•  
  •  
 

Journal of Undergraduate Research

Keywords

historiography, data truncation, Monte Carlo stimulations

College

Family, Home, and Social Sciences

Department

Economics

Abstract

Data truncation is the source of econometric problems in many economic datasets. Truncation occurs when all observations below or above a certain threshold are systematically removed or are unavailable. For example, campaign contributions below a certain level are not usually publicly available, so any contribution below that level would not be included in the dataset. When such data is used in standard ordinary least squares analysis, the results of the analysis are biased and inconsistent. Many methods have been developed to try to correct for truncation bias with varying degrees of success. In my research, I examined a new estimation method of Quasi-maximum Likelihood using flexible distributions and compared it to a few other existing methods. In past research, similar methods have proven to be more accurate and efficient than many other standard models

Download

Share

COinS