Search results

Seismometers continuously record a wide range of ground movements not caused by earthquake activity, but rather generated by human activities such as traffic, industrial machinery functioning and industrial processes. In this Chapter we exploit seismic data to predict variations in Gross Domestic Product (GDP) for a set of States in the USA over the period from 2016 to 2021. We measure the noise generated at specific frequencies that are linked to human activity and use it as an indicator of economic activity. We show a remarkable reduction in seismic noise due to a slowdown in traffic and economic activities during the Corona economic crisis. Our results point at seismic data as a valuable source of information that can be used for monitoring regional and national economies.

This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.

This chapter studies the effects of the COVID-19 pandemic on the economic structure of the US and EU economies by measuring its impact on some reference macro-economic variables. We use a factor model approach on a set of variables available at different frequencies (daily, weekly, monthly, and quarterly) and provide evidence of instability in the primary factors driving the economy. A sequential analysis of the factors allows us to evaluate the model's forecasting performance and extract some instability measures based on the factor model's eigenvalues. Finally, we show how to use COVID-related variables, such as policy, economic, and health indicators, to compute conditional forecasts with factor models, and perform a scenario analysis on the variables of interest to understand economic instability.

This survey presents the recent and rapidly expanding literature, which analyses the economic impacts of the COVID-19 pandemic, by means of Computable General Equilibrium (CGE) modelling. It does so not only by contrasting and assessing the different methodological approaches, and the key findings of the simulation exercises, but also by putting the various contributions in a historical perspective. This is necessary because each CGE-based study should be evaluated while keeping in mind when it was realised, since questions, priorities, expectations have been constantly changing during the spreading of the pandemic.

Traditional epidemic models, like the classical SIR, are fitted to real data using deterministic optimization techniques. As a consequence, their performances cannot be properly assessed and, more importantly, the estimates of the critical epidemic parameters (which are of dramatic importance in monitoring the epidemic evolution) cannot be complemented with the calculation of confidence intervals. The aim of the present work is to remove such limitations and to compare the results obtained using two stochastic versions of deterministic SIR models. We describe the two alternatives and the associated estimation procedures, and we apply the two methodologies to a set of COVID-19 data observed in Italy in the 2020 pandemic wave. Our estimates of the basic reproduction number are comparable with the official sources, but using our methods uncertainty can also be properly assessed.

This chapter aims to provide suggestive evidence on how the Lombardy region dealt with the COVID-19 pandemic in 2020 and discuss future challenges for the Lombardy healthcare system. After an introduction to the wide spread of the virus inside the region, we describe the Lombardy health system so the reader may understand the context in which the virus has taken hold so quickly. The pandemic has heavily stressed the system, mainly because Lombardy experienced an excess of hospital admissions. We have considered the increased mortality rate as a proxy of the proper managing of the COVID-19 pandemic. In addition, we describe the process of treating non-COVID patients, such as those affected by acute myocardial infarction (AMI), stroke and oncological diseases. Despite the pandemic, hospitals have been able to guarantee a high level of performance. A discussion of the future evolution of the healthcare system concludes this chapter.

This chapter discusses methodological challenges that may be faced by researchers interested in financial markets in relation to the COVID-19 pandemic. In particular, we focus on the behaviour of investors and consider three aspects that affect their investment decision process, namely comovement, cross-sectional asset pricing, and out-of-sample forecasting. We argue that, in relation to the pandemic, relevant financial time series such as asset returns exhibit nonlinear dynamics, which should be suitably incorporated within appropriate methodological tools. We discuss possible existing approaches that ensure that those nonlinearities are properly accounted for. Finally, possible areas of future research are touched upon.

This chapter presents an evaluation of the literature on the effect of the pandemic on mental health. It draws mainly on the existing economics literature and presents the state of the art of the COVID-19 effect on mental health. While paying particular attention to how the deterioration of mental health evolved over time and across countries, this chapter also considers variation of mental health across individual demographic characteristics as well as different circumstances through which mental health has been affected. Moreover, it provides a general assessment of the methodological aspects of various studies, by discussing the sample and data used, measures of mental health as well as causality issues. Overall, researchers for various countries around the world adopting different measures of mental health, often non-comparable samples and different methodologies document consistently that the level of mental health has been deteriorated during the pandemic, with the negative effect of the lockdown on mental health being evident in the early stage of the pandemic and on the whole population. Findings point out to a high degree of heterogeneity within demographic groups.