Missing-Data Imputation in Nonstationary Panel Data Models

Abstract

A linear interpolation (Lerp) approach, utilizing a common stochastic trend, is explored to impute missing values in nonstationary panel data models. The Lerp algorithm is considerably faster and easier to use than the leading methods recommended in the statistics literature. It shows through a set of simulations that the Lerp works well, whereas other existing methods fail to perform properly, when the panel data contain a high degree of missingness and/or a strong correlation across cross-sectional units. As an illustration, the method is applied to study the cost-of-living-index dataset with missing values. The test on the imputed panel data provides the supporting evidence for the U.S. economy convergence that depends on the state physical spatial proximities and the state industrial development similarities.

Citation

Kang, W. (2011), "Missing-Data Imputation in Nonstationary Panel Data Models", Drukker, D.M. (Ed.) Missing Data Methods: Time-Series Methods and Applications (Advances in Econometrics, Vol. 27 Part 2), Emerald Group Publishing Limited, Leeds, pp. 235-251. https://doi.org/10.1108/S0731-9053(2011)000027B007