Paolo Paruolo

This chapter presents the book’s motivation, plan and message. The key take-away is that greater systematic access to publicly collected administrative data can generate positive externalities to all stakeholders in the public discourse. The book’s main aim is to lower the information asymmetries surrounding this issue.

This chapter provides an overview of structured cross-border interactions in the area of data-driven impact evaluations. Interest in these interactions stems from results in the literature that suggest that human and human-data interactions are driving factors for the development of data-driven policy learning. The chapter discusses policy instruments that could be used to support such interactions, may improve coordination and increase public value.

This chapter discusses some of the current challenges and opportunities in data-driven learning, including the role played by Artificial Intelligence, data protection techniques and human capital. It argues that computational systems can accelerate and support (but not replace) humans in data-driven learning, that advances in data protection can improve privacy and access to microdata for research, and that human capital is a key factor for data-driven learning, both in the production of causal evidence and in its communication and use in society. Finally, the chapter discusses the association between data-driven learning and trust in the public sector in democracies, which further reinforces interest in policy measures that support data-driven learning.

This paper discusses asymptotic and bootstrap confidence intervals for multivariate permanent‐transitory decompositions of cointegrated vector autoregressive I(1) systems, with a focus on price discovery. Alternative estimators of the permanent components are compared in terms of efficiency also under separable linear restrictions on the parameters, and it is found that the one based on model residuals is most efficient. Bootstrap implementations are discussed and compared. Asymptotic results are also derived for information and component shares widely used in price discovery to measure the relative importance of different markets. A Monte Carlo study and an application illustrate the findings.

This paper discusses semiparametric inference on hypotheses on the cointegration and the attractor spaces for linear processes with moderately large cross‐sectional dimension. The approach is based on sample canonical correlations and functional approximation of Brownian motions, and it can be applied both to the whole system and or to any set of linear combinations of it. The hypotheses of interest are cast in terms of the number of stochastic trends in specified subsystems, and inference is based either on selection criteria or on sequences of tests. This paper derives the limit distribution of these tests in the special one‐dimensional case, and discusses asymptotic properties of the derived inference criteria for hypotheses on the attractor space for sequentially diverging sample size and number of basis elements in the functional approximation. Finite sample properties are analysed via a Monte Carlo study and an empirical illustration on exchange rates is provided.

This Special Issue collects contributions related to the advances in the theory and practice of Econometrics induced by the research of Katarina Juselius and Søren Johansen, whom this Special Issue aims to celebrate