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 compare 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 the lowest root mean squared error. 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.

Bastianin, A. (2019). Robust Measures of Skewness and Kurtosis for Macroeconomic and Financial Time Series [Working paper].

Robust Measures of Skewness and Kurtosis for Macroeconomic and Financial Time Series

Bastianin, A
2019

Abstract

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 compare 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 the lowest root mean squared error. 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.
Working paper
University of Milan Bicocca Department of Economics, Management and Statistics Working Paper No. 408
FRED-MD; kurtosis; L-moments; outlier; robust statistics; skewness
English
2019
https://ssrn.com/abstract=3385397
Bastianin, A. (2019). Robust Measures of Skewness and Kurtosis for Macroeconomic and Financial Time Series [Working paper].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/229484
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