Parameter cascading for panel models with unknown number of unobserved factors: an application to the credit spread puzzle
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Publication:1623512
DOI10.1016/j.csda.2013.11.007zbMath1506.62017OpenAlexW2043662886MaRDI QIDQ1623512
Publication date: 23 November 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2013.11.007
model selection criteriafactor modelcommon stochastic trendscredit spread puzzlefactor error structurelarge panel data
Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)
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Cites Work
- Panel cointegration with global stochastic trends
- Panel data models with multiple time-varying individual effects
- Selecting the number of components in principal component analysis using cross-validation approximations
- Asymptotics of the principal components estimator of large factor models with weakly influential factors
- Estimating the number of common factors in serially dependent approximate factor models
- On the number of principal components: a test of dimensionality based on measurements of similarity between matrices
- Detection of structural breaks in linear dynamic panel data models
- Estimating cross-section common stochastic trends in nonstationary panel data
- A new approach for selecting the number of factors
- A NEW PANEL DATA TREATMENT FOR HETEROGENEITY IN TIME TRENDS
- Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
- Testing Hypotheses About the Number of Factors in Large Factor Models
- A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix
- Forecasting Using Principal Components From a Large Number of Predictors
- Linear Mixed-Effects Modeling by Parameter Cascading
- Panel Data Models With Interactive Fixed Effects
- Determining the Number of Factors in the General Dynamic Factor Model
- A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets
- Determining the Number of Factors in Approximate Factor Models