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Found 8 papers, 2 papers with code
Imputation of Counterfactual Outcomes when the Errors are Predictable
Often overlooked is the possibility that the out-of-sample error can be informative about the missing counterfactual outcome if it is mutually or serially correlated.
Constructing High Frequency Economic Indicators by Imputation
This is also the case for a weekly version of the CFNAI index of economic activity that is imputed using seasonally unadjusted data.
Approximate Factor Models with Weaker Loadings
Pervasive cross-section dependence is increasingly recognized as a characteristic of economic data and the approximate factor model provides a useful framework for analysis.
Factor-Based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions
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.
Modeling Macroeconomic Variations After COVID-19
Dynamic responses of variables to shocks in a VAR similar in magnitude and shape to the ones identified before 2020 can be recovered by directly or indirectly modeling covid and treating it as exogenous.
Estimation and Inference by Stochastic Optimization: Three Examples
This paper illustrates two algorithms designed in Forneron & Ng (2020): the resampled Newton-Raphson (rNR) and resampled quasi-Newton (rqN) algorithms which speed-up estimation and bootstrap inference for structural models.
Least Squares Estimation Using Sketched Data with Heteroskedastic Errors
The result arises because the sketched estimates in the case of random projections can be expressed as degenerate $U$-statistics, and under certain conditions, these statistics are asymptotically normal with homoskedastic variance.
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
This paper proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data.
