COVID-19 impacts of inbound tourism on Australian economy.

The pandemic COVID-19 has severely impacted upon the world economy, devastating the tourism industry globally. This paper estimates the short-run economic impacts of the inbound tourism industry on the Australian economy during the pandemic. The analysis covers effects both at the macroeconomic as well as at the industry and occupation level, from direct contribution (using tourism satellite accounts) to economy-wide effects (using the computable general equilibrium modelling technique). Findings show that the pandemic affects a range of industries and occupations that are beyond the tourism sector. The paper calls for strong support from the government on tourism as the recovery of tourism can deliver spillover benefits for other sectors and across the whole spectrum of occupations in the labour market.

Data on forecasting energy prices using machine learning.

This article contains the data related to the research article "Long-term forecast of energy commodities price using machine learning" (Herrera et al., 2019). The datasets contain monthly prices of six main energy commodities covering a large period of nearly four decades. Four methods are applied, i.e. a hybridization of traditional econometric models, artificial neural networks, random forests, and the no-change method. Data is divided into 80-20% ratio for training and test respectively and RMSE, MAPE, and M-DM test used for performance evaluation. Other methods can be applied to the dataset and used as a benchmark.