Robust measures of skewness and kurtosis for macroeconomic and financial time series

The sample skewness and kurtosis of macroeconomic and financial time series are routinely scrutinized in the early stages of model-building and are often the central topic of studies in economics and finance. Notwithstanding the availability of several robust estimators, most scholars in economics rely on method-of-moments estimation that is known to be very sensitive to outliers. We carry out an extensive Monte Carlo analysis to evaluate the bias and root mean squared error of twelve different estimators of skewness and kurtosis. We consider nine statistical distributions that approximate the range of data generating processes of many macroeconomic and financial time series. Both in independently and identically distributed samples and in data generating processes featuring serial correlation L-moments and trimmed L-moments estimators are particularly resistant to outliers and deliver improvements over standard as well as alternative robust estimators of skewness and kurtosis. The application to 128 macroeconomic and financial time series sourced from a large, monthly frequency, database (i.e. the FRED-MD of McCracken and Ng, 2016) confirms the findings of the simulation study.