Improved penalization for determining the number of factors in approximate factor models
From MaRDI portal
Publication:613167
DOI10.1016/j.spl.2010.08.005zbMath1202.62081OpenAlexW2146661536MaRDI QIDQ613167
Lucia Alessi, Matteo Barigozzi, Marco Capasso
Publication date: 20 December 2010
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2010.08.005
Related Items (51)
F-test and z-test for high-dimensional regression models with a factor structure ⋮ Rank determination in tensor factor model ⋮ Wavelet estimation for factor models with time-varying loadings ⋮ Dynamic factor long memory volatility ⋮ Dissecting the financial cycle with dynamic factor models ⋮ Fundamental bubbles in equity markets ⋮ Transformed contribution ratio test for the number of factors in static approximate factor models ⋮ Robust High-Dimensional Volatility Matrix Estimation for High-Frequency Factor Model ⋮ A Randomized Sequential Procedure to Determine the Number of Factors ⋮ Recent developments in high dimensional covariance estimation and its related issues, a review ⋮ Dynamic factor models with infinite-dimensional factor space: asymptotic analysis ⋮ A self-reliant projected information criterion for the number of factors ⋮ Large-dimensional dynamic factor models: estimation of impulse-response functions with I(1) cointegrated factors ⋮ Autoencoder asset pricing models ⋮ Simultaneous multiple change-point and factor analysis for high-dimensional time series ⋮ Generalized dynamic factor models and volatilities: estimation and forecasting ⋮ Using principal component analysis to estimate a high dimensional factor model with high-frequency data ⋮ Factor models with local factors -- determining the number of relevant factors ⋮ Sequential testing for structural stability in approximate factor models ⋮ Consistent estimation of high-dimensional factor models when the factor number is over-estimated ⋮ Large volatility matrix analysis using global and national factor models ⋮ Efficient estimation of approximate factor models via penalized maximum likelihood ⋮ Factor models for high‐dimensional functional time series I: Representation results ⋮ Factor models for high‐dimensional functional time series II: Estimation and forecasting ⋮ On determination of the number of factors in an approximate factor model ⋮ Canonical correlation-based model selection for the multilevel factors ⋮ Detection of Multiple Structural Breaks in Large Covariance Matrices ⋮ Confidence intervals of treatment effects in panel data models with interactive fixed effects ⋮ Factor models in high-dimensional time series: A time-domain approach ⋮ Dynamic mortality factor model with conditional heteroskedasticity ⋮ Nonlinear factor models for network and panel data ⋮ Asymptotic analysis of the squared estimation error in misspecified factor models ⋮ Determining the number of factors when the number of factors can increase with sample size ⋮ Least squares estimation of large dimensional threshold factor models ⋮ On the determination of the number of factors using information criteria with data-driven penalty ⋮ Sufficient forecasting using factor models ⋮ Robust factor number specification for large-dimensional elliptical factor model ⋮ Large Covariance Estimation by Thresholding Principal Orthogonal Complements ⋮ Bi-cross-validation for factor analysis ⋮ Eigenvalue difference test for the number of common factors in the approximate factor models ⋮ Factor dimension determination for panel interactive effects models: an orthogonal projection approach ⋮ Projected principal component analysis in factor models ⋮ Impairment of monetary autonomy: case of ``trilemma`` vs. ``duo ⋮ A wavelet method for panel models with jump discontinuities in the parameters ⋮ Factor and Idiosyncratic Empirical Processes ⋮ Did financial factors matter during the Great Recession? ⋮ A diagnostic criterion for approximate factor structure ⋮ Model selection for factor analysis: Some new criteria and performance comparisons ⋮ Dynamic factor models with infinite-dimensional factor spaces: one-sided representations ⋮ Determining the number of factors with potentially strong within-block correlations in error terms ⋮ Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components
Cites Work
This page was built for publication: Improved penalization for determining the number of factors in approximate factor models
