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Public investment in Big Science generates social benefits that can ultimately support economic growth. This paper implements a model for the social Cost – Benefit Analysis (CBA) of Big Science and relies on Monte Carlo methods to quantify the uncertainty of long-term projections. We evaluate social costs and benefits of the High Luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN up to 2038. Monte Carlo simulations show that there is a 94% chance to observe a positive net present value for society. The attractiveness of CERN for Early Stage Researchers and technological spillovers for collaborating firms are key for a positive CBA result. Cultural effects, especially those related to onsite visitors, also contribute to generating societal benefits. [ABSTRACT FROM AUTHOR]

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 12 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. [ABSTRACT FROM AUTHOR]