Forecasting US inflation using dynamic general-to-specific model selection

Bagdatoglou, G., Kontonikas, A. and Wohar, M. E. (2015) Forecasting US inflation using dynamic general-to-specific model selection. Bulletin of Economic Research, 68(2), pp. 151-167. (doi: 10.1111/boer.12041)

Abstract

We forecast US inflation using a standard set of macroeconomic predictors and a dynamic model selection and averaging methodology that allows the forecasting model to change over time. Pseudo out-of-sample forecasts are generated from models identified from a multipath general-to-specific algorithm that is applied dynamically using rolling regressions. Our results indicate that the inflation forecasts that we obtain employing a short rolling window substantially outperform those from a well-established univariate benchmark, and contrary to previous evidence, are considerably robust to alternative forecast periods.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kontonikas, Professor Alexandros
• Authors: Bagdatoglou, G., Kontonikas, A., and Wohar, M. E.
• College/School: College of Social Sciences > Adam Smith Business School > Accounting and Finance
• Journal Name: Bulletin of Economic Research
• Publisher: John Wiley & Sons, Ltd.
• ISSN: 0307-3378
• ISSN (Online): 1467-8586

Copyright Holders

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 © 2015 Board of Trustees of the Bulletin of Economic Research and John Wiley &
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• First Published: First published in Bulletin of Economic Research 68(2): 151-167
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher