Ercument Cahan, Jushan Bai, Serena Ng
arXiv 4 Mar 2021 · Econometrics · publishedJournal of Econometrics (2022) · 8 citations (OpenAlex)
arXiv:2103.03045 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jin, Miao and Su (2021) On Factor Models with Random Missing: EM Estimation, Inference, and Cross Validation, Journal of Econometrics 222:1, Part C, 745… | 0.874 | 5 | 2 | 100% |
| 2 | Xiong and Pelger (2019) Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference, SSRN Working Paper 3465… | 0.874 | 5 | 2 | 100% |
| 3 | Bai and Ng (2021) Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data, Journal of the American Statistical Association | 0.773 | 13 | 5 | 46% |
| 4 | Bai and Ng (2006) Confidence Intervals for Diffusion Index Forecasts and Inference with Factor-Augmented Regressions, Econometrica 74:4, 1133–1150 | 0.737 | 3 | 2 | 100% |
| 5 | Bai (2003) Inferential Theory for Factor Models of Large Dimensions, Econometrica 71:1, 135–172 self | 0.644 | 4 | 1 | 100% |
| 6 | Stock and Watson (2016) Factor Models and Structural Vector Autoregressions in Macroeconomics, in J. B | 0.644 | 2 | 2 | 100% |
| 7 | Stock and Watson (1998) Diffusion Indexes, NBER Working Paper 6702 | 0.511 | 2 | 1 | 100% |
| 8 | Athey, Bayati, Doudchenko, Imbens and Khosravi (2018) Matrx Completion Methods for Causal Panel Data Methods, arXiv:1710.10251v2 | 0.405 | 1 | 1 | 100% |
| 9 | Bai and Ng (2002) Determining the Number of Factors in Approximate Factor Models, Econometrica 70:1, 191–221 | 0.405 | 1 | 1 | 100% |
| 10 | Banbura and Modugno (2014) Maximum Likelihood Estimation of Factor Models on Datasets with Arbitrary Pattern of Missing Data, Journal of Applied Econometri… | 0.405 | 1 | 1 | 100% |
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