It is well known that height is positively associated with earnings. Based on individual level data, this paper investigates the channels through which height influences income in China. Our first key finding is that taller people are more likely to become members of the Communist Party,...

Previously we introduced Singular Spectrum Analysis SSA and its multivariate extension MSSA as a powerful tool for cleaning data. Here we compare MSSA with the data filling algorithm M-REM (Multivariate Regularized Expectation Maximization). We compare theoretical methodology, numerical...

Data cleaning in the real world has to cope with new data arriving (or failing to arrive) as time passes, and which may be bad data. We illustrate MSSA data cleaning with a real-time historical simulation on some problematic data. The example also serves to determine some MSSA algorithm...

This paper introduces a powerful method for detecting and fixing unphysical spikes in time series. The method utilizes Multiple Singular Spectrum Analysis (MSSA) to define local market trends used to identify outlier data spikes that are not caused by market movements, and then effectively...

We introduce a powerful method for cleaning time series - Multi-Channel Singular Spectrum Analysis (MSSA). “Cleaning” means filling data gaps and removing unphysical spikes, which are chronic problems. MSSA utilizes all available information in “time” and “space” with...

This study investigates the role of employment policies in reducing internal control ineffectiveness and financial restatements. We provide new evidence that employee treatment policies are an important predictor of ineffective internal control. We also find that employee-friendly policies...

Fair lending analysis of non-mortgage credit products often involves proxying for race/ethnicity since such information is not required to be reported. Using mortgage data, this paper evaluates a series of proxy approaches (geo, surname, geo-surname, and BISG) as compared with the race/ethnicity...

This paper studies the interplay of machine learning and sampling scheme in an empirical analysis of money laundering detection algorithms. Using actual transaction data provided by a U.S. financial institution, we study five major machine learning algorithms including Bayes logistic regression,...

We examine whether debt covenant violations serve as an important determinant of material weaknesses in internal control over financial reporting. Motivated by the debt covenant hypothesis, we posit that the probability of ineffective internal control is expected to be significantly higher for...