Rights statement: This is the peer reviewed version of the following article: Konstantakis, KN, Paraskeuopoulou, D, Michaelides, PG, Tsionas, EG. Bank deposits and Google searches in a crisis economy: Bayesian non-linear evidence for Greece (2009–2015). Int J Fin Econ. 2021; 26: 5408– 5424. doi.org/10.1002/ijfe.2072 which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1002/ijfe.2072 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
<mark>Journal publication date</mark> | 31/10/2021 |
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<mark>Journal</mark> | International Journal of Finance and Economics |
Issue number | 4 |
Volume | 26 |
Number of pages | 17 |
Pages (from-to) | 5408-5424 |
Publication Status | Published |
Early online date | 19/08/20 |
<mark>Original language</mark> | English |
Due to a variety of reasons, the Greek economy faced a severe crisis being a member of EMU. Nonetheless, the country's banking system experienced a dramatic outflow of deposits, in the period 2009–2015. The present paper attempts to shed light on the possibility of forecasting bank deposits, based on the keyword “Grexit” of Google searches. In this context, apart from standard forecasting models like AR (p) and ARDL (p, q) we estimate a novel Neural Network ARDL (p, q, G) model and its respective Bayesian modification. We show that extending standard autoregressive models with the information provided by Internet Searches leads to significant improvement in the forecasting accuracy of the Bank Deposits, compared with other standard models. Furthermore, the forecasting performance of the models, which are extended with Google searches, is shown to be better than models containing only the well-known indicators. Our findings are robust and econometrically sound.