Chapter The effectiveness of marketing tools in a consumer goods market in Italy during the Great Recession (2010-2015)

In the case of markets characterized by a stationary primary demand, the relevant dimension for measuring a company’s success is represented by market shares. The paper aims to build and comment on a model that gauges the competitive effects of marketing maneuvers on market shares, with reference to tea-based beverages in Italy in the period November 2010 – October 2015. This analysis will be instrumental in establishing the effectiveness of marketing policies based on promotions or advertising. We estimate such a model on weekly data provided by IRI Infoscan and Nielsen, involving the top five brands in the Italian market. After a descriptive analysis and a stationarity test, we estimate a Multinomial Logit model, making use of the Seemingly Unrelated Regressions method. The results allow us to identify the effectiveness of each brand’s marketing policies. Moreover, they enable us to derive the matrices of direct and cross elasticities of brands’ market shares with respect to the main marketing tools (price, promotions, distribution, advertising investments) and to compare basic and average market shares. Based on these results, it is therefore possible to identify the market’s competitive structure, revealing the most incisive factors to be price and weighted distribution, while advertising investments are significant in only a few cases and elasticities are remarkably low. The competitive structure appears to be of a horizontal type (i.e., cross elasticities do not vary greatly)

Chapter Measuring the effectiveness of COVID-19 containment policies in Italian regions: are we doing enough?

A successful fight against COVID-19 greatly depends on citizens’ adherence to the restrictive measures, which may not suffice alone. Making use of a containment index, data on sanctions, and Google’s movement trends across Italian regions, complemented by other sources, we investigate the extent to which compliance with the mobility limitations has affected the number of deaths over time in the period from the 24th of February 2020 to the 9th of November 2020, by using panel data for Italian regions, analysed through a negative binomial regression method. We also differentiated the study period, estimating two distinct models on two subsamples: until the 13th of September and since the 14th of September. In so doing, we show how the pandemic dynamics have changed between the first and the second wave of the emergency. Our results highlight that the importance of the restrictive measures and of citizens’ accord on their abidance has greatly increased since the end of the summer, also because the stringency level of the adopted measures has critically declined. Informing citizens about the effects and purposes of the restrictive measures is of paramount importance, especially in the current phase of the pandemic

Understanding pro-environmental travel behaviours in Western Europe

This study aims at understanding, from a gender perspective, the reasons behind citizens’ choice of using public transport, and whether this choice is driven by pro-environmental behaviour. Using Eurobarometer data (2013), we perform ordered logistic regressions comparatively for Germany, Italy and the Netherlands. Financial, political and environmental factors are shown to have significant roles in shaping travel behaviours, with interesting gender and cross-country differences

Statistical hypothesis testing within the Generalized Error Distribution: Comparing the behavior of some nonparametric techniques

This paper’s goal is to deal with the issue of hypothesis testing when the errors are assumed to be distributed according to a Generalized Error Distribution. Focus is given to the central tendency parameters, validating the suitability of nonparametric methods in this respect. The present work describes a simulation study aimed at assessing the validity of the Van der Waerden and Wilcoxon tests in the case of data coming from a G.E.D.; in order to compare the statistical power of such tests, we proceed to calculate the usual empirical significance level. The use of test statistics obtained by means of a Van der Waerden test generalized by considering the G.E.D.’s shape parameter provides better results, in terms of statistical power, compared to the Wilcoxon and the classic Van der Waerden test

A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments

In this paper, by considering a model-based approach for conditional moment estimation, a nonparametric test was performed to study the long-memory property of higher moments. We considered the daily returns of the stocks included in the S&P500 index in the last ten years (for the period running from the 1st of January 2011 to the 1st of January 2021). We found that mean and skewness were characterized by short memory, while variance and shape had long memory. These results have deep implications in terms of asset allocation, option pricing and market efficiency evaluation

Statistical hypothesis testing within the Generalized Error Distribution: Comparing the behavior of some nonparametric techniques

This paper’s goal is to deal with the issue of hypothesis testing when the errors are assumed to be distributed according to a Generalized Error Distribution. Focus is given to the central tendency parameters, validating the suitability of nonparametric methods in this respect. The present work describes a simulation study aimed at assessing the validity of the Van der Waerden and Wilcoxon tests in the case of data coming from a G.E.D.; in order to compare the statistical power of such tests, we proceed to calculate the usual empirical significance level. The use of test statistics obtained by means of a Van der Waerden test generalized by considering the G.E.D.’s shape parameter provides better results, in terms of statistical power, compared to the Wilcoxon and the classic Van der Waerden test

A Nonparametric Approach for Testing Long Memory in Stock Returns’ Higher Moments

In this paper, by considering a model-based approach for conditional moment estimation, a nonparametric test was performed to study the long-memory property of higher moments. We considered the daily returns of the stocks included in the S&P500 index in the last ten years (for the period running from the 1st of January 2011 to the 1st of January 2021). We found that mean and skewness were characterized by short memory, while variance and shape had long memory. These results have deep implications in terms of asset allocation, option pricing and market efficiency evaluation