<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>16465</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>26</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>report</type>
    <publisherName>SFB 649</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-06-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Academic Ranking Scales in Economics: Prediction and Imputation</title>
    <abstract language="eng">Publications are a vital element of any scientist’s career. It is not only the number of media outlets but aslo the quality of published research that enters decisions on jobs, salary, tenure, etc. Academic ranking scales in economics and other disciplines are, therefore, widely used in classification, judgment and scientific depth of individual research. These ranking systems are competing, allow for different disciplinary gravity and sometimes give&#13;
orthogonal results. Here a statistical analysis of the interconnection between Handelsblatt (HB), Research Papers in Economics (RePEc, here RP) and Google Scholar (GS) systems&#13;
is presented. Quantile regression allows us to successfully predict missing ranking data and to obtain a so-called HB Common Score and to carry out a cross-rankings analysis.&#13;
Based on the merged ranking data from different data providers, we discuss the ranking systems dependence, analyze the age effect and study the relationship between the research expertise areas and the ranking performance.</abstract>
    <identifier type="url">https://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2016-021.pdf</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="UBICOseries">SFB 649 Discussion Paper ; 2016,021</enrichment>
    <author>
      <firstName>Alona</firstName>
      <lastName>Zharova</lastName>
    </author>
    <submitter>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </submitter>
    <author>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </author>
    <author>
      <firstName>Wolfgang Karl</firstName>
      <lastName>Härdle</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Scientometrics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ranking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Quantile Regression</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Handelsblatt</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>RePEc</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Google Scholar</value>
    </subject>
    <collection role="institutes" number="5309H01">FG Wirtschaftsstatistik und Ökonometrie</collection>
  </doc>
  <doc>
    <id>16472</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Croatian Statistical Association</publisherName>
    <publisherPlace>Zagreb</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2016-06-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Order Flow Forecasting with Multiplicative Error Models</title>
    <abstract language="eng">A flexible statistical approach for the analysis of time-varying dynamics of transaction data on financial markets is here applied to intra-day trading strategies. A local adaptive technique is used to successfully predict financial time series, i.e., the buyer and the seller-initiated trading volumes and the order flow dynamics. Analysing order flow series and its information content of mini Nikkei 225 index futures traded at the Osaka Securities Exchange in 2012 and 2013, a data-driven optimal length of local windows up to approximately 1-2 hours is reasonable to capture parameter variations and is suitable for short-term prediction. Our proposed trading strategies achieve statistical arbitrage opportunities and are therefore beneficial for quantitative finance practice.</abstract>
    <parentTitle language="eng">Book of abstracts of the ISCCRO - international statistical conference in Croatia</parentTitle>
    <identifier type="url">http://www.hsd-stat.hr/en/isccro_en/publications/</identifier>
    <note>ISSN 1849-9864</note>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <author>
      <firstName>Wolfgang Karl</firstName>
      <lastName>Härdle</lastName>
    </author>
    <submitter>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </submitter>
    <author>
      <firstName>Andrija</firstName>
      <lastName>Mihoci</lastName>
    </author>
    <author>
      <firstName>Christopher</firstName>
      <lastName>Hian-Ann Ting</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Forecasting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Multiplicative Error Models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Order Flow</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Trading Volume</value>
    </subject>
    <collection role="institutes" number="5309H01">FG Wirtschaftsstatistik und Ökonometrie</collection>
  </doc>
</export-example>
