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Public debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.

Using quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.

The results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.

Compared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

This article explores the migration intentions (MIs) embedded in population movements from rural to urban areas in Bangladesh. In this country, urban-centric development policies have made cities epicentres of commerce and industrialisation, offering significant employment and livelihood opportunities. This rapid transformation has generated several socio-psychological factors that are influencing the willingness of rural populations to migrate to cities for better jobs, lifestyles and services.

The present study adopted the theory of planned behaviour (TPB) as a conceptual model to assess the behavioural and psychological factors underlying MIs.

The results of the structural equation modelling (SEM) indicate that MIs are mainly influenced by subjective norms (SN) and, to a lesser extent, attitudes towards migration (ATM) and perceived behavioural control (PBC).

The analysis drew on an original dataset built through interviews with migrants from rural areas employed in the ready-made garment (RMG) industry in four selective areas of the Metropolitan City of Chittagong.

The purpose of this paper is to investigate the foreign direct investments (FDI)-growth nexus and the impact of natural resource abundance in the host country on the FDI-growth nexus.

For a large data set of 104 countries for the period 1996-2015, Arellano and Bond’s GMM estimation method is applied to investigate the impact of FDI inflow on economic growth and the role of the natural resource sector on the FDI-growth relationship.

The paper found a positive and significant effect of FDI inflows on economic growth of the host country. However, the impact of FDI inflows on economic growth changes with the changes in the size of the natural resource sector. The estimated positive impact of FDI inflows on economic growth declines with the expansion in the size of natural resources. Beyond a certain limit, a further expansion in the size of natural resource sector will lead to a negative effect of FDI on economic growth.

The paper found a positive and significant impact of FDI inflows on economic growth of the host country. However, the impact of FDI inflows on economic growth changes with the changes in the size of the natural resource sector. The estimated positive impact of FDI inflows on economic growth declines with the expansion in the size of the natural resources. Beyond a certain limit, a further expansion in the size of the natural resource sector will lead to a negative effect of FDI on economic growth. The same analysis is repeated for groups of countries divided into different income groups. FDI inflows are found to have significant growth enhancing role in all three groups of countries. However, FDI inflows-induced growth was found to be more pronounced in the middle- and low-income countries compared to high-income countries. Further, FDI-induced economic growth is slowed down in low-income and middle-income countries by the increase in size of the natural resource sector. While in high-income countries, the size of the natural resource sector has no significant role on the FDI-growth nexus.

While countries use their natural resource sector as an instrument to attract FDI into the countries, low- and middle-income countries face the dilemma of experiencing the resource curse in the form of watered down FDI-induced growth. Therefore, low- and middle-income countries need to try at the same time to attract FDI into the non-resources sector to keep the relative size of the natural resource sector low as to avoid hampering the FDI-induced economic growth. High-income countries, on the other hand, do not experience the FDI-induced growth hampering impact of the natural resource sector. Therefore, high-income countries should attract FDI into the countries regardless of the sector attracting the foreign investments.

The paper is part of the author’s PhD research and is an original contribution.