Impact factors |
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| Institution: | 1. Faculty of Economics and Econometrics, University of Amsterdam, Roetersstraat 11, 1018WB Amsterdam, The Netherlands;2. Department of Economics, University of Insubria, Via Ravasi 2, 21100 Varese, Italy;1. Advanced Ceramics, University of Bremen, Am Biologischen Garten 2/IW3, D-28359 Bremen, Germany;2. Institute of Applied and Physical Chemistry, University of Bremen, Leobener Str., NW2, D-28359 Bremen, Germany;3. Institute of Solid State Physics, University of Bremen, Otto-Hahn-Allee 1, D-28359 Bremen, Germany;4. Central Laboratory for Crystallography and Applied Materials, University of Bremen, Klagenfurter Str., D-28359 Bremen, Germany;1. Key Laboratory of Functional Small Organic Molecule, Ministry of Education and College of Life Science, Jiangxi Normal University, 99 Ziyang Road, Nanchang, Jiangxi 330022, China;2. Key Laboratory of Green Chemistry, Jiangxi Province and College of Chemistry and Chemical Engineering, Jiangxi Normal University, 99 Ziyang Road, Nanchang, Jiangxi 330022, China;1. Yutaka Seino Distinguished Center for Diabetes Research, Kansai Electric Power Medical Research Institute, Kobe, Japan;2. Center for Diabetes, Endocrinology and Metabolism, Kansai Electric Power Hospital, Osaka, Japan;3. Center for Metabolism and Clinical Nutrition, Kansai Electric Power Hospital, Osaka, Japan;4. Division of Molecular and Metabolic Medicine, Kobe University Graduate School of Medicine, Kobe, Japan |
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| Abstract: | In this paper we discuss sensitivity of forecasts with respect to the information set considered in prediction; a sensitivity measure called impact factor, IF, is defined. This notion is specialized to the case of VAR processes integrated of order 0, 1 and 2. For stationary VARs this measure corresponds to the sum of the impulse response coefficients. For integrated VAR systems, the IF has a direct interpretation in terms of long-run forecasts. Various applications of this concept are reviewed; they include questions of policy effectiveness and of forecast uncertainty due to data revisions. A unified approach to inference on the IF is given, showing under what circumstances standard asymptotic inference can be conducted also in systems integrated of order 1 and 2. It is shown how the results reported here can be used to calculate similar sensitivity measures for models with a simultaneity structure. |
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