Pages that link to "Item:Q506051"
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The following pages link to Determining the number of factors when the number of factors can increase with sample size (Q506051):
Displaying 31 items.
- Bi-cross-validation for factor analysis (Q104117) (← links)
- Factor modeling for high-dimensional time series: inference for the number of factors (Q447821) (← links)
- On the determination of the number of factors using information criteria with data-driven penalty (Q513695) (← links)
- Estimating the number of common factors in serially dependent approximate factor models (Q694956) (← links)
- Exact and asymptotic tests on a factor model in low and large dimensions with applications (Q739589) (← links)
- Determining the number of factors in approximate factor models by twice K-fold cross validation (Q777679) (← links)
- Determination of vector error correction models in high dimensions (Q1739869) (← links)
- Robust factor number specification for large-dimensional elliptical factor model (Q2008233) (← links)
- Rank determination in tensor factor model (Q2136659) (← links)
- Efficient estimation of heterogeneous coefficients in panel data models with common shocks (Q2173185) (← links)
- Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects (Q2227062) (← links)
- Inferences in panel data with interactive effects using large covariance matrices (Q2398975) (← links)
- The application of spectral distribution of product of two random matrices in the factor analysis (Q2465139) (← links)
- Consistent variable selection in large panels when factors are observable (Q2489495) (← links)
- Group fused Lasso for large factor models with multiple structural breaks (Q2688655) (← links)
- Eigenvalue ratio test for the number of factors (Q2857587) (← links)
- Consistently determining the number of factors in multivariate volatility modelling (Q2950203) (← links)
- Estimating the number of factors to include in a high-dimensional multivariate bilinear model (Q4784241) (← links)
- A self-reliant projected information criterion for the number of factors (Q5077197) (← links)
- Determining the number of factors in high-dimensional generalized latent factor models (Q5102503) (← links)
- Nonlinear Factor‐Augmented Predictive Regression Models with Functional Coefficients (Q5111851) (← links)
- THE FACTOR-LASSO AND K-STEP BOOTSTRAP APPROACH FOR INFERENCE IN HIGH-DIMENSIONAL ECONOMIC APPLICATIONS (Q5384842) (← links)
- Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components (Q5870780) (← links)
- Estimating Number of Factors by Adjusted Eigenvalues Thresholding (Q5885109) (← links)
- Learning Latent Factors From Diversified Projections and Its Applications to Over-Estimated and Weak Factors (Q5885115) (← links)
- Linear panel regressions with two-way unobserved heterogeneity (Q6090548) (← links)
- Shrinkage estimation of multiple threshold factor models (Q6108331) (← links)
- On determination of the number of factors in an approximate factor model (Q6138244) (← links)
- Forecasting a Nonstationary Time Series Using a Mixture of Stationary and Nonstationary Factors as Predictors (Q6150354) (← links)
- Mining the factor zoo: estimation of latent factor models with sufficient proxies (Q6150517) (← links)
- Nonparametric Estimation and Conformal Inference of the Sufficient Forecasting With a Diverging Number of Factors (Q6620856) (← links)
