# Factor-Based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions

*Ercument Cahan*,
*Jushan Bai* () and
*Serena Ng* ()

Papers from arXiv.org

**Abstract:**
Economists are blessed with a wealth of data for analysis, but more often than not, values in some entries of the data matrix are missing. Various methods have been proposed to handle missing observations in a few variables. We exploit the factor structure in panel data of large dimensions. Our \textsc{tall-project} algorithm first estimates the factors from a \textsc{tall} block in which data for all rows are observed, and projections of variable specific length are then used to estimate the factor loadings. A missing value is imputed as the estimated common component which we show is consistent and asymptotically normal without further iteration. Implications for using imputed data in factor augmented regressions are then discussed. To compensate for the downward bias in covariance matrices created by an omitted noise when the data point is not observed, we overlay the imputed data with re-sampled idiosyncratic residuals many times and use the average of the covariances to estimate the parameters of interest. Simulations show that the procedures have desirable finite sample properties.

**Date:** 2021-03, Revised 2022-02

**New Economics Papers:** this item is included in nep-ecm and nep-rmg

**References:** View references in EconPapers View complete reference list from CitEc

**Citations:** Track citations by RSS feed

**Downloads:** (external link)

http://arxiv.org/pdf/2103.03045 Latest version (application/pdf)

**Related works:**

This item may be available elsewhere in EconPapers: Search for items with the same title.

**Export reference:** BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text

**Persistent link:** https://EconPapers.repec.org/RePEc:arx:papers:2103.03045

Access Statistics for this paper

More papers in Papers from arXiv.org

Bibliographic data for series maintained by arXiv administrators ().