Feasible weighted projected principal component analysis for semi-parametric factor models

Sung Hoon Choi

The Econometrics Journal, 2023, vol. 26, issue 2, 215-234

Abstract: SummaryVarious factor estimation procedures have been developed, based on the latent factor model. They often consider general conditions that allow for correlations and heteroscedasticity. However, the conventional principal components method does not efficiently estimate the parameters. It also does not accommodate additional covariates, which explain the unknown factors, even if they are available. In particular, a few aggregated macroeconomic variables can be used as covariates in diffusion index forecasts. To account for these features, I propose the feasible weighted projected principal component (WPPC) analysis, based on semi-parametric factor models, and also establish its asymptotic properties. In addition, I apply the WPPC method to the diffusion index forecasting model. Finally, I investigate the performance of the WPPC estimator in forecasting excess bond returns using US bond market and macroeconomic data.

Keywords: Conditional sparsity; cross-sectional correlation; heteroscedasticity; semi-parametric factor models; unknown factors (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:26:y:2023:i:2:p:215-234.

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