AN ADAPTIVE MULTIVARIATE APPROACH TO TIME SERIES FORECASTING

In recent years, time series analysts have shifted their interest from univariate to multivariate forecasting approaches. Among them, the Box-Jenkins transfer function process and the state space method have received the most attention. This paper presents a simplified approach that embodies some desirable features of existing methods. It stresses empirical analysis, has a unified modeling structure, is easily applicable, and is adaptive to changes without necessitating prior information on the evolution of a system under study. The core of the method relies on the Carbone-Longini adaptive estimation procedure (AEP). Results of a comparative study based on the well-known Lydia E. Pinkham data and the Box-Jenkins sales/leading indicator data illustrate the merits of multivariate AEP in improving forecasting accuracy while simplifying the analysis process. Subject Area: Forecasting.     

Unpaid labour and territorial extraction in digital value networks

Production in knowledge and data-intensive industries is powered by work that can, in theory, be done from anywhere, via cloudwork platforms. Cloudwork platforms govern data value chains in distinct ways to concentrate power and extract value at the global scale. We argue that unpaid labour is a systemic mechanism of accumulation in these digital value networks. In this paper we demonstrate how it is tied to platform business models and facilitated by elements of platform governance including monopsony power, a high degree of spatial flexibility in sourcing labour, regulatory unaccountability and digital enclosure. We draw on a survey of 699 workers on 14 platforms in 74 countries to show that unpaid labour is an engine of South–North value extraction, and workers in the global South perform more unpaid labour than counterparts in the global North. Our findings have important ramifications our understanding of the changing international division of labour and platform capitalism.     

AN ADAPTIVE APPROACH TO TIME-SERIES FORECASTING

This paper extends the applicability of the Carbone-Longini adaptive estimation procedure (AEP) to time-series forecasting. Comparisons with adaptive filtering, the Box-Jenkins methodology, and multiple regression analysis as it applies to time-series analysis are provided. Specific time-series data examined by Box and Jenkins and Box and Tiao constitute the basis for these comparisons. The analysis of the results indicate the robustness and performance superiority of the simple distributive-lag forecast model coupled with the concept of adaptively “tracking” rather than “fitting” historical data.