multivariate time series: a frequency components approach ⋮ Covariance changes detection in multivariate time series ⋮ Factor Models for High-Dimensional Tensor Time Series ⋮ Forecasting with nonstationary dynamic factor models ⋮ Factor modeling for high-dimensional time series: inference for the number of factors ⋮ Factor models in high-dimensional time series: A time-domain approach ⋮ An Extended Portmanteau Test for VARMA Models With Mixing Nonlinear Constraints ⋮ Likelihood Function and Canonical Correlation Analysis of the Peña–Box Model ⋮ Robustness Comparison of the Peña–Box Model and the Factor Model to Extract Useful Predictors ⋮ A spectral EM algorithm for dynamic factor models ⋮ Dynamic factor analysis of nonstationary multivariate time series ⋮ Robust factor modelling for high-dimensional time series: an application to air pollution data ⋮ Principal component analysis for second-order stationary vector time series ⋮ Nonstationary dynamic factor analysis ⋮ Functional dynamic factor models with application to yield curve forecasting ⋮ Data science, big data and statistics ⋮ On the logical development of statistical models ⋮ Mortality forecasting using factor models: time-varying or time-invariant factor loadings? ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Bias correction for time series factor models ⋮ Model specification and selection for multivariate time series ⋮ Model selection criteria for reduced rank multivariate time series: a simulation study ⋮ Generalized dynamic factor models and volatilities: consistency, rates, and prediction intervals ⋮ A robust procedure to build dynamic factor models with cluster structure ⋮ Threshold factor models for high-dimensional time series ⋮ Estimating change-point latent factor models for high-dimensional time series ⋮ Computation of the Beveridge--Nelson decomposition for multivariate economic time series ⋮ Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages ⋮ Clustering time series by linear dependency ⋮ On time-irreversibility and other non-linear features in time series ⋮ On a new procedure for identifying a dynamic common factor model ⋮ Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components
This page was built for publication: Identifying a Simplifying Structure in Time Series
