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Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero
Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.
Mr. Alexei P Kireyev and Andrei Leonidov
This paper proposes a network model of multilaterally equilibrium exchange rates. The model introduces a topological component into the exchange rate analysis, consistently taking into account simultaneous higher-order interactions among all currencies. The paper defines the currency demand indicator. On its base, it derives a multilateral exchange rate network, finds its dynamically stationary position, and identifies the multilaterally equilibrium levels of bilateral exchanges rates. Potentially, the model can be developed further to calculate the deviations of the observed bilateral exchange rates from their multilaterally equilibrium levels, which can be interpreted as their over- or undervaluation. For illustration, the model is applied to daily 1995-2016 exchange rates among 130 currencies sourced from the Thomson Reuters Datastream.
Mr. Alexei P Kireyev and Andrei Leonidov

: Implications from Network Analysis . Journal of Policy Modeling , 31 ( 5 ), 601 – 619 . Heiberger , R. H. ( 2014 ). Stock Network Stability in Times of Crisis . Physica A: Statistical Mechanics and its Applications , 393 , 376 – 381 . Joseph , A. , Vodenska , I. , Stanley , E. , Chen , G. ( 2014 ). Netconomics: Novel Forecasting Techniques from the Combination of Big Data , Network Science and Economics . arXiv preprint arXiv:1403.0848 . International Monetary Fund, 2015 ,” Guidance Note for Surveillance under Article IV Consultation

Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero

learn about what has already been achieved in close collaboration with international organizations leading the methodological development . Statistical agencies could contribute, learn, and profit from projects and work already ongoing in the big data community. It will be essential to not only leverage but also contribute to existing big data networks, such as the United Nations Global Working Group on Big Data. Robust governance frameworks around big data that promote international and national cooperation will need to avoid overlaps and ensure effective

International Monetary Fund. Western Hemisphere Dept.

reducing backlog of criminal court cases and collecting detailed statistics. With respect to the recommendations of the Detailed Assessment Report on AML/CFT, the authorities updated the national risk assessment in 2019 in line with international best practice, and helped by big data, network and AI analysis have improved the identification, assessment and understanding of money laundering and terrorist financing (ML/TF) risks. The authorities also report that they have improved coordination between supervisory entities (though not beyond these); and they are working on

International Monetary Fund. Western Hemisphere Dept.
This 2020 Article IV Consultation with Colombia highlights that with the disruptions associated with the coronavirus disease 2019 pandemic and with lower oil prices, real gross domestic product (GDP) is projected to contract by 2.4 percent in 2020. In the near term, disruptions associated, directly and indirectly, with the pandemic are expected to generate a recession of -2.4 percent in 2020. Weaker domestic demand from the shutdown efforts is expected to partially offset lower external demand and commodity prices, such that the current account deficit is projected to rise to 4.7 percent of GDP. In the wake of exceptional shocks and risks, recent monetary easing is welcomed by the IMF and accommodation should continue to support the economy if underlying inflation and inflation expectations remain moderate. Continued liquidity support should be provided as required, and available capital buffers in the banking system should be used as needed. All available space under the fiscal rule can be used to meet unforeseen health expenditures and for countercyclical spending to further support the economy through recession.