<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://purl.org/rss/1.0/" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel rdf:about="http://bura.brunel.ac.uk/handle/2438/13030">
    <title>BURA Collection:</title>
    <link>http://bura.brunel.ac.uk/handle/2438/13030</link>
    <description />
    <items>
      <rdf:Seq>
        <rdf:li rdf:resource="http://bura.brunel.ac.uk/handle/2438/33119" />
        <rdf:li rdf:resource="http://bura.brunel.ac.uk/handle/2438/33118" />
        <rdf:li rdf:resource="http://bura.brunel.ac.uk/handle/2438/33004" />
        <rdf:li rdf:resource="http://bura.brunel.ac.uk/handle/2438/32965" />
      </rdf:Seq>
    </items>
    <dc:date>2026-04-12T15:49:44Z</dc:date>
  </channel>
  <item rdf:about="http://bura.brunel.ac.uk/handle/2438/33119">
    <title>Connectedness Spillover Matrices : a Tool for Diversification</title>
    <link>http://bura.brunel.ac.uk/handle/2438/33119</link>
    <description>Title: Connectedness Spillover Matrices : a Tool for Diversification
Authors: González Cortés, D; Nandy, M; Lodh, S
Abstract: ...
Description: ...</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="http://bura.brunel.ac.uk/handle/2438/33118">
    <title>Predicting Cryptocurrency Prices during Economic Uncertainty with Explainable Artificial Intelligence</title>
    <link>http://bura.brunel.ac.uk/handle/2438/33118</link>
    <description>Title: Predicting Cryptocurrency Prices during Economic Uncertainty with Explainable Artificial Intelligence
Authors: González Cortés, D; Nandy, M; Lodh, S; Senyo, PK; Wu, J; Onieva, E
Abstract: ...
Description: ...</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="http://bura.brunel.ac.uk/handle/2438/33004">
    <title>GDP Revisions are Not Cool: The Impact of Statistical Agencies’ Trade-Oﬀ</title>
    <link>http://bura.brunel.ac.uk/handle/2438/33004</link>
    <description>Title: GDP Revisions are Not Cool: The Impact of Statistical Agencies’ Trade-Oﬀ
Authors: Asimakopoulos, S; Lalik, M; Paredes, J; García, JS
Abstract: Oﬃcial estimates of economic growth are regularly revised and therefore forecasts for GDP growth are done on the basis of ever-changing data. The economic literature has intensively studied the properties of those revisions and their implications for forecasting models. However, it is much less known about the reasons for Statistical Agencies (SAs) to revise their estimates. In order to be timely and reliable, SAs have an explicit interest in not revising their initial GDP estimates too much, while they are much more open to revise GDP components over time. More than a curiosity, we exploit this resulting cross-correlation of GDP components revisions to build a model to better forecast GDP.
Description: JEL Classification: C01, C82, E01.; A working paper version of the article is available at ECB (https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2857~073085df17.en.pdf) and at SSRN (Asimakopoulos, Stylianos and Lalik, Magdalena and Paredes, Joan and García, José Salvado, GDP Revisions are Not Cool: The Impact of Statistical Agencies’ Trade-Oﬀ (October, 2023). ECB Working Paper No. 2023/2857, Available at SSRN: https://ssrn.com/abstract=4618392 or http://dx.doi.org/10.2139/ssrn.4618392). It has not been certified by peer review.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
  <item rdf:about="http://bura.brunel.ac.uk/handle/2438/32965">
    <title>Network Effects in Corporate Emissions: Evidence from a Data-Dependent Spatial Panel Model</title>
    <link>http://bura.brunel.ac.uk/handle/2438/32965</link>
    <description>Title: Network Effects in Corporate Emissions: Evidence from a Data-Dependent Spatial Panel Model
Authors: Asimakopoulos, S; Kapetanios, G; Sarafidis, V; Ventouri, A
Abstract: ...
Description: A preprint version of the article is available at arXiv:2602.21434v1 [econ.GN] (https://arxiv.org/abs/2602.21434). It has not been certified by peer review.</description>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </item>
</rdf:RDF>

