Economic Dynamics, Contribution to the Encyclopedia of Nonlinear Science, Alwyn Scott (ed.), Routledge, 2004.

Contribution to the Encyclopedia of Nonlinear Science, Alwyn Scott (ed.), Routledge, 2005, pp.245-248.

Forecasting asylum-related migration flows with machine learning and data at scale

The effects of the so-called "refugee crisis" of 2015-16 continue to dominate the political agenda in Europe. Migration flows were sudden and unexpected, leaving governments unprepared and exposing significant shortcomings in the field of migration forecasting. Migration is a complex system typified by episodic variation, underpinned by causal factors that are interacting, highly context dependent and short-lived. Correspondingly, migration monitoring relies on scattered data, while approaches to forecasting focus on specific migration flows and often have inconsistent results that are difficult to generalise at the regional or global levels. Here we show that adaptive machine learning algorithms that integrate official statistics and non-traditional data sources at scale can effectively forecast asylum-related migration flows. We focus on asylum applications lodged in countries of the European Union (EU) by nationals of all countries of origin worldwide; the same approach can be applied in any context provided adequate migration or asylum data are available. We exploit three tiers of data - geolocated events and internet searches in countries of origin, detections of irregular crossings at the EU border, and asylum recognition rates in countries of destination - to effectively forecast individual asylum-migration flows up to four weeks ahead with high accuracy. Uniquely, our approach a) monitors potential drivers of migration in countries of origin to detect changes early onset; b) models individual country-to-country migration flows separately and on moving time windows; c) estimates the effects of individual drivers, including lagged effects; d) provides forecasts of asylum applications up to four weeks ahead; e) assesses how patterns of drivers shift over time to describe the functioning and change of migration systems

Extracting the Italian output gap: a Bayesian approach

During the last decades particular effort has been directed towards understanding and predicting the relevant state of the business cycle with the objective of decomposing permanent shocks from those having only a transitory impact on real output. This trend--cycle decomposition has a relevant impact on several economic and fiscal variables and constitutes by itself an important indicator for policy purposes. This paper deals with trend--cycle decomposition for the Italian economy having some interesting peculiarities which makes it attractive to analyse from both a statistic and an historical perspective. We propose an univariate model for the quarterly real GDP, subsequently extended to include the price dynamics through a Phillips curve. This study considers a series of the Italian quarterly real GDP recently released by OECD which includes both the 1960s and the recent global financial crisis of 2007--2008. Parameters estimate as well as the signal extraction are performed within the Bayesian paradigm which effectively handles complex models where the parameters enter the log--likelihood function in a strongly nonlinear way. A new Adaptive Independent Metropolis--within--Gibbs sampler is then developed to efficiently simulate the parameters of the unobserved cycle. Our results suggest that inflation influences the Output Gap estimate, making the extracted Italian OG an important indicator of inflation pressures on the real side of the economy, as stated by the Phillips theory. Moreover, our estimate of the sequence of peaks and troughs of the Output Gap is in line with the OECD official dating of the Italian business cycle

A nonlinear structural model for volatility clustering

A simple nonlinear structural model of endogenous belief heterogeneity is proposed. News about fundamentals is an IID random process, but nevertheless volatility clustering occurs as an endogenous phenomenon caused by the interaction between different types of traders, fundamentalists and technical analysts. The belief types are driven by adaptive, evolutionary dynamics according to the success of the prediction strategies as measured by accumulated realized profits, conditioned upon price deviations from the rational expectations fundamental price. Asset prices switch irregularly between two different regimes --periods of small price fluctuations and periods of large price changes triggered by random news and reinforced by technical trading -- thus, creating time varying volatility similar to that observed in real financial data.