Robust Factor Models with Explanatory Proxies

Fan, Jianqing; Yuan, Ke; Liao, Yuan

doi:10.2139/ssrn.2753404

Cited by 7 publications

(4 citation statements)

References 88 publications

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“…They inferred that the DI is an effective approach to lessen the regression dimension, and it appears to be difficult to enhance this performance without introducing severe changes to the predictive model. Recently, the factor models extended for forecasting aims include those of [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Fat Big Data Using Factor Models and Penalization Techniques: A Monte Carlo Simulation and Application

Khan,

Albalawi

2024

Axioms

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This article assesses the predictive accuracy of factor models utilizing Partial·Least·Squares (PLS) and Principal·Component·Analysis (PCA) in comparison to autometrics and penalization techniques. The simulation exercise examines three types of scenarios by introducing the issues of multicollinearity, heteroscedasticity, and autocorrelation. The number of predictors and sample size are adjusted to observe the effects. The accuracy of the models is evaluated by calculating the Root·Mean·Square·Error (RMSE) and the Mean·Absolute·Error (MAE). In the presence of severe multicollinearity, the factor approach utilizing (PLS demonstrates exceptional performance in comparison. Autometrics achieves the lowest RMSE and MAE values across all levels of heteroscedasticity. Autometrics provides better forecasts with low and moderate autocorrelation. However, Elastic·Smoothly·Clipped·Absolute·Deviation (E-SCAD) forecasts well with severe autocorrelation. In addition to the simulation, we employ a popular Pakistani macroeconomic dataset for empirical research. The dataset contains 79 monthly variables from January 2013 to December 2020. The competing approaches perform differently compared to the simulation datasets, although “The PLS factor approach outperforms its competing approaches in forecasting, with lower RMSE and MAE”. It is more probable that the actual dataset exhibits a high degree of multicollinearity.

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Section: Introductionmentioning

confidence: 99%

Analysis of Fat Big Data Using Factor Models and Penalization Techniques: A Monte Carlo Simulation and Application

Khan,

Albalawi

2024

Axioms

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“…Some latent factors can be approximated well by observed economic factors, such as Fama-French factors 8 for equity data (Fama and French, 1992) or level, slope, and curvature factors for bond data (Diebold and Li, 2006). Fan et al (2016a) propose robust factor models to exploit the explanatory power of observed proxies on latent factors. Another approach is to model how the factor loadings relate to observable variables.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Proximate Factors for Large Dimensions

Pelger

Xiong

2018

SSRN Journal

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“…Ma, Linton and Gao (2017) introduced a quantile version of such models. Fan, Ke and Liao (2016) considered a linear factor model where the factors can be partially explained by observed covariates. Nonetheless, nonlinearity into the factors is yet to be addressed.…”

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confidence: 99%

Nonparametric additive factor models

Bodelet,

Shan

2020

Preprint

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This paper proposes a nonparametric additive factor model where the common components depend on the latent factors through unknown smooth functions. Our approach is novel in the literature on nonlinear factor models as we propose a general and rigorous framework for identification, specify a general nonparametric and flexible estimation procedure based on sieve methods, and derive consistency results. The key point of our strategy relies on the specification of an asymptotic parameter space for the factors. Estimation is then obtained by using sieve approximations of this infinite dimensional factor space. We prove convergence of the sieve estimators as both time and cross-sectional sizes increase at appropriate rates. The finite sample performance of the estimators is illustrated in extensive numerical experiments. Finally, we show relevance and usefulness of our method by an application to a nonlinear CAPM on S&P500 data.

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Robust Factor Models with Explanatory Proxies

Cited by 7 publications

References 88 publications

Analysis of Fat Big Data Using Factor Models and Penalization Techniques: A Monte Carlo Simulation and Application

Analysis of Fat Big Data Using Factor Models and Penalization Techniques: A Monte Carlo Simulation and Application

Interpretable Proximate Factors for Large Dimensions

Nonparametric additive factor models

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