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

Andrea Bastianin

No 408, Working Papers from University of Milano-Bicocca, Department of Economics

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.

Keywords: FRED-MD; kurtosis; L-moments; outlier; robust statistics; skewness. (search for similar items in EconPapers)
JEL-codes: C22 C46 C52 C55 C58 (search for similar items in EconPapers)
Pages: 53
Date: 2019-05-06, Revised 2019-05-06
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Robust measures of skewness and kurtosis for macroeconomic and financial time series (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:mib:wpaper:408

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