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A dynamic log-linear regression model to forecast numbers of future filings at the European Patent Office
Affiliation:1. European Patent Office, Munich, Germany;2. American University, Washington, DC, USA;1. Institute of Higher Education and Research in Healthcare, University of Lausanne, Medical and Surgical Department of Pediatrics, Lausanne University Hospital, Biopôle II, route de la Corniche 10, 1011 Lausanne, Switzerland;2. School of Nursing and Midwifery, Faculty of Health and Human Sciences, Plymouth University, 3 Portland Villas, Room 101, Drake Circus, Plymouth PL4 8AA, United Kingdom;3. Pediatric Intensive Care Unit, Medical and Surgical Department of Pediatrics, Lausanne University of Lausanne, rue du Bugnon 46, 1011 Lausanne, Switzerland;4. Pediatric Intensive Care Unit, University Lille, CHU Lille, EA 2694, Santé publique: épidémiologie et qualité des soins, F-59000 Lille, France;1. School of Mathematics and Statistics, Wuhan University, Wuhan 430072, PR China;2. Computational Science Hubei Key Laboratory, Wuhan University, Wuhan 430072, PR China;1. School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA;2. School of Materials Science and Engineering, University of New South Wales, NSW 2052, Australia;3. Mechanical, Materials & Aerospace Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA;1. Instituto de Medicina Integral Prof. Fernando Figueira (IMIP), Recife, PE, Brazil;2. Instituto de Medicina Integral Prof. Fernando Figueira (IMIP), Grupo de Estudos em Gestão e Avaliação em Saúde (GEAS), Recife, PE, Brazil;3. Instituto de Medicina Integral Prof. Fernando Figueira (IMIP), Programa de Residência Médica, Recife, PE, Brazil
Abstract:An econometric model is applied to forecast future levels of patent filings at the European Patent Office out to 2019, using historical data from 1990 to 2013 with 28 source country terms. Descriptors include Research and Development expenditures and Gross domestic product, where the latter is split into trend and business cycles components. The model is applied to logarithmically standardised data.The effects on the forecasts of additional future positive and negative stimuli to the GDP components are considered. Reasonable forecasting accuracy is found. Using a series of shorter historical data windows may give improved accuracy for short term forecasts.
Keywords:Business cycles  Lognormal  Gross domestic product (GDP)  Patent filings forecasts  Research and development expenditure (R&D)  Linear model
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