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Business and consumer uncertainty in the face of the pandemic: A sector analysis in European countries
This paper examines the evolution of business and consumer uncertainty amid the coronavirus pandemic in 32 European countries and the European Union (EU). Since uncertainty is not directly observable, we approximate it using the geometric discrepancy indicator of Claveria et al. (2019). This approach allows us quantifying the proportion of disagreement in business and consumer expectations of 32 countries. We have used information from all monthly forward-looking questions contained in Joint Harmonised Programme of Business and Consumer Surveys conducted by the European Commission (the industry survey, the service survey, the retail trade survey, the building survey and the consumer survey). First, we have calculated a discrepancy indicator for each of the 17 survey questions analysed, which allows us to approximate the proportion of uncertainty about different aspects of economic activity, both form the demand and the supply sides of the economy. We then use these indicators to calculate disagreement indices at the sector level. We graphic the evolution of the degree of uncertainty in the main economic sectors of the analysed economies up to June 2020. We observe marked differences, both across variables, sectors and countries since the inception of the COVID-19 crisis. Finally, by adding the sectoral indicators, an indicator of business uncertainty is calculated and compared with that of consumers. Again, we find substantial differences in the evolution of uncertainty between managers and consumers. This analysis seeks to offer a global overview of the degree of economic uncertainty in the midst of the coronavirus crisis at the sectoral level.
Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection
We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase.
Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model
This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting.
