2021/03/04 by Ercument Cahan, Jushan Bai, Cahan, Ercument +3 · 3 citations
Economics, Econometrics and Finance · Environmental Science · Mathematics · #Econometrics (econ.EM) #FOS: Economics and business #Soil Geostatistics and Mapping #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference #econ.EM
paper · pdf · doi:10.48550/arxiv.2103.03045
openalex publication_date 2021/03/04 · arxiv created 2022/02/01 · arxiv updated 2022/02/02 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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 tall-project algorithm first estimates the factors from a 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.